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WO2025221290A1 - Circuit de photodiode à discrimination multiniveau - Google Patents

Circuit de photodiode à discrimination multiniveau

Info

Publication number
WO2025221290A1
WO2025221290A1 PCT/US2024/049471 US2024049471W WO2025221290A1 WO 2025221290 A1 WO2025221290 A1 WO 2025221290A1 US 2024049471 W US2024049471 W US 2024049471W WO 2025221290 A1 WO2025221290 A1 WO 2025221290A1
Authority
WO
WIPO (PCT)
Prior art keywords
photodetector
photodetectors
light
vehicle
analog output
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
PCT/US2024/049471
Other languages
English (en)
Inventor
Yimin Li
Chen Gu
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Seyond Inc
Original Assignee
Seyond Inc
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Seyond Inc filed Critical Seyond Inc
Publication of WO2025221290A1 publication Critical patent/WO2025221290A1/fr
Pending legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S7/00—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00
    • G01S7/48—Details of systems according to groups G01S13/00, G01S15/00, G01S17/00 of systems according to group G01S17/00
    • G01S7/483—Details of pulse systems
    • G01S7/486—Receivers
    • G01S7/4861—Circuits for detection, sampling, integration or read-out
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/02—Systems using the reflection of electromagnetic waves other than radio waves
    • G01S17/06—Systems determining position data of a target
    • G01S17/08—Systems determining position data of a target for measuring distance only
    • G01S17/10—Systems determining position data of a target for measuring distance only using transmission of interrupted, pulse-modulated waves
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S17/00—Systems using the reflection or reradiation of electromagnetic waves other than radio waves, e.g. lidar systems
    • G01S17/88—Lidar systems specially adapted for specific applications
    • G01S17/89—Lidar systems specially adapted for specific applications for mapping or imaging

Definitions

  • This disclosure relates generally to multilevel discrimination photodiode circuit in LiDAR systems.
  • LiDAR Light detection and ranging
  • a LiDAR system may be a scanning or non-scanning system.
  • Some typical scanning LiDAR systems include a light source, a light transmitter, a light steering system, and a light detector.
  • the light source generates a light beam that is directed by the light steering system in particular directions when being transmitted from the LiDAR system.
  • a transmitted light beam is scattered or reflected by an object, a portion of the scattered or reflected light returns to the LiDAR system to form a return light pulse.
  • the light detector detects the return light pulse.
  • the LiDAR system can determine the distance to the object based on the speed of light. This technique of determining the distance is referred to as the time-of-flight (ToF) technique.
  • the light steering system can direct light beams along different paths to allow the LiDAR system to scan the surrounding environment and produce images or point clouds.
  • a typical non-scanning LiDAR system illuminates an entire field-of-view (FOV) rather than scanning through the FOV.
  • An example of the non-scanning LiDAR system is a flash LiDAR, which can also use the ToF technique to measure the distance to an object.
  • LiDAR systems can also use techniques other than time-of-flight and scanning to measure the surrounding environment.
  • TCSPC Time Correlated Single Photon Counting
  • NIR near infrared
  • the signal processing circuits that are used with typical TCSPC implementations may consume a relatively high amount of resources (e.g., processing resources, memory resources, silicon space resources, etc.).
  • a system may include a photodetector set that includes multiple photodetectors.
  • the photodetector set may be configured to generate an analog output that is based on a number of photodetectors of the photodetector set that are activated during a detection period corresponding to a transmitted light signal.
  • the system may also include an analog-to-digital converter (ADC) configured to generate a digital output based on the analog output.
  • ADC analog-to-digital converter
  • the system may include a signal processing circuit configured to generate a histogram of one or more return pulses based on the digital output.
  • FIG. 1 is a signal timing diagram illustrating an example TCSPC (time correlated single photon counting) photodetector operation.
  • FIG. 2 illustrates one or more example LiDAR systems disposed or included in a motor vehicle.
  • FIG. 3 is a block diagram illustrating interactions between an example LiDAR system and multiple other systems including a vehicle perception and planning system.
  • FIG. 4 is a block diagram illustrating an example LiDAR system.
  • FIG. 5 is a block diagram illustrating an example fiber-based laser source.
  • FIGs. 6A-6C illustrate an example LiDAR system using pulse signals to measure distances to objects disposed in a field-of-view (FOV).
  • FOV field-of-view
  • FIG. 7 is a block diagram illustrating an example apparatus used to implement systems, apparatus, and methods in various embodiments.
  • FIG. 8 is a block diagram of an example TCSPC (Time Correlated Single Photon Counting) system that may be used as part of a TCSPC implementation, according to one or more embodiments of the present disclosure.
  • TCSPC Time Correlated Single Photon Counting
  • FIG. 9 is a circuit diagram illustrating an example photodetector system according to one or more embodiments of the present disclosure.
  • FIG. 10 is a flow diagram of a method for performing TCSPC, according to one or more embodiments of the present disclosure.
  • Coupled to is intended to include both direct coupling (in which two elements that are coupled to each other contact each other) and indirect coupling (in which at least one additional element is located between the two elements). Therefore, the terms “coupled to” and “coupled with” are used synonymously. Within the context of a networked environment where two or more components or devices are able to exchange data, the terms “coupled to” and “coupled with” are also used to mean “communicatively coupled with”, possibly via one or more intermediary devices.
  • the components or devices can be optical, mechanical, and/or electrical devices.
  • first sensor could be termed a second sensor and, similarly, a second sensor could be termed a first sensor, without departing from the scope of the various described examples.
  • the first sensor and the second sensor can both be sensors and, in some cases, can be separate and different sensors.
  • inventive subject matter is considered to include all possible combinations of the disclosed elements. As such, if one embodiment comprises elements A, B, and C, and another embodiment comprises elements B and D, then the inventive subject matter is also considered to include other remaining combinations of A, B, C, or D, even if not explicitly discussed herein.
  • transitional term “comprising” means to have as parts or members, or to be those parts or members. As used herein, the transitional term “comprising” is inclusive or open-ended and does not exclude additional, unrecited elements or method steps.
  • any language directed to a computer should be read to include any suitable combination of computing devices or network platforms, including servers, interfaces, systems, databases, agents, peers, engines, controllers, modules, or other types of computing devices operating individually or collectively.
  • the computing devices comprise a processor configured to execute software instructions stored on a tangible, non- transitory computer readable storage medium (e.g., hard drive, FPGA, PLA, solid state drive, RAM, flash, ROM, or any other volatile or non-volatile storage devices).
  • the software instructions configure or program the computing device to provide the roles, responsibilities, or other functionality as discussed below with respect to the disclosed apparatus
  • the disclosed technologies can be embodied as a computer program product that includes a non- transitory computer readable medium storing the software instructions that causes a processor to execute the disclosed steps associated with implementations of computer-based algorithms, processes, methods, or other instructions.
  • the various servers, systems, databases, or interfaces exchange data using standardized protocols or algorithms, possibly based on HTTP, HTTPS, AES, public-private key exchanges, web service APIs, known financial transaction protocols, or other electronic information exchanging methods.
  • Data exchanges among devices can be conducted over a packet-switched network, the Internet, LAN, WAN, VPN, or other type of packet switched network; a circuit switched network; cell switched network; or other type of network.
  • TCSPC time correlated single photon counting
  • a light source e.g., a laser such as a LiDAR laser
  • the TCSPC system may also be configured to detect return pulses that respectively correspond to the emitted light pulses. For example, an emitted light pulse may reflect off an object as a return pulse that may be detected by the TCSPC system (e.g., by a photodetector of the TCSPC system). Further, the TCSPC may be configured to determine the amount of time it took for the pulse to travel from the light source, reflect off the object, and then travel back to the detector.
  • the TCSPC system may be configured to build a histogram that indicates the number of detections of return pulses in different time windows within a detection period that corresponds to the amount of time between emitted light pulses.
  • the histogram may also accordingly indicate an overall optical waveform corresponding to the return pulses and the object from which the return pulses may reflect.
  • FIG. 1 is a signal timing diagram 100 illustrating an example TCSPC photodetector operation.
  • the diagram 100 includes a depiction of an optical waveform 102 that represents an overall optical waveform that may be formed based on return pulses from emitted light pulses.
  • the diagram 100 also includes detections 104 that illustrate different detections of single return pulses corresponding to the waveform 102 at different points in time during a detection period “td”.
  • Each of the return pulses illustrated in the detections 104 may correspond to different emitted light pulses Further the detection period “td” may correspond to the amount of time that may pass for a detection corresponding to a respective emitted light pulse to occur.
  • the detections 104 only are meant to illustrate that different detections occur at different points in time and does not illustrate all of the different detections that may occur.
  • the diagram 100 includes a histogram 106 that corresponds to the number of detections that occur within particular time windows “tw’s” within the depiction period “td”. As illustrated in the diagram 100, the histogram 106 may represent the shape of the waveform 102.
  • a typical TCSPC design may have a single photodetector device, such as a SPAD (single photon avalanche diode), receiving the return pulses and generating an output to a pre-amplifier and then a comparator.
  • the amplified signal is then sent to a TDC (time to digital converter), and a digital processor/ counter then counts and accumulates the number of pulses over time to form the histogram of the number of pulses in a time position within the detection period.
  • each pulse may be assumed to be a single photon detected and non- overlapping in time with any other photons detected.
  • the output histogram may correlate to the overall shape of the input optical waveform.
  • the receiving path usually works in a format of a 2D or ID array.
  • the photodetector and processing circuit are built into an ASIC, via either a standard CMOS process or a flip chip way.
  • signal path is equivalent to a single photodetector device and a 1 bit ADC (analog-to- digital converter).
  • the silicon area of the single photodetector device is usually much smaller than the corresponding area of a signal process circuit which requires a much larger area for memory and logic circuits to process and generate the histogram.
  • CMOS process this solution is potentially very costly in silicon area.
  • flip chip implementations this leads to a high cost of an overall solution.
  • a system may be configured to be implemented with respect to TCSPC applications in a manner that reduces the overall resources used as compared to more traditional TCSPC systems.
  • a multiple single photodetector devices also referred to herein as “photodetectors” may be implemented in a set (e.g., used in parallel) to operate as an analog photon counter.
  • the photodetector set may be configured to generate an analog output that is based on a number of photodetectors of the photodetector set that are activated during a detection period corresponding to an emitted light pulse.
  • the individual output signals of activated photodetectors may be accumulated into the analog output
  • the number of photodetectors that are activated within a particular time window of the detection period may correspond to the number of photons that are received within that time window. Therefore, the analog output may indicate the number of photons that are received within particular time windows and may accordingly be used to determine the photon count for each time window.
  • the analog output may be provided to one or more multi-bit number ADCs (analog-to-digital converters) that may generate a digital number that corresponds to the amplitude of the output signal.
  • the digital number may accordingly indicate the number of photons received.
  • ADCs analog-to-digital converters
  • One or more analog circuits can do the counting by levels instead.
  • 3 single photodetector devices may be used to generate 4 possible levels of accumulated output.
  • a 2 -bit ADC may be used to detect the accumulated output. If 7 single photodetector devices are used, then a 3 -bit ADC may be used to detect the accumulated output.
  • This arrangement may shorten detection time, since the number of light pulse emissions may be reduced due to the ability to count multiple return pulses at a time. For example, to achieve approximately the same SNR (signal to noise ratio), this configuration with a 2 -bit ADC may reduce accumulation time by at least a factor of 2. Alternatively, to keep the same accumulation time, the detection sensitivity may be improved by at least a factor of 2 or 6dB. In addition, for storage, the memory size requirements may also be reduced by at least a factor of 2 for utilization of a 2 -bit ADC. Additionally or alternatively, the amount of silicon required to implement an analog counter as described in the present disclosure, may be less than the amount that may be required to implement a signal processing system that digitally counts the number of return photons, such as done in traditional TCSPC systems.
  • a photodetector system may include a voltage source and one or more photodetectors, each having an anode connected to the voltage source via a first switch.
  • One or more resistors may each have a first end connected to a cathode of a corresponding one of one or more photodetectors, and may have all second ends connected together to an analog output.
  • a capacitor may have an anode connected to the analog output, and may have a cathode connected to the ground.
  • a second switch and a load resistor may be connected in series between the analog output and the ground.
  • An analog-to-digital converter may generate a digital output (Output) based on the analog output.
  • Vehicle 200 can be a car, a sport utility vehicle (SUV), a truck, a train, a wagon, a bicycle, a motorcycle, a tricycle, a bus, a mobility scooter, a tram, a ship, a boat, an underwater vehicle, an airplane, a helicopter, an unmanned aviation vehicle (UAV), a spacecraft, etc.
  • Motor vehicle 200 can be a vehicle having any automated level.
  • motor vehicle 200 can be a partially automated vehicle, a highly automated vehicle, a fully automated vehicle, or a driverless vehicle.
  • a partially automated vehicle can perform some driving functions without a human driver’s intervention.
  • a partially automated vehicle can perform blind-spot monitoring, lane keeping and/or lane changing operations, automated emergency braking, smart cruising and/or traffic following, or the like. Certain operations of a partially automated vehicle may be limited to specific applications or driving scenarios (e.g., limited to only freeway driving).
  • a highly automated vehicle can generally perform all operations of a partially automated vehicle but with less limitations.
  • a highly automated vehicle can also detect its own limits in operating the vehicle and ask the driver to take over the control of the vehicle when necessary.
  • a fully automated vehicle can perform all vehicle operations without a driver’s intervention but can also detect its own limits and ask the driver to take over when necessary.
  • a driverless vehicle can operate on its own without any driver intervention.
  • motor vehicle 200 comprises one or more LiDAR systems 210 and 220A-220I.
  • Each of LiDAR systems 210 and 220A-220I can be a scanning-based LiDAR system and/or a non-scanning LiDAR system (e.g., a flash LiDAR).
  • a scanning-based LiDAR system scans one or more light beams in one or more directions (e.g., horizontal and vertical directions) to detect objects in a field-of-view (FOV).
  • a non-scanning based LiDAR system transmits laser light to illuminate an FOV without scanning.
  • a flash LiDAR is a type of non-scanning based LiDAR system.
  • a flash LiDAR can transmit laser light to simultaneously illuminate an FOV using a single light pulse or light shot.
  • a LiDAR system is a frequently-used sensor of a vehicle that is at least partially automated.
  • motor vehicle 200 may include a single LiDAR system 210 (e.g., without LiDAR systems 220A-220I) disposed at the highest position of the vehicle (e.g., at the vehicle roof). Disposing LiDAR system 210 at the vehicle roof facilitates a 360-degree scanning around vehicle 200.
  • motor vehicle 200 can include multiple LiDAR systems, including two or more of systems 210 and/or 220 A- 2201. As shown in FIG. 2, in one embodiment, multiple LiDAR systems 210 and/or 220A-220I are attached to vehicle 200 at different locations of the vehicle.
  • LiDAR system 220A is attached to vehicle 200 at the front right corner; LiDAR system 220B is attached to vehicle 200 at the front center position; LiDAR system 220C is attached to vehicle 200 at the front left comer; LiDAR system 220D is attached to vehicle 200 at the right-side rear view mirror; LiDAR system 220E is attached to vehicle 200 at the left-side rear view mirror; LiDAR system 220F is attached to vehicle 200 at the back center position; LiDAR system 220G is attached to vehicle 200 at the back right corner; LiDAR system 220H is attached to vehicle 200 at the back left corner; and/or LiDAR system 2201 is attached to vehicle 200 at the center towards the backend (e.g., back end of the vehicle roof).
  • the backend e.g., back end of the vehicle roof
  • LiDAR systems can be distributed and attached to a vehicle in any desired manner and FIG. 2 only illustrates one embodiment.
  • LiDAR systems 220D and 220E may be attached to the B-pillars of vehicle 200 instead of the rear-view mirrors.
  • LiDAR system 220B may be attached to the windshield of vehicle 200 instead of the front bumper.
  • LiDAR systems 210 and 220A-220I are independent LiDAR systems having their own respective laser sources, control electronics, transmitters, receivers, and/or steering mechanisms. In other embodiments, some of LiDAR systems 210 and 220A- 2201 can share one or more components, thereby forming a distributed sensor system.
  • optical fibers are used to deliver laser light from a centralized laser source to all LiDAR systems.
  • system 210 (or another system that is centrally positioned or positioned anywhere inside the vehicle 200) includes a light source, a transmitter, and a light detector, but has no steering mechanisms.
  • System 210 may distribute transmission light to each of systems 220A-220I. The transmission light may be distributed via optical fibers.
  • Optical connectors can be used to couple the optical fibers to each of system 210 and 220A-220I.
  • one or more of systems 220A-220I include steering mechanisms but no light sources, transmitters, or light detectors.
  • a steering mechanism may include one or more moveable mirrors such as one or more polygon mirrors, one or more single plane mirrors, one or more multi-plane mirrors, or the like. Embodiments of the light source, transmitter, steering mechanism, and light detector are described in more detail below.
  • one or more of systems 220A-220I scan light into one or more respective FOVs and receive corresponding return light. The return light is formed by scattering or reflecting the transmission light by one or more objects in the FOVs.
  • Systems 220A-220I may also include collection lens and/or other optics to focus and/or direct the return light into optical fibers which deliver the received return light to system 210.
  • System 210 includes one or more light detectors for detecting the received return light.
  • system 210 is disposed inside a vehicle such that it is in a temperature-controlled environment, while one or more systems 220A-220I may be at least partially exposed to the external environment.
  • FIG. 3 is a block diagram 300 illustrating interactions between vehicle onboard LiDAR system(s) 310 and multiple other systems including a vehicle perception and planning system 320.
  • LiDAR system(s) 310 can be mounted on or integrated to a vehicle.
  • LiDAR system(s) 310 include sensor(s) that scan laser light to the surrounding environment to measure the distance, angle, and/or velocity of objects. Based on the scattered light that returned to LiDAR system(s) 310, it can generate sensor data (e.g., image data or 3D point cloud data) representing the perceived external environment.
  • sensor data e.g., image data or 3D point cloud data
  • LiDAR system(s) 310 can include one or more of short-range LiDAR sensors, medium- range LiDAR sensors, and long-range LiDAR sensors.
  • a short-range LiDAR sensor measures objects located up to about 20-50 meters from the LiDAR sensor.
  • Short-range LiDAR sensors can be used for, e.g., monitoring nearby moving objects (e.g., pedestrians crossing street in a school zone), parking assistance applications, or the like.
  • a medium-range LiDAR sensor measures objects located up to about 70-200 meters from the LiDAR sensor.
  • Medium-range LiDAR sensors can be used for, e.g., monitoring road intersections, assistance for merging onto or leaving a freeway, or the like.
  • a long-range LiDAR sensor measures objects located up to about 200 meters and beyond.
  • Long-range LiDAR sensors are typically used when a vehicle is travelling at a high speed (e.g., on a freeway), such that the vehicle’s control systems may only have a few seconds (e.g., 6-8 seconds) to respond to any situations detected by the LiDAR sensor.
  • the LiDAR sensor data can be provided to vehicle perception and planning system 320 via a communication path 313 for further processing and controlling the vehicle operations.
  • Communication path 313 can be any wired or wireless communication links that can transfer data.
  • other vehicle onboard sensor(s) 330 are configured to provide additional sensor data separately or together with LiDAR system(s) 310.
  • Other vehicle onboard sensors 330 may include, for example, one or more camera(s) 332, one or more radar(s) 334, one or more ultrasonic sensor(s) 336, and/or other sensor(s) 338.
  • Camera(s) 332 can take images and/or videos of the external environment of a vehicle.
  • Camera(s) 332 can take, for example, high-definition (HD) videos having millions of pixels in each frame.
  • a camera includes image sensors that facilitate producing monochrome or color images and videos. Color information may be important in interpreting data for some situations (e.g., interpreting images of traffic lights).
  • Camera(s) 332 can include one or more of narrow- focus cameras, wider-focus cameras, side-facing cameras, infrared cameras, fisheye cameras, or the like.
  • the image and/or video data generated by camera(s) 332 can also be provided to vehicle perception and planning system 320 via communication path 333 for further processing and controlling the vehicle operations.
  • Communication path 333 can be any wired or wireless communication links that can transfer data.
  • Camera(s) 332 can be mounted on, or integrated to, a vehicle at any location (e.g., rear-view mirrors, pillars, front grille, and/or back bumpers, etc.).
  • Other vehicle onboard sensos(s) 330 can also include radar sensor(s) 334.
  • Radar sensor(s) 334 use radio waves to determine the range, angle, and velocity of objects. Radar sensor(s) 334 produce electromagnetic waves in the radio or microwave spectrum. The electromagnetic waves reflect off an object and some of the reflected waves return to the radar sensor, thereby providing information about the object’s position and velocity.
  • Radar sensor(s) 334 can include one or more of short-range radar(s), medium-range radar(s), and long-range radar(s).
  • a short-range radar measures objects located at about 0.1-30 meters from the radar.
  • a short-range radar is useful in detecting objects located near the vehicle, such as other vehicles, buildings, walls, pedestrians, bicyclists, etc.
  • a short-range radar can be used to detect a blind spot, assist in lane changing, provide rear-end collision warning, assist in parking, provide emergency braking, or the like.
  • a medium -range radar measures objects located at about 30-80 meters from the radar.
  • a long-range radar measures objects located at about 80-200 meters.
  • Medium- and/or long-range radars can be useful in, for example, traffic following, adaptive cruise control, and/or highway automatic braking.
  • Sensor data generated by radar sensor(s) 334 can also be provided to vehicle perception and planning system 320 via communication path 333 for further processing and controlling the vehicle operations.
  • Radar sensor(s) 334 can be mounted on, or integrated to, a vehicle at any location (e.g., rear-view mirrors, pillars, front grille, and/or back bumpers, etc.).
  • Other vehicle onboard sensor(s) 330 can also include ultrasonic sensor(s) 336.
  • Ultrasonic sensor(s) 336 use acoustic waves or pulses to measure objects located external to a vehicle. The acoustic waves generated by ultrasonic sensor(s) 336 are transmitted to the surrounding environment. At least some of the transmitted waves are reflected off an object and return to the ultrasonic sensor(s) 336. Based on the return signals, a distance of the object can be calculated.
  • Ultrasonic sensor(s) 336 can be useful in, for example, checking blind spots, identifying parking spaces, providing lane changing assistance into traffic, or the like.
  • Ultrasonic sensor(s) 336 can also be provided to vehicle perception and planning system 320 via communication path 333 for further processing and controlling the vehicle operations.
  • Ultrasonic sensor(s) 336 can be mounted on, or integrated to, a vehicle at any location (e.g., rear-view mirrors, pillars, front grille, and/or back bumpers, etc.).
  • one or more other sensor(s) 338 may be attached in a vehicle and may also generate sensor data.
  • Other sensor(s) 338 may include, for example, global positioning systems (GPS), inertial measurement units (IMU), or the like.
  • Sensor data generated by other sensor(s) 338 can also be provided to vehicle perception and planning system 320 via communication path 333 for further processing and controlling the vehicle operations.
  • communication path 333 may include one or more communication links to transfer data between the various sensor(s) 330 and vehicle perception and planning system 320.
  • sensor data from other vehicle onboard sensor(s) 330 can be provided to vehicle onboard LiDAR system(s) 310 via communication path 331.
  • LiDAR system(s) 310 may process the sensor data from other vehicle onboard sensor(s) 330.
  • sensor data from camera(s) 332, radar sensor(s) 334, ultrasonic sensor(s) 336, and/or other sensor(s) 338 may be correlated or fused with sensor data LiDAR system(s) 310, thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system 320.
  • sensors onboard other vehicle(s) 350 are used to provide additional sensor data separately or together with LiDAR system(s) 310.
  • two or more nearby vehicles may have their own respective LiDAR sensor(s), camera(s), radar sensor(s), ultrasonic sensor(s), etc.
  • Nearby vehicles can communicate and share sensor data with one another. Communications between vehicles are also referred to as V2V
  • sensor data generated by other vehicle(s) 350 can be communicated to vehicle perception and planning system 320 and/or vehicle onboard LiDAR system(s) 310, via communication path 353 and/or communication path 351, respectively.
  • Communication paths 353 and 351 can be any wired or wireless communication links that can transfer data. Sharing sensor data facilitates a better perception of the environment external to the vehicles. For instance, a first vehicle may not sense a pedestrian that is behind a second vehicle but is approaching the first vehicle. The second vehicle may share the sensor data related to this pedestrian with the first vehicle such that the first vehicle can have additional reaction time to avoid collision with the pedestrian.
  • data generated by sensors onboard other vehicle(s) 350 may be correlated or fused with sensor data generated by LiDAR system(s) 310 (or with other LiDAR systems located in other vehicles), thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system 320.
  • intelligent infrastructure system(s) 340 are used to provide sensor data separately or together with LiDAR system(s) 310. Certain infrastructures may be configured to communicate with a vehicle to convey information and vice versa. Communications between a vehicle and infrastructures are generally referred to as V2I (vehicle to infrastructure) communications.
  • intelligent infrastructure system(s) 340 may include an intelligent traffic light that can convey its status to an approaching vehicle in a message such as “changing to yellow in 5 seconds.”
  • Intelligent infrastructure system(s) 340 may also include its own LiDAR system mounted near an intersection such that it can convey traffic monitoring information to a vehicle. For example, a left-turning vehicle at an intersection may not have sufficient sensing capabilities because some of its own sensors may be blocked by traffic in the opposite direction.
  • sensors of intelligent infrastructure system(s) 340 can provide useful data to the left-turning vehicle.
  • data may include, for example, traffic conditions, information of objects in the direction the vehicle is turning to, traffic light status and predictions, or the like.
  • These sensor data generated by intelligent infrastructure system(s) 340 can be provided to vehicle perception and planning system 320 and/or vehicle onboard LiDAR system(s) 310, via communication paths 343 and/or 341, respectively.
  • Communication paths 343 and/or 341 can include any wired or wireless communication links that can transfer data.
  • sensor data from intelligent infrastructure system(s) 340 may be transmitted to LiDAR system(s) 310 and correlated or fused with sensor data generated by LiDAR system(s) 310, thereby at least partially offloading the sensor fusion process performed by vehicle perception and planning system 320.
  • V2V and V2I communications described above are examples of vehicle-to-X (V2X) communications, where the “X” represents any other devices, systems, sensors, infrastructure, or the like that can share data with a vehicle.
  • vehicle perception and planning system 320 receives sensor data from one or more of LiDAR system(s) 310, other vehicle onboard sensor(s) 330, other vehicle(s) 350, and/or intelligent infrastructure system(s) 340.
  • sensor fusion sub-system 322 can generate a 360- degree model using multiple images or videos captured by multiple cameras disposed at different positions of the vehicle.
  • Sensor fusion sub-system 322 obtains sensor data from different types of sensors and uses the combined data to perceive the environment more accurately.
  • a vehicle onboard camera 332 may not capture a clear image because it is facing the sun or a light source (e.g., another vehicle’s headlight during nighttime) directly.
  • a LiDAR system 310 may not be affected as much and therefore sensor fusion sub-system 322 can combine sensor data provided by both camera 332 and LiDAR system 310, and use the sensor data provided by LiDAR system 310 to compensate the unclear image captured by camera 332.
  • a radar sensor 334 may work better than a camera 332 or a LiDAR system 310. Accordingly, sensor fusion sub-system 322 may use sensor data provided by the radar sensor 334 to compensate the sensor data provided by camera 332 or LiDAR system 310.
  • sensor data generated by other vehicle onboard sensor(s) 330 may have a lower resolution (e.g., radar sensor data) and thus may need to be correlated and confirmed by LiDAR system(s) 310, which usually has a higher resolution.
  • LiDAR system(s) 310 which usually has a higher resolution.
  • a sewage cover also referred to as a manhole cover
  • vehicle perception and planning system 320 may not be able to determine whether the object is an obstacle that the vehicle needs to avoid.
  • High-resolution sensor data generated by LiDAR system(s) 310 thus can be used to correlated and confirm that the object is a sewage cover and causes no harm to the vehicle.
  • Vehicle perception and planning system 320 further comprises an object classifier 323.
  • object classifier 323 can use any computer vision techniques to detect and classify the objects and estimate the positions of the objects.
  • object classifier 323 can use machine-learning based techniques to detect and classify objects. Examples of the machine- learning based techniques include utilizing algorithms such as region-based convolutional neural networks (R-CNN), Fast R-CNN, Faster R-CNN, histogram of oriented gradients (HOG), region -based fully convolutional network (R-FCN), single shot detector (SSD), spatial pyramid pooling (SPP-net), and/or You Only Look Once (Yolo).
  • Vehicle perception and planning system 320 further comprises a road detection sub- system 324.
  • Road detection sub-system 324 localizes the road and identifies objects and/or markings on the road. For example, based on raw or fused sensor data provided by radar sensor(s) 334, camera(s) 332, and/or LiDAR system(s) 310, road detection sub-system 324 can build a 3D model of the road based on machine-learning techniques (e.g., pattern recognition algorithms for identifying lanes). Using the 3D model of the road, road detection sub-system 324 can identify objects (e.g., obstacles or debris on the road) and/or markings on the road (e.g., lane lines, turning marks, crosswalk marks, or the like).
  • objects e.g., obstacles or debris on the road
  • markings on the road e.g., lane lines, turning marks, crosswalk marks, or the like.
  • Vehicle perception and planning system 320 further comprises a localization and vehicle posture sub-system 325.
  • localization and vehicle posture sub-system 325 can determine position of the vehicle and the vehicle’s posture. For example, using sensor data from LiDAR system(s) 310, camera(s) 332, and/or GPS data, localization and vehicle posture sub-system 325 can determine an accurate position of the vehicle on the road and the vehicle’s six degrees of freedom (e.g., whether the vehicle is moving forward or backward, up or down, and left or right).
  • high-definition (HD) maps are used for vehicle localization. HD maps can provide highly detailed, three-dimensional, computerized maps that pinpoint a vehicle’s location.
  • localization and vehicle posture sub-system 325 can determine precisely the vehicle’s current position (e.g., which lane of the road the vehicle is currently in, how close it is to a curb or a sidewalk) and predict vehicle’s future positions.
  • Vehicle perception and planning system 320 further comprises obstacle predictor 326.
  • Objects identified by object classifier 323 can be stationary (e.g., a light pole, a road sign) or dynamic (e.g., a moving pedestrian, bicycle, another car). For moving objects, predicting their moving path or future positions can be important to avoid collision. Obstacle predictor 326 can predict an obstacle trajectory and/or warn the driver or the vehicle planning sub-system 328 about a potential collision. For example, if there is a high likelihood that the obstacle’s trajectory intersects with the vehicle’s current moving path, obstacle predictor 326 can generate such a warning. Obstacle predictor 326 can use a variety of techniques for making such a prediction.
  • Such techniques include, for example, constant velocity or acceleration models, constant turn rate and velocity/accel eration models, Kalman Filter and Extended Kalman Filter based models, recurrent neural network (RNN) based models, long short-term memory (LSTM) neural network based models, encoder-decoder RNN models, or the like.
  • RNN recurrent neural network
  • LSTM long short-term memory
  • vehicle perception and planning system 320 further comprises vehicle planning sub-system 328.
  • Vehicle planning sub-system 328 can include one or more planners such as a route planner, a driving behaviors planner, and a motion planner.
  • the route planner can plan the route of a vehicle based on the vehicle’s current location data, target location data, traffic information, etc.
  • the driving behavior planner adjusts the timing and planned movement based on how other objects might move, using the obstacle prediction results provided by obstacle predictor 326.
  • the motion planner determines the specific operations the vehicle needs to follow.
  • the planning results are then communicated to vehicle control system 380 via vehicle interface 370.
  • the communication can be performed through communication paths 327 and 371, which include any wired or wireless communication links that can transfer data.
  • Vehicle control system 380 controls the vehicle’s steering mechanism, throttle, brake, etc., to operate the vehicle according to the planned route and movement.
  • vehicle perception and planning system 320 may further comprise a user interface 360, which provides a user (e.g., a driver) access to vehicle control system 380 to, for example, override or take over control of the vehicle when necessary.
  • User interface 360 may also be separate from vehicle perception and planning system 320.
  • User interface 360 can communicate with vehicle perception and planning system 320, for example, to obtain and display raw or fused sensor data, identified objects, vehicle’s location/posture, etc. These displayed data can help a user to better operate the vehicle.
  • User interface 360 can communicate with vehicle perception and planning system 320 and/or vehicle control system 380 via communication paths 321 and 361 respectively, which include any wired or wireless communication links that can transfer data. It is understood that the various systems, sensors, communication links, and interfaces in FIG. 3 can be configured in any desired manner and not limited to the configuration shown in FIG. 3.
  • FIG. 4 is a block diagram illustrating an example LiDAR system 400.
  • LiDAR system 400 can be used to implement LiDAR systems 210, 220A-220I, and/or 310 shown in FIGs. 1 and 2.
  • LiDAR system 400 comprises a light source 410, a transmitter 420, an optical receiver and light detector 430, a steering system 440, and a control circuitry 350. These components are coupled together using communications paths 412, 414, 422, 432, 442, 452, and 462. These communications paths include communication links (wired or wireless, bidirectional or unidirectional) among the various LiDAR system components, but need not be physical components themselves.
  • the communications paths can be implemented by one or more electrical wires, buses, or optical fibers
  • the communication paths can also be wireless channels or free-space optical paths so that no physical communication medium is present.
  • communication path 414 between light source 410 and transmitter 420 may be implemented using one or more optical fibers.
  • Communication paths 432 and 452 may represent optical paths implemented using free space optical components and/or optical fibers.
  • communication paths 412, 422, 442, and 462 may be implemented using one or more electrical wires that carry electrical signals.
  • the communications paths can also include one or more of the above types of communication mediums (e.g., they can include an optical fiber and a free-space optical component, or include one or more optical fibers and one or more electrical wires).
  • LiDAR system 400 can be a coherent LiDAR system.
  • a coherent LiDAR system may include a route 472 providing a portion of transmission light from transmitter 420 to optical receiver and light detector 430.
  • Route 372 may include one or more optics (e.g., optical fibers, lens, mirrors, etc.) for providing the light from transmitter 420 to optical receiver and light detector 430.
  • the transmission light provided by transmitter 420 may be modulated light and can be split into two portions.
  • One portion is transmitted to the FOV, while the second portion is sent to the optical receiver and light detector of the LiDAR system.
  • the second portion is also referred to as the light that is kept local (LO) to the LiDAR system.
  • the transmission light is scattered or reflected by various objects in the FOV and at least a portion of it forms return light.
  • the return light is subsequently detected and interferometrically recombined with the second portion of the transmission light that was kept local.
  • Coherent LiDAR provides a means of optically sensing an object’s range as well as its relative velocity along the line-of-sight (LOS).
  • LiDAR system 400 can also include other components not depicted in FIG. 4, such as power buses, power supplies, LED indicators, switches, etc. Additionally, other communication connections among components may be present, such as a direct connection between light source 410 and optical receiver and light detector 430 to provide a reference signal so that the time from when a light pulse is transmitted until a return light pulse is detected can be accurately measured.
  • Light source 410 outputs laser light for illuminating objects in a field of view (FOV).
  • the laser light can be infrared light having a wavelength in the range of 700nm to 1mm.
  • Light source 410 can be, for example, a semiconductor-based laser (e.g., a diode laser) and/or a fiber- based laser.
  • a semiconductor-based laser can be, for example, an edge emitting laser (EEL), a vertical cavity surface emitting laser (VCSEL), an external-cavity diode laser, a vertical- external-cavity surface-emitting laser, a distributed feedback (DFB) laser, a distributed Bragg reflector (DBR) laser, an interband cascade laser, a quantum cascade laser, a quantum well laser, a double heterostructure laser, or the like.
  • EEL edge emitting laser
  • VCSEL vertical cavity surface emitting laser
  • DBR distributed Bragg reflector
  • a fiber-based laser is a laser in which the active gain medium is an optical fiber doped with rare-earth elements such as erbium, ytterbium, neodymium, dysprosium, praseodymium, thulium and/or holmium.
  • a fiber laser is based on double-clad fibers, in which the gain medium forms the core of the fiber surrounded by two layers of cladding.
  • the double-clad fiber allows the core to be pumped with a high-power beam, thereby enabling the laser source to be a high power fiber laser source.
  • light source 410 comprises a master oscillator (also referred to as a seed laser) and power amplifier (MOP A).
  • the power amplifier amplifies the output power of the seed laser.
  • the power amplifier can be a fiber amplifier, a bulk amplifier, or a semiconductor optical amplifier.
  • the seed laser can be a diode laser (e.g., a Fabry-Perot cavity laser, a distributed feedback laser), a solid-state bulk laser, or a tunable external-cavity diode laser.
  • light source 410 can be an optically pumped microchip laser. Microchip lasers are alignment-free monolithic solid-state lasers where the laser crystal is directly contacted with the end mirrors of the laser resonator.
  • a microchip laser is typically pumped with a laser diode (directly or using a fiber) to obtain the desired output power.
  • a microchip laser can be based on neodymium-doped yttrium aluminum garnet (Y3A15O12) laser crystals (i.e., Nd:YAG), or neodymium-doped vanadate (i.e., ND:YV04) laser crystals.
  • light source 410 may have multiple amplification stages to achieve a high power gain such that the laser output can have high power, thereby enabling the LiDAR system to have a long scanning range.
  • the power amplifier of light source 410 can be controlled such that the power gain can be varied to achieve any desired laser output power.
  • FIG. 5 is a block diagram illustrating an example fiber-based laser source 500 having a seed laser and one or more pumps (e.g., laser diodes) for pumping desired output power.
  • Fiber- based laser source 500 is an example of light source 410 depicted in FIG. 4.
  • fiber-based laser source 500 comprises a seed laser 502 to generate initial light pulses of one or more wavelengths (e.g., infrared wavelengths such as 1550 nm), which are provided to a wavelength-division multiplexor (WDM) 504 via an optical fiber 503.
  • WDM wavelength-division multiplexor
  • Fiber-based laser source 500 further comprises a pump 506 for providing laser power (e.g., of a different wavelength, such as 980 nm) to WDM 504 via an optical fiber 505.
  • WDM wavelength-division multiplexor
  • WDM 504 multiplexes the light pulses provided by seed laser 502 and the laser power provided by pump 506 onto a single optical fiber 507.
  • the output of WDM 504 can then be provided to one or more pre-amplifier(s) 508 via optical fiber 507.
  • Pre-amplifier(s) 508 can be optical amplifier(s) that amplify optical signals (e.g., with about 10-30 dB gain). In some embodiments, pre-amplifier(s) 508 are low noise amplifiers.
  • Pre-amplifier(s) 508 output to an optical combiner 510 via an optical fiber 509.
  • Combiner 510 combines the output laser light of pre-amplifier(s) 508 with the laser power provided by pump 512 via an optical fiber 511.
  • Combiner 510 can combine optical signals having the same wavelength or different wavelengths.
  • a combiner is a WDM.
  • Combiner 510 provides combined optical signals to a booster amplifier 514, which produces output light pulses via optical fiber 515.
  • the booster amplifier 514 provides further amplification of the optical signals (e.g., another 20-40dB).
  • the output light pulses can then be transmitted to transmitter 420 and/or steering mechanism 440 (shown in FIG. 4).
  • FIG. 5 illustrates one example configuration of fiber-based laser source 500.
  • Laser source 500 can have many other configurations using different combinations of one or more components shown in FIG. 5 and/or other components not shown in FIG. 5 (e.g., other components such as power supplies, lens(es), filters, splitters, combiners, etc.).
  • fiber-based laser source 500 can be controlled (e.g., by control circuitry 350) to produce pulses of different amplitudes based on the fiber gain profile of the fiber used in fiber-based laser source 500.
  • Communication path 412 couples fiber-based laser source 500 to control circuitry 350 (shown in FIG. 4) so that components of fiber-based laser source 500 can be controlled by or otherwise communicate with control circuitry 350.
  • fiber-based laser source 500 may include its own dedicated controller. Instead of control circuitry 350 communicating directly with components of fiber-based laser source 500, a dedicated controller of fiber-based laser source 500 communicates with control circuitry 350 and controls and/or communicates with the components of fiber-based laser source 500.
  • Fiber-based laser source 500 can also include other components not shown, such as one or more power connectors, power supplies, and/or power lines.
  • typical operating wavelengths of light source 410 comprise, for example, about 850 nm, about 905 nm, about 940 nm, about 1064 nm, and about 1550 nm.
  • the upper limit of maximum usable laser power is set by the U.S. FDA (U.S. Food and Drug Administration) regulations.
  • the optical power limit at 1550 nm wavelength is much higher than those of the other aforementioned wavelengths. Further, at 1550 nm, the optical power loss in a fiber is low. There characteristics of the 1550 nm wavelength make it more beneficial for long-range LiDAR applications.
  • the amount of optical power output from light source 410 can be characterized by its peak power, average power, pulse energy, and/or the pulse energy density.
  • the peak power is the ratio of pulse energy to the width of the pulse (e.g., full width at half maximum or FWHM). Thus, a smaller pulse width can provide a larger peak power for a fixed amount of pulse energy.
  • a pulse width can be in the range of nanosecond or picosecond.
  • the average power is the product of the energy of the pulse and the pulse repetition rate (PRR). As described in more detail below, the PRR represents the frequency of the pulsed laser light. In general, the smaller the time interval between the pulses, the higher the PRR.
  • the PRR typically corresponds to the maximum range that a LiDAR system can measure.
  • Light source 410 can be configured to produce pulses at high PRR to meet the desired number of data points in a point cloud generated by the LiDAR system.
  • Light source 410 can also be configured to produce pulses at medium or low PRR to meet the desired maximum detection distance.
  • Wall plug efficiency (WPE) is another factor to evaluate the total power consumption, which may be a useful indicator in evaluating the laser efficiency.
  • WPE Wall plug efficiency
  • FIG. 2 multiple LiDAR systems may be attached to a vehicle, which may be an electrical -powered vehicle or a vehicle otherwise having limited fuel or battery power supply. Therefore, high WPE and intelligent ways to use laser power are often among the important considerations when selecting and configuring light source 410 and/or designing laser delivery systems for vehicle-mounted LiDAR applications.
  • Light source 410 can be configured to include many other types of light sources (e.g., laser diodes, short-cavity fiber lasers, solid-state lasers, and/or tunable external cavity diode lasers) that are configured to generate one or more light signals at various wavelengths.
  • light source 410 comprises amplifiers (e.g., pre-amplifiers and/or booster amplifiers), which can be a doped optical fiber amplifier, a solid-state bulk amplifier, and/or a semiconductor optical amplifier. The amplifiers are configured to receive and amplify light signals with desired gains.
  • LiDAR system 400 further comprises a transmitter 420.
  • Light source 410 provides laser light (e.g., in the form of a laser beam) to transmitter 420.
  • the laser light provided by light source 410 can be amplified laser light with a predetermined or controlled wavelength, pulse repetition rate, and/or power level.
  • Transmitter 420 receives the laser light from light source 410 and transmits the laser light to steering mechanism 440 with low divergence.
  • transmitter 420 can include, for example, optical components (e.g., lens, fibers, mirrors, etc.) for transmitting one or more laser beams to a field- of-view (FOV) directly or via steering mechanism 440. While FIG. 4 illustrates transmitter 420 and steering mechanism 440 as separate components, they may be combined or integrated as one system in some embodiments. Steering mechanism 440 is described in more detail below.
  • transmitter 420 often comprises a collimating lens configured to collect the diverging laser beams and produce more parallel optical beams with reduced or minimum divergence.
  • the collimated optical beams can then be further directed through various optics such as mirrors and lens.
  • a collimating lens may be, for example, a single plano-convex lens or a lens group.
  • the collimating lens can be configured to achieve any desired properties such as the beam diameter, divergence, numerical aperture, focal length, or the like.
  • a beam propagation ratio or beam quality factor (also referred to as the M2 factor) is used for measurement of laser beam quality.
  • the M2 factor represents a degree of variation of a beam from an ideal Gaussian beam.
  • the M2 factor reflects how well a collimated laser beam can be focused on a small spot, or how well a divergent laser beam can be collimated. Therefore, light source 410 and/or transmitter 420 can be configured to meet, for example, a scan resolution requirement while maintaining the desired M2 factor.
  • One or more of the light beams provided by transmitter 420 are scanned by steering mechanism 440 to a FOV.
  • Steering mechanism 440 scans light beams in multiple dimensions (e.g., in both the horizontal and vertical dimension) to facilitate LiDAR system 400 to map the environment by generating a 3D point cloud.
  • a horizontal dimension can be a dimension that is parallel to the horizon or a surface associated with the LiDAR system or a vehicle (e.g., a road surface).
  • a vertical dimension is perpendicular to the horizontal dimension (i.e., the vertical dimension forms a 90-degree angle with the horizontal dimension).
  • Steering mechanism 440 will be described in more detail below.
  • the laser light scanned to an FOV may be scattered or reflected by an object in the FOV.
  • FIG. 4 further illustrates an optical receiver and light detector 430 configured to receive the return light.
  • Optical receiver and light detector 430 comprises an optical receiver that is configured to collect the return light from the FOV.
  • the optical receiver can include optics (e.g., lens, fibers, mirrors, etc.) for receiving, redirecting, focusing, amplifying, and/or filtering return light from the FOV.
  • the optical receiver often includes a collection lens (e.g., a single plano-convex lens or a lens group) to collect and/or focus the collected return light onto a light detector.
  • a light detector detects the return light focused by the optical receiver and generates current and/or voltage signals proportional to the incident intensity of the return light. Based on such current and/or voltage signals, the depth information of the object in the FOV can be derived.
  • One example method for deriving such depth information is based on the direct TOF (time of flight), which is described in more detail below.
  • a light detector may be characterized by its detection sensitivity, quantum efficiency, detector bandwidth, linearity, signal to noise ratio (SNR), overload resistance, interference immunity, etc.
  • SNR signal to noise ratio
  • the light detector can be configured or customized to have any desired characteristics.
  • optical receiver and light detector 430 can be configured such that the light detector has a large dynamic range while having a good linearity.
  • the light detector linearity indicates the detector’s capability of maintaining linear relationship between input optical signal power and the detector’s output.
  • a detector having good linearity can maintain a linear relationship over a large dynamic input optical signal range.
  • a light detector structure can be a PEST based structure, which has a undoped intrinsic semiconductor region (i.e., an “i” region) between a p- type semiconductor and an n-type semiconductor region.
  • Other light detector structures comprise, for example, an APD (avalanche photodiode) based structure, a PMT (photomultiplier tube) based structure, a SiPM (Silicon photomultiplier) based structure, a SPAD (single-photon avalanche diode) based structure, and/or quantum wires.
  • Si, InGaAs, and/or Si/Ge based materials can be used. It is understood that many other detector structures and/or material systems can be used in optical receiver and light detector 430.
  • a light detector may have an internal gain such that the input signal is amplified when generating an output signal.
  • noise may also be amplified due to the light detector’s internal gain.
  • Common types of noise include signal shot noise, dark current shot noise, thermal noise, and amplifier noise.
  • optical receiver and light detector 430 may include a pre-amplifier that is a low noise amplifier (LNA).
  • the pre-amplifier may also include a transimpedance amplifier (TIA), which converts a current signal to a voltage signal.
  • TIA transimpedance amplifier
  • NEP input equivalent noise or noise equivalent power
  • the NEP of a light detector specifies the power of the weakest signal that can be detected and therefore it in turn specifies the maximum range of a LiDAR system.
  • various light detector optimization techniques can be used to meet the requirement of LiDAR system 400. Such optimization techniques may include selecting different detector structures, materials, and/or implementing signal processing techniques (e.g., filtering, noise reduction, amplification, or the like).
  • signal processing techniques e.g., filtering, noise reduction, amplification, or the like.
  • coherent detection can also be used for a light detector.
  • Coherent detection allows for detecting amplitude and phase information of the received light by interfering the received light with a local oscillator. Coherent detection can improve detection sensitivity and noise immunity.
  • FIG. 4 further illustrates that LiDAR system 400 comprises steering mechanism 440.
  • steering mechanism 440 directs light beams from transmitter 420 to scan an FOV in multiple dimensions.
  • a steering mechanism is referred to as a raster mechanism, a scanning mechanism, or simply a light scanner. Scanning light beams in multiple directions (e.g., in both the horizontal and vertical directions) facilitates a LiDAR system to map the environment by generating an image or a 3D point cloud.
  • a steering mechanism can be based on mechanical scanning and/or solid-state scanning. Mechanical scanning uses rotating mirrors to steer the laser beam or physically rotate the LiDAR transmitter and receiver (collectively referred to as transceiver) to scan the laser beam.
  • Solid-state scanning directs the laser beam to various positions through the FOV without mechanically moving any macroscopic components such as the transceiver.
  • Solid-state scanning mechanisms include, for example, optical phased arrays based steering and flash LiDAR based steering. In some embodiments, because solid-state scanning mechanisms do not physically move macroscopic components, the steering performed by a solid-state scanning mechanism may be referred to as effective steering.
  • a LiDAR system using solid-state scanning may also be referred to as a non-mechanical scanning or simply non- scanning LiDAR system (a flash LiDAR system is an example non-scanning LiDAR system).
  • Steering mechanism 440 can be used with a transceiver (e.g., transmitter 420 and optical receiver and light detector 430) to scan the FOV for generating an image or a 3D point cloud.
  • a transceiver e.g., transmitter 420 and optical receiver and light detector 430
  • a two-dimensional mechanical scanner can be used with a single-point or several single-point transceivers.
  • a single-point transceiver transmits a single light beam or a small number of light beams (e.g., 2-8 beams) to the steering mechanism.
  • a two-dimensional mechanical steering mechanism comprises, for example, polygon mirror(s), oscillating mirror(s), rotating prism(s), rotating tilt mirror surface(s), single- plane or multi-plane mirror(s), or a combination thereof.
  • steering mechanism 440 may include non-mechanical steering mechanism(s) such as solid-state steering mechanism(s).
  • steering mechanism 440 can be based on tuning wavelength of the laser light combined with refraction effect, and/or based on reconfigurable grating/phase array.
  • steering mechanism 440 can use a single scanning device to achieve two- dimensional scanning or multiple scanning devices combined to realize two-dimensional scanning.
  • a one-dimensional mechanical scanner can be used with an array or a large number of single-point transceivers.
  • the transceiver array can be mounted on a rotating platform to achieve 360-degree horizontal field of view.
  • a static transceiver array can be combined with the one- dimensional mechanical scanner.
  • a one-dimensional mechanical scanner comprises polygon mirror(s), oscillating mirror(s), rotating prism(s), rotating tilt mirror surface(s), or a combination thereof, for obtaining a forward-looking horizontal field of view. Steering mechanisms using mechanical scanners can provide robustness and reliability in high volume production for automotive applications.
  • a two-dimensional transceiver can be used to generate a scan image or a 3D point cloud directly.
  • a stitching or micro shift method can be used to improve the resolution of the scan image or the field of view being scanned.
  • signals generated at one direction e.g., the horizontal direction
  • signals generated at the other direction e.g., the vertical direction
  • steering mechanism 440 comprise one or more optical redirection elements (e.g., mirrors or lenses) that steer return light signals (e.g., by rotating, vibrating, or directing) along a receive path to direct the return light signals to optical receiver and light detector 430.
  • the optical redirection elements that direct light signals along the transmitting and receiving paths may be the same components (e.g., shared), separate components (e.g., dedicated), and/or a combination of shared and separate components. This means that in some cases the transmitting and receiving paths are different although they may partially overlap (or in some cases, substantially overlap or completely overlap).
  • LiDAR system 400 further comprises control circuitry 350.
  • Control circuitry 350 can be configured and/or programmed to control various parts of the LiDAR system 400 and/or to perform signal processing.
  • control circuitry 350 can be configured and/or programmed to perform one or more control operations including, for example, controlling light source 410 to obtain the desired laser pulse timing, the pulse repetition rate, and power; controlling steering mechanism 440 (e.g., controlling the speed, direction, and/or other parameters) to scan the FOV and maintain pixel registration and /or alignment; controlling optical receiver and light detector 430 (e.g., controlling the sensitivity, noise reduction, filtering, and/or other parameters) such that it is an optimal state; and monitoring overall system health/ status for functional safety (e.g., monitoring the laser output power and/or the steering mechanism operating status for safety).
  • controlling light source 410 to obtain the desired laser pulse timing, the pulse repetition rate, and power
  • controlling steering mechanism 440 e.g., controlling the speed, direction, and/or other parameters
  • Control circuitry 350 can also be configured and/or programmed to perform signal processing to the raw data generated by optical receiver and light detector 430 to derive distance and reflectance information, and perform data packaging and communication to vehicle perception and planning system 320 (shown in FIG. 3). For example, control circuitry 350 determines the time it takes from transmitting a light pulse until a corresponding return light pulse is received; determines when a return light pulse is not received for a transmitted light pulse; determines the direction (e.g., horizontal and/or vertical information) for a transmitted/return light pulse; determines the estimated range in a particular direction; derives the reflectivity of an object in the FOV, and/or determines any other type of data relevant to LiDAR system 400.
  • direction e.g., horizontal and/or vertical information
  • LiDAR system 400 can be disposed in a vehicle, which may operate in many different environments including hot or cold weather, rough road conditions that may cause intense vibration, high or low humidities, dusty areas, etc. Therefore, in some embodiments, optical and/or electronic components of LiDAR system 400 (e.g., optics in transmitter 420, optical receiver and light detector 430, and steering mechanism 440) are disposed and/or configured in such a manner to maintain long term mechanical and optical stability. For example, components in LiDAR system 400 may be secured and sealed such that they can operate under all conditions a vehicle may encounter.
  • optical and/or electronic components of LiDAR system 400 e.g., optics in transmitter 420, optical receiver and light detector 430, and steering mechanism 440
  • components in LiDAR system 400 may be secured and sealed such that they can operate under all conditions a vehicle may encounter.
  • an anti-moisture coating and/or hermetic sealing may be applied to optical components of transmitter 420, optical receiver and light detector 430, and steering mechanism 440 (and other components that are susceptible to moisture).
  • housing(s), enclosure(s), fairing(s), and/or window can be used in LiDAR system 400 for providing desired characteristics such as hardness, ingress protection (IP) rating, self- cleaning capability, resistance to chemical and resistance to impact, or the like.
  • IP ingress protection
  • efficient and economical methodologies for assembling LiDAR system 400 may be used to meet the LiDAR operating requirements while keeping the cost low.
  • LiDAR system 400 can include other functional units, blocks, or segments, and can include variations or combinations of these above functional units, blocks, or segments.
  • LiDAR system 400 can also include other components not depicted in FIG. 4, such as power buses, power supplies, LED indicators, switches, etc.
  • other connections among components may be present, such as a direct connection between light source 410 and optical receiver and light detector 430 so that light detector 430 can accurately measure the time from when light source 410 transmits a light pulse until light detector 430 detects a return light pulse.
  • These communications paths represent communication (bidirectional or unidirectional) among the various LiDAR system components but need not be physical components themselves.
  • the communications paths can be implemented by one or more electrical wires, buses, or optical fibers
  • the communication paths can also be wireless channels or open-air optical paths so that no physical communication medium is present.
  • communication path 414 includes one or more optical fibers
  • communication path 452 represents an optical path
  • communication paths 412, 422, 442, and 462 are all electrical wires that carry electrical signals.
  • the communication paths can also include more than one of the above types of communication mediums (e.g., they can include an optical fiber and an optical path, or one or more optical fibers and one or more electrical wires).
  • an example LiDAR system 600 includes a laser light source (e.g., a fiber laser), a steering mechanism (e.g., a system of one or more moving mirrors), and a light detector (e.g., a photodetector with one or more optics).
  • a laser light source e.g., a fiber laser
  • a steering mechanism e.g., a system of one or more moving mirrors
  • a light detector e.g., a photodetector with one or more optics.
  • LiDAR system 600 can be implemented using, for example, LiDAR system 400 described above.
  • LiDAR system 600 transmits a light pulse 602 along light path 604 as determined by the steering mechanism of LiDAR system 600.
  • light pulse 602 which is generated by the laser light source, is a short pulse of laser light.
  • the signal steering mechanism of the LiDAR system 600 is a pulsed-signal steering mechanism.
  • LiDAR systems can operate by generating, transmitting, and detecting light signals that are not pulsed and derive ranges to an object in the surrounding environment using techniques other than time-of-flight. For example, some LiDAR systems use frequency modulated continuous waves (i.e., “FMCW”). It should be further appreciated that any of the techniques described herein with respect to time-of-flight based systems that use pulsed signals also may be applicable to LiDAR systems that do not use one or both of these techniques.
  • FMCW frequency modulated continuous waves
  • FIG. 6A e.g., illustrating a time-of-flight LiDAR system that uses light pulses
  • light pulse 602 when light pulse 602 reaches object 606, light pulse 602 scatters or reflects to form a return light pulse 608.
  • Return light pulse 608 may return to system 600 along light path 610.
  • the time from when transmitted light pulse 602 leaves LiDAR system 600 to when return light pulse 608 arrives back at LiDAR system 600 can be measured (e.g., by a processor or other electronics, such as control circuitry 350, within the LiDAR system).
  • This time-of-flight combined with the knowledge of the speed of light can be used to determine the range/di stance from LiDAR system 600 to the portion of object 606 where light pulse 602 scattered or reflected.
  • LiDAR system 600 scans the external environment (e.g., by directing light pulses 602, 622, 626, 630 along light paths 604, 624, 628, 632, respectively). As depicted in FIG. 6C, LiDAR system 600 receives return light pulses 608, 642, 648 (which correspond to transmitted light pulses 602, 622, 630, respectively). Return light pulses 608, 642, and 648 are formed by scattering or reflecting the transmitted light pulses by one of objects 606 and 614. Return light pulses 608, 642, and 648 may return to LiDAR system 600 along light paths 610, 644, and 646, respectively.
  • the external environment within the detectable range e.g., the field of view between path 604 and 632, inclusively
  • the external environment within the detectable range can be precisely mapped or plotted (e.g., by generating a 3D point cloud or images).
  • LiDAR system 600 may determine that there are no objects within a detectable range of LiDAR system 600 (e.g., an object is beyond the maximum scanning distance of LiDAR system 600). For example, in FIG. 6B, light pulse 626 may not have a corresponding return light pulse (as illustrated in FIG. 6C) because light pulse 626 may not produce a scattering event along its transmission path 628 within the predetermined detection range.
  • LiDAR system 600 or an external system in communication with LiDAR system 600 (e.g., a cloud system or service), can interpret the lack of return light pulse as no object being disposed along light path 628 within the detectable range of LiDAR system 600.
  • light pulses 602, 622, 626, and 630 can be transmitted in any order, serially, in parallel, or based on other timings with respect to each other.
  • FIG. 6B depicts transmitted light pulses as being directed in one dimension or one plane (e.g., the plane of the paper)
  • LiDAR system 600 can also direct transmitted light pulses along other dimension(s) or plane(s).
  • LiDAR system 600 can also direct transmitted light pulses in a dimension or plane that is perpendicular to the dimension or plane shown in FIG. 6B, thereby forming a 2-dimensional transmission of the light pulses.
  • This 2-dimensional transmission of the light pulses can be point-by-point, line-by-line, all at once, or in some other manner.
  • LiDAR system 600 can be configured to perform a point scan, a line scan, a one-shot without scanning, or a combination thereof.
  • a point cloud or image from a 1 -dimensional transmission of light pulses e.g., a single horizontal line
  • 2- dimensional data e.g., (1) data from the horizontal transmission direction and (2) the range or distance to objects
  • a point cloud or image from a 2-dimensional transmission of light pulses can generate 3-dimensional data (e.g., (1) data from the horizontal transmission direction, (2) data from the vertical transmission direction, and (3) the range or distance to objects).
  • a LiDAR system performing an n-dimensional transmission of light pulses generates (n+1) dimensional data.
  • the LiDAR system can measure the depth of an object or the range/di stance to the object, which provides the extra dimension of data. Therefore, a 2D scanning by a LiDAR system can generate a 3D point cloud for mapping the external environment of the LiDAR system.
  • the density of a point cloud refers to the number of measurements (data points) per area performed by the LiDAR system.
  • a point cloud density relates to the LiDAR scanning resolution. Typically, a larger point cloud density, and therefore a higher resolution, is desired at least for the region of interest (ROI).
  • the density of points in a point cloud or image generated by a LiDAR system is equal to the number of pulses divided by the field of view. In some embodiments, the field of view can be fixed. Therefore, to increase the density of points generated by one set of transmission-receiving optics (or transceiver optics), the LiDAR system may need to generate a pulse more frequently. In other words, a light source in the LiDAR system may have a higher pulse repetition rate (PRR).
  • PRR pulse repetition rate
  • the farthest distance that the LiDAR system can detect may be limited. For example, if a return signal from a distant object is received after the system transmits the next pulse, the return signals may be detected in a different order than the order in which the corresponding signals are transmitted, thereby causing ambiguity if the system cannot correctly correlate the return signals with the transmitted signals.
  • the farthest distance the LiDAR system can detect may be 300 meters and 150 meters for 500 kHz and 1 MHz, respectively.
  • the density of points of a LiDAR system with 500 kHz repetition rate is half of that with 1 MHz.
  • Optical and/or signal processing techniques are also used to correlate between transmitted and return light signals.
  • Various systems, apparatus, and methods described herein may be implemented using digital circuitry, or using one or more computers using well-known computer processors, memory units, storage devices, computer software, and other components.
  • a computer includes a processor for executing instructions and one or more memories for storing instructions and data.
  • a computer may also include, or be coupled to, one or more mass storage devices, such as one or more magnetic disks, internal hard disks and removable disks, magneto- optical disks, optical disks, etc.
  • Various systems, apparatus, and methods described herein may be implemented using computers operating in a client-server relationship.
  • the client computers are located remotely from the server computers and interact via a network.
  • the client- server relationship may be defined and controlled by computer programs running on the respective client and server computers.
  • client computers can include desktop computers, workstations, portable computers, cellular smartphones, tablets, or other types of computing devices.
  • FIGS. 1-10 may be implemented using a computer program product tangibly embodied in an information carrier, e.g., in a non-transitory machine-readable storage device, for execution by a programmable processor; and the method processes and steps described herein, including one or more of the steps of at least some of the FIGS. 1-10, may be implemented using one or more computer programs that are executable by such a processor.
  • a computer program is a set of computer program instructions that can be used, directly or indirectly, in a computer to perform a certain activity or bring about a certain result.
  • a computer program can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand- alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment.
  • Apparatus 700 comprises a processor 710 operatively coupled to a persistent storage device 720 and a main memory device 730.
  • Processor 710 controls the overall operation of apparatus 700 by executing computer program instructions that define such operations.
  • the computer program instructions may be stored in persistent storage device 720, or other computer-readable medium, and loaded into main memory device 730 when execution of the computer program instructions is desired.
  • processor 710 may be used to implement one or more components and systems described herein, such as control circuitry 450 (shown in FIG. 4), vehicle perception and planning system 320 (shown in FIG. 3), and vehicle control system 380 (shown in FIG. 3).
  • the method steps of at least some of FIGS. 1-10 can be defined by the computer program instructions stored in main memory device 730 and/or persistent storage device 720 and controlled by processor 710 executing the computer program instructions.
  • the computer program instructions can be implemented as computer executable code programmed by one skilled in the art to perform an algorithm defined by the method steps discussed herein in connection with at least some of FIGS. 1-10.
  • the processor 710 executes an algorithm defined by the method steps of these aforementioned figures.
  • Apparatus 700 also includes one or more network interfaces 780 for communicating with other devices via a network.
  • Apparatus 700 may also include one or more input/output devices 790 that enable user interaction with apparatus 700 (e.g., display, keyboard, mouse, speakers, buttons, etc.).
  • Processor 710 may include both general and special purpose microprocessors and may be the sole processor or one of multiple processors of apparatus 700.
  • Processor 710 may comprise one or more central processing units (CPUs), and one or more graphics processing units (GPUs), which, for example, may work separately from and/or multi-task with one or more CPUs to accelerate processing, e.g., for various image processing applications described herein.
  • processors 710, persistent storage device 720, and/or main memory device 730 may include, be supplemented by, or incorporated in, one or more application-specific integrated circuits (ASICs) and/or one or more field programmable gate arrays (FPGAs).
  • ASICs application-specific integrated circuits
  • FPGAs field programmable gate arrays
  • Persistent storage device 720 and main memory device 730 each comprise a tangible non-transitory computer readable storage medium.
  • Persistent storage device 720, and main memory device 730 may each include high-speed random access memory, such as dynamic random access memory (DRAM), static random access memory (SRAM), double data rate synchronous dynamic random access memory (DDR RAM), or other random access solid state memory devices, and may include non-volatile memory, such as one or more magnetic disk storage devices such as internal hard disks and removable disks, magneto-optical disk storage devices, optical disk storage devices, flash memory devices, semiconductor memory devices, such as erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM), digital versatile disc read-only memory (DVD-ROM) disks, or other non-volatile solid state storage devices.
  • DRAM dynamic random access memory
  • SRAM static random access memory
  • DDR RAM double data rate synchronous dynamic random access memory
  • Input/output devices 790 may include peripherals, such as a printer, scanner, display screen, etc.
  • input/output devices 790 may include a display device such as a cathode ray tube (CRT), plasma or liquid crystal display (LCD) monitor for displaying information to a user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to apparatus 700.
  • a display device such as a cathode ray tube (CRT), plasma or liquid crystal display (LCD) monitor for displaying information to a user, a keyboard, and a pointing device such as a mouse or a trackball by which the user can provide input to apparatus 700.
  • CTR cathode ray tube
  • LCD liquid crystal display
  • LiDAR system 400 and/or apparatus 700 may utilize one or more neural networks or other deep-learning techniques performed by processor 710 or other systems or apparatuses discussed herein.
  • FIG. 7 is a high-level representation of some of the components of such a computer for illustrative purposes.
  • FIG. 8 is a block diagram of an example TCSPC system 800 (“system 800”) that may be used as part of a TCSPC implementation, according to one or more embodiments of the present disclosure.
  • the system 800 may be implemented in any suitable system that may use TCSPC.
  • the system 800 may be used with a LiDAR system in some embodiments.
  • the system 800 may be implemented in a LiDAR system, such as LiDAR systems 210 and 220A-220I of FIG. 2, the LiDAR system 310 of FIG. 3, the LiDAR system 400 of FIG. 4, and/or any other appropriate LiDAR system.
  • the system 800 is not limited to being used with only LiDAR systems.
  • one or more elements of the system 800 may be implemented using a computing apparatus, such as the apparatus 700 of FIG. 7.
  • the system 800 may include an analog counting circuit 802 (“counting circuit 802”).
  • the counting circuit 802 may be configured to generate an analog output signal 808 (also referred to as an “analog output 808”) that is based on a number of return pulses that are detected within a time window of a detection period corresponding to an emitted light pulse.
  • the counting circuit 802 may include a photodetector set configured to generate the analog output 808 based on a number of photodetectors of the photodetector set that are activated within a same time window during the detection period.
  • the activation of a particular photodetector may correspond to that photodetector detecting a return pulse (e.g., a return photon) that corresponds to the emitted light pulse.
  • the activation of multiple photodetectors within the same time window may correspond to multiple photodetectors each detecting a return pulse within that time window.
  • the counting circuit 802 may be configured to accumulate, into the analog output 808, the individual output signals of the photodetectors that are generated at any given time by the activation of the photodetectors.
  • the analog output 808 for a given point in time may be based on the number of photodetectors that are activated within the particular time window, which may also indicate a count for the number of return pulses detected within the particular time window.
  • the amplitude of the analog output 808 for a particular time window may be greater in instances in which three photodetectors are activated than in instances in which only one photodetector is activated due to the accumulation being of three individual output signals rather than just one. Therefore, the amplitude of the analog output 808 during a given time window may indicate the number of return pulses that may be detected for the given time window.
  • the counting circuit 802 may be implemented in any suitable manner in which individual photodetector output signals may be aggregated into the analog output 808.
  • FIG. 9 illustrates an example of the counting circuit 802, according to one or more embodiments of the present disclosure.
  • the system 800 may also include an Analog-to-Digital Converter 804 (“ADC 804”).
  • ADC 804 may be configured to receive the analog output 808 and generate a digital output signal 810 (also referred to as “digital output 810”) based on the analog output 808.
  • the digital output 810 may include a value that is based on the number of activated photodetectors that correspond to the analog output 808.
  • the analog output 808 may have a first amplitude that may be represented by the digital output 810 having a binary value of “00”. Additionally or alternatively, in instances in which the analog output 808 corresponds to one activated photodetector, the analog output 808 may have a second amplitude that may be represented by the digital output 810 having a binary value of “01”. In these and other embodiments, in instances in which the analog output 808 corresponds to two activated photodetectors, the analog output 808 may have a third amplitude that may be represented by the digital output 810 having a binary value of “10”. Further, in instances in which the analog output 808 corresponds to three activated photodetectors, the analog output 808 may have a fourth amplitude that may be represented by the digital output 810 having a binary value of “11”.
  • the output size of the ADC (e.g., the number of bits output by the ADC) and the number of photodetectors included in the photodetector set may be based on each other.
  • the number photodetectors that may be indicated as being activated from the analog output 808 may be limited by the number of output bits of the ADC.
  • the greatest number of activated photodetectors that may be individually counted may be based on the largest number that may be represented by the output bits of the ADC. Therefore, in some embodiments, for “N” number of output bits of the ADC, the number of photodetectors that may be included in the photodetector set may be less than or equal to “2 N 1 ”
  • the number of photodetectors included in the photodetector set may be equal to “2 N — 1 ”
  • the number of photodetectors included in the photodetector set may be three.
  • the number of photodetectors included in the photodetector set may be seven.
  • the number of photodetectors included in the photodetector set may be fifteen.
  • the size of the ADC 804 and the number of photodetectors may be based on specific implementation considerations and/or design tradeoffs. For example, a larger ADC 804 and a correspondingly larger photodetector set may allow for larger photon counts for a particular time window, which may result in being able to construct a resulting histogram in less time. However, the larger ADC 804 and photodetector set may use more resources (e.g., silicon space and/or memory) and/or may not be able to process incoming analog outputs 808 as quickly.
  • resources e.g., silicon space and/or memory
  • a smaller ADC 804 and a correspondingly smaller photodetector set may allow for smaller photon counts for a particular time window but may use fewer resources (e.g., silicon space and/or memory) and/or may be able to process incoming analog outputs 808 more quickly.
  • the digital output 810 may be received by a signal processing circuit 806 (“processing circuit 806”) in some embodiments.
  • the processing circuit 806 may be implemented using hardware including one or more processors, CPUs graphics processing units (GPUs), data processing units (DPUs), parallel processing units (PPUs), microprocessors, field-programmable gate arrays (FPGA), application-specific integrated circuits (ASICs), accelerators (e.g., deep learning accelerators (DLAs)), one or more programmable vision accelerators (PVAs), which may include one or more vector processing units (VPUs), one or more direct memory access (DMA) systems, one or more pixel processing engines (PPEs), etc., and/or other processor types.
  • the processing circuit 806 may be implemented using a combination of hardware and software.
  • the processing circuit 806 may be configured to process the digital output 810 to generate a histogram of the return pulses.
  • the histogram may be based on a sampling period that includes multiple digital outputs 810.
  • the digital outputs 810 may respectively correspond to individual analog outputs 808 of multiple analog outputs 808.
  • the individual analog outputs may each correspond to individual detection periods of multiple detection periods.
  • the detection periods may respectively correspond to individual emitted light pulses of multiple emitted light pulses that are transmitted during the sampling period.
  • the detection period may correspond to the amount of time allocated for detection of return pulses for any given emitted light pulse.
  • the detection period may correspond to the amount of time between emission of light pulses.
  • the processing circuit 806 may be configured to determine, from the digital output 810, the counts of return pulses for the different time windows to generate the histogram. In these and other embodiments, the processing circuit 806 may use fewer resources than traditional signal processing circuits used for TCSPC applications due to at least part of the return pulse counting being already indicated from the digital output 810.
  • the system 800 may be made to the system 800 without departing from the scope of the present disclosure.
  • the number of photodetectors included in the counting circuit 802 may vary.
  • the size of the ADC 804 may vary.
  • the specific implementations of the counting circuit 802 and/or the processing circuit 806 may vary.
  • the system 800 as illustrated does not include every element that may be included in a TCSPC system implementation.
  • FIG. 9 is a circuit diagram illustrating an example photodetector system 900 (“system 900”) according to one or more embodiments of the present disclosure.
  • the system 900 may be part of and/or implemented with a TCSPC system in some embodiments. Further, one or more of the elements of the system 900 may be examples of one or more elements of the system 800.
  • the system 900 may include an analog counting circuit 902 (“counting circuit 902”).
  • the counting circuit 902 may be an example of the counting circuit 802 of FIG. 8.
  • the counting circuit 902 may include a photodetector set 912 in some embodiments.
  • the photodetector set 912 may include a set of multiple photodetectors 932.
  • the photodetector set may include a first photodetector 932a, a second photodetector 932b, and a third photodetector 932c.
  • the number of photodetectors 932 included in the photodetector set 912 may vary.
  • the number of photodetectors 932 may be based on one or more factors such as discussed with respect to the counting circuit 802 of FIG. 8.
  • the photodetectors 932 may include light detector structures (also referred to as photodetectors”, “optical detectors” “light detectors” or “detectors”) that may be configured to detect light signals (e.g., return pulses such as discussed in the present disclosure).
  • the photodetectors may include any suitable device and/or structure that may be configured to detect light and generate an electrical signal based on such detected light.
  • one or more of the photodetectors 832 may include an APD (avalanche photodiode) based structure, a PMT (photomultiplier tube) based structure, a SiPM (Silicon photomultiplier) based structure, a SPAD (single-photon avalanche diode) based structure, and/or quantum wires.
  • APD active photodiode
  • PMT photomultiplier tube
  • SiPM Silicon photomultiplier
  • SPAD single-photon avalanche diode
  • the photodetector set 912 may be configured such that an accumulated analog output signal “Xacc” at an analog output node 908 may be based on the number of photodetectors 932 of the photodetector set 912 are activated at a time.
  • each photodetector 932 may be configured to generate a discrete analog output signal in response to being activated.
  • the photodetectors 932 may be arranged in a parallel array such as illustrated in FIG. 9 such that the amplitude of the accumulated analog output at the analog output node 908 increases by the discrete outputs based on the number of photodetectors 932 that are activated.
  • the first photodetector 932a may have a first output having a first discrete amplitude “Al”
  • the second photodetector 932b may have a second output having a second discrete amplitude “A2”
  • the third photodetector 932c may have a third output having a third discrete amplitude “A3.”
  • Activation of only the first photodetector 932a may result in the amplitude of Xacc being “Al”
  • activation of the first photodetector 932a and the second photodetector 932b at the same time may result in the amplitude of Xacc being “A1+A2”
  • activation of the first photodetector 932a and the third photodetector 932c at the same time may result in the amplitude of Xacc being “A1+A3”
  • activation of the second photodetector 932b and the third photodetector 932c at the same time may result
  • the counting circuit 902 may include a voltage source 922 and a resistor set 914 coupled to the photodetector set 912.
  • the voltage source 922 and the resistor set 914 may be coupled to the photodetector set 912 to help obtain the accumulated output Xacc as discussed.
  • the voltage source 922 may be configured to generate a source voltage. Further, the voltage source 922 may be coupled to a bias node 930 of the counting circuit 902 such that a bias voltage at the bias node 930 is based on the source voltage. For instance, in some embodiments, the bias voltage may be same as or substantially the same as the source voltage. The source voltage may be any suitable voltage to obtain a target bias voltage.
  • the photodetectors 932 may be coupled to the bias node 930 such that the photodetectors 932 may be biased based on the bias voltage.
  • the photodetectors 932 may each include a cathode that is coupled to the bias node 930 in parallel with each other as illustrated in FIG. 9.
  • the biasing of the photodetectors 932 may be based on the respective voltages between the respective cathodes and anodes of the photodetectors 932 such that the bias voltage at the cathodes of the photodetectors may affect the biasing of the photodetectors.
  • the gain of an individual photodetector 932 may correlate to its bias voltage, such that the discrete output signals (e.g., amplitudes) of the respective photodetectors 932 may be based on the bias voltage that may be provided by the voltage source 922.
  • the photodetectors 932 may individually be configured to activate in response to detecting light (e.g., in response to detecting a return pulse).
  • the amount of light that may be required to cause the photodetectors 932 to activate may be based on their respective bias voltages, such that the respective sensitivities and/or dynamic ranges of the photodetectors 932 may be based on the bias voltage at the bias node 930.
  • the source voltage of the voltage source 922 may be adjusted based on a target sensitivity and/or dynamic range of the photodetectors 932.
  • the counting circuit 902 may include a first switch 924 coupled between the bias node 930 and the voltage source 922.
  • the first switch 924 may be switched on and off to control the biasing of the photodetectors 932.
  • the photodetectors 932 may be biased according to the source voltage of the voltage source 922.
  • the photodetectors 932 may not be biased according to the source voltage of the voltage source 922 due to the bias node 930 (and accordingly the photodetectors 932) being disconnected from the voltage source 922.
  • the first switch 924 may be switched on during a particular sampling period that may be used to generate a particular histogram. In these and other embodiments, the first switch 924 may be switched on during an entire sampling period. Additionally or alternatively, the first switch 924 maybe switched off between two or more detection periods within the same sampling period. In these and other embodiments, the first switch 924 may be switched on between two or more sampling periods or may be switched off between two or more sampling periods. The particular switching scheme of the first switch 924 may vary depending on particular implementations and/or applications.
  • the resistor set 914 may include multiple detector resistors 934 that may each correspond to one of the photodetectors 932.
  • the resistor set 914 may include a first detector resistor 934a that corresponds to the first photodetector 932a, a second detector resistor 934b that corresponds to the second photodetector 932b, and a third detector resistor 934c that corresponds to the third photodetector 932c.
  • the detector resistors 934 may be coupled in parallel with each other (e.g., in an array) to the analog output node 908, for example as illustrated in FIG. 9. Further, each detector resistor 934 may be coupled between its corresponding photodetector 932 and the analog output node 908, for example as illustrated in FIG. 9. The coupling of the detector resistors 934 with respect to each other and with respect to the photodetectors 932 may be such that the individual output signals that produced by the photodetectors 932 may be accumulated at the analog output node 908.
  • the sizes of the detector resistors 934 may affect the individual output signals that may be produced by their corresponding photodetectors 932. For example, a larger detector resistor 934 may cause less current to run through such detector resistor 934 than a smaller detector resistor 934 such that the amplitude of a corresponding individual output signal provided at the analog output node 908 may be based on the resistor size.
  • the resistors 934 may be individually sized such that the individual output signals of the different photodetectors 932 may be relatively the same for the same or similar light detections by the individual photodetectors 932.
  • the resistors 934 may be individually sized such that the current running through them may be relatively the same for the same or similar light detections by the individual photodetectors 932.
  • the counting circuit 902 may include an RC circuit 916.
  • the RC circuit 916 may be configured to store the voltage at the analog output node 908 for a particular amount of time.
  • the RC circuit 916 may be configured to store the voltage at the analog output node 908 for an amount of time that corresponds to the time windows used to generate the histogram.
  • the accumulated analog output voltage of the accumulated analog output Xacc may correspond to all of the photodetectors 932 that are activated during the particular time window due to the individual output signals of the individually activated photodetectors being stored and accumulated by the RC circuit 916.
  • the RC circuit 916 may include a capacitor 920 coupled between the analog output node 908 and ground. Further, the RC circuit 916 may include a load resistor 918 coupled between the analog output node 908 and ground.
  • the load resistor 918 may be coupled with respect to the capacitor 920 such that the capacitor 920 discharges through the load resistor 918.
  • the resistor 918 may be coupled in parallel with the capacitor 920 as illustrated in FIG. 9. Additionally or alternatively, the load resistor 918 may be coupled in series with the capacitor 920 between the capacitor and ground.
  • the capacitor 920 and the resistor 918 may be sized such that the time constant of the RC circuit 916 (which may correspond to the capacitor discharge rate) may allow for the capacitor 920 to adequately charge and discharge within each respective detection period and/or time window such that the accumulated analog output for each detection period and/or time window may be discretely separated.
  • the RC circuit 916 may include a second switch 926 coupled between the analog output node 908 and ground.
  • the second switch 926 may be coupled between the analog output node 908 and the load resistor 918. Additionally or alternatively, the second switch 926 may be coupled between the load resistor 918 and ground.
  • the second switch 926 may be switched on and off to control the discharging of the capacitor 920. For example, in instances in which the second switch 926 is switched off, the capacitor 920 may charge based on the voltage at the analog output node 908. Additionally or alternatively, in instances in which the second switch 926 is switched on, the capacitor 920 may discharge. In some embodiments, the second switch 926 may be switched on during at least one detection period of a sampling period such that the capacitor 920 may continuously discharge during the detection period. Additionally or alternatively, the second switch 926 may be switched on during an entire sampling period such that the capacitor may continuously discharge during the entire sampling period.
  • the second switch 926 may be switched off during an entire detection period but may be switched on between two or more detection periods within the same sampling period. Additionally or alternatively, the second switch 926 maybe switched off during individual time windows of the detection period and then switched on between time windows.
  • the particular switching scheme of the second switch 926 may vary depending on particular implementations, applications, resistor size, and/or capacitor size.
  • the counting circuit 902 may include an amplifier circuit 936 (“amplifier 936”) and an ADC 904.
  • the amplifier 936 may be coupled between the analog output node 908 and the ADC 904.
  • the amplifier 936 may include any suitable device, circuit, etc. that may be configured to amplify the accumulated analog output Xacc to generate an amplified accumulated analog output.
  • reference to the “accumulated analog output” or the “analog output of the photodetector set 912” may refer to the analog output at the analog output node 908 and/or the amplified analog output that may be output by the amplifier 936.
  • the amplifier 936 include an input coupled to the analog output node 908 and an output coupled to an input of the ADC 904.
  • the amplifier 936 may be configured to amplify the accumulated analog output Xacc, which may be provided to the input of the ADC 904.
  • the ADC 904 may be analogous to the ADC 804 of FIG. 8.
  • the ADC 904 may be configured to convert the amplified accumulated analog output and convert such output into a digital signal, such as described with respect to FIG. 8.
  • the ADC 904 may be sized (e.g., have a particular number of output bits) in relation to the number of photodetectors 932 included in the photodetector set 912, such as described with respect to FIG. 8.
  • the ADC 904 may be a 2 -bit ADC in relation to the number of photodetectors 932 being three.
  • the ADC 904 may have a particular dynamic range.
  • the source voltage of the voltage source 922 may be such that the bias voltage of the photodetectors 932 may be established to match the dynamic range of the photodetectors 932 with that of the ADC 904.
  • the ADC 904 may have a particular reference voltage (Vref) and/or detection voltage range.
  • the amplifier 936 be calibrated to convert the voltage range of the accumulated analog output Xacc to match approximately to the reference voltage or detection voltage range of the ADC 904.
  • the reference voltage of the ADC 904 may be provided at a reference voltage input 940 of the ADC 904.
  • a reference voltage provided at the reference voltage input 940 may be determined based on the bias applied to the photodetectors 932.
  • the reference voltage at the reference voltage input 940 may be proportional to the amount of the bias voltage.
  • the reference voltage at the reference voltage input 940 may be the amount that the bias voltage is over the threshold voltage of the photodetectors 932.
  • the system 900 may be configured such that any return pulses (e.g., photons) detected by the individual photodetectors 932 may activate the detecting photodetectors 932.
  • the activated photodetectors 932 may cause a current to pass through a corresponding resistor 934 to the analog output node 908.
  • the accumulated current may charge the capacitor 920.
  • a voltage at the analog output node 908 may increase also proportionally to the number of actively detecting photodetectors 932.
  • the voltage signal at the analog output node 908 (as dictated by the voltage stored by the capacitor 920) may be amplified by the amplifier 936 and then converted to a digital output by the ADC 904, which may indicate the number of photodetectors 932 activated.
  • the capacitor 920 may be discharged in a manner such that the accumulated analog output may be identified for particular time windows within a detection period such that the digital output may accordingly indicate the number of photodetectors activated during discrete time windows.
  • Such digital output may then be provided to a processing circuit (e.g., the processing circuit 806 of FIG. 8) to generate a histogram of the return pulses as discussed in the present disclosure.
  • the system 900 may be made to the system 900 without departing from the scope of the present disclosure.
  • the number of photodetectors included in the counting circuit 902 may vary.
  • the size of the ADC 904 may vary.
  • the specific implementations of the counting circuit 902 may vary.
  • the system 900 as illustrated does not include every element that may be included in a TCSPC system implementation.
  • the system 800 and/or the system 900 may be implemented in any appropriate LiDAR system, such as those described in the present disclosure.
  • the LiDAR system may include the systems 700 or 800 may be included in a vehicle.
  • FIG. 10 is a flow diagram of a method 1000 for performing TCSPC, according to one or more embodiments of the present disclosure.
  • the method 1000 may be performed by any suitable system or device.
  • one or more operations of the method 1000 may be performed by one or more elements of the system 800 and/or 900 of FIGS. 8 and 9.
  • the method 1000 may include a block 1002.
  • an analog output may be generated.
  • the analog output may be an accumulated analog output that may be based on a number of photodetectors of a photodetector set that are activated during a detection period corresponding to an emitted light pulse.
  • the generation of the analog output may be accomplished in any manner described above with respect to FIG. 8 and/or FIG 9.
  • the method 1000 may also include a block 1004.
  • a digital output may be generated based on the analog output.
  • the digital output may be generated in any manner described above with respect to FIG. 8 and/or FIG. 9.
  • the method 1000 may also include a block 1006.
  • a histogram of one or more return light pulses may be generated based on the digital output.
  • the histogram may be generated in any manner described above with respect to FIG. 8 and/or FIG. 9.
  • Example 1 A system comprising: a photodetector set comprising a plurality of photodetectors, the photodetector set being configured to generate an analog output that is based on a number of photodetectors of the photodetector set that are activated during a detection period corresponding to an emitted light pulse; an analog-to-digital converter (ADC) configured to generate a digital output based on the analog output; and a signal processing circuit configured to generate a histogram of one or more return light pulses based on the digital output.
  • ADC analog-to-digital converter
  • Example 2 The system of Example 1, wherein the histogram is based on a sampling period that includes a plurality of digital outputs, the plurality of digital outputs respectively corresponding to individual analog outputs of a plurality of analog outputs, the individual analog outputs each corresponding to individual detection periods of a plurality of detection periods respectively corresponding to individual emitted light pulses of a plurality of emitted light pulses that are transmitted during the sampling period.
  • Example 3 The system of any of Example 1 or Example 2, wherein the number of photodetectors that are activated is based on a number of return pulses of the transmitted light signal received within a same time window within the detection period.
  • Example 4 The system of any of Examples 1-3, further comprising a voltage source configured to generate a source voltage and coupled to each photodetector of the photodetector set such that each photodetector is biased based on the source voltage.
  • Example 5 The system of Example 4, wherein: the voltage source is coupled to a bias node such that a bias voltage at the bias node is based on the source voltage; and the photodetectors of the photodetector set are respectively coupled in parallel with each other with respect to the bias node.
  • Example 6 The system of Example 5, wherein: each photodetector of the photodetector set includes a respective cathode; and each respective cathode is coupled to the bias node.
  • Example 7 The system of any of Example 5 or Example 6, further comprising a first switch coupled between the voltage source and the bias node.
  • Example 8 The system of Example 7, wherein the first switch is configured to be switched on during the sampling period.
  • Example 9 The system of any of Example 7 or Example 8, wherein the first switch is configured to be switched off between two or more detection periods within the sampling period.
  • Example 10 The system of any of Examples 7-9, wherein the first switch is configured to be switched on between two or more detection periods within the sampling period.
  • Example 11 The system of any of Examples 7-10, wherein the first switch is configured to be switched off between the sampling period and a subsequent sampling period.
  • Example 12 The system of any of Examples 7-10, wherein the first switch is configured to be switched on between the sampling period and a subsequent sampling period.
  • Example 13 The system of any of Examples 5-12, further comprising: an analog output node corresponding to the analog output; and a resistor set comprising a plurality of detector resistors, wherein: each photodetector of the photodetector set is respectively coupled between the bias node and a respective detector resistor of the resistor set; and each detector resistor of the resistor set is coupled between its corresponding photodetector and the analog output node.
  • Example 14 The system of Example 13, further comprising a capacitor coupled between the analog output node and ground.
  • Example 15 The system of Example 14, further comprising a load resistor coupled between the analog output node and ground in parallel with the capacitor.
  • Example 16 The system of any of Example 13 or Example 14, wherein one or more of the load resistor or the capacitor is sized based on a timing frequency of the plurality of transmitted light pulses.
  • Example 17 The system of any of Example 15 or Example 16, further comprising a second switch coupled to the load resistor between the analog output node and ground.
  • Example 18 The system of Example 17, wherein the second switch is configured to be switched on between two or more detection periods of the plurality of detection periods.
  • Example 19 The system of any of Example 17 or Example 18, wherein the second switch is configured to be switched off during at least one detection period of the plurality of detection periods.
  • Example 20 The system of any of Examples 17- 19, wherein the second switch is configured to be switched on during at least one detection period of the plurality of detection periods.
  • Example 21 The system of any of Examples 1-20, further comprising an amplifier coupled between the analog output node and the ADC.
  • Example 22 The system of any of Examples 1-21, wherein an output size of the ADC and a number of photodetectors included in the photodetector set are based on each other.
  • Example 23 The system of any of Examples 1-22, wherein the output size of the ADC is “N” number of bits such that the digital output is “N” number of bits and such that "2 N — 1" is greater than or equal to the number of photodetectors included in the photodetector set.
  • Example 24 The system of any of Examples 1-23, wherein a reference voltage of the ADC is determined based on a bias of one or more photodetectors of the photodetector set.
  • Example 25 The system of any of Examples 1-24, wherein each photodetector of the photodetector set generates a discrete level of the analog output such that the ADC generates the digital output representing the number of photodetectors that are activated during the sampling period.
  • Example 26 A LiDAR system comprising the system of any of Examples 1-25.
  • Example 27 A vehicle comprising a LiDAR system comprising the system of any of
  • Example 28 A method comprising: generating an analog output that is based on a number of photodetectors of a photodetector set that are activated during a detection period corresponding to an emitted light pulse; generating a digital output based on the analog output; and generating a histogram of one or more return light pulses based on the digital output.
  • Example 29 The method of Example 28, wherein the histogram is based on a sampling period that includes a plurality of digital outputs, the plurality of digital outputs respectively corresponding to individual analog outputs of a plurality of analog outputs, the individual analog outputs each corresponding to individual detection periods of a plurality of detection periods respectively corresponding to individual emitted light pulses of a plurality of emitted light pulses that are transmitted during the sampling period.
  • Example 30 The method of any of Example 28 or Example 29, wherein the number of photodetectors that are activated is based on a number of return pulses of the transmitted light signal received within a same time window within the detection period.
  • Example 31 The method of any of Examples 28-30, wherein the method is implemented using the system of any of Examples 1-27.

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  • Engineering & Computer Science (AREA)
  • Computer Networks & Wireless Communication (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Optical Radar Systems And Details Thereof (AREA)

Abstract

Un système peut comprendre un ensemble de photodétecteurs composé de plusieurs photodétecteurs. L'ensemble de photodétecteurs peut être configuré pour générer une sortie analogique qui est basée sur un nombre de photodétecteurs de l'ensemble de photodétecteurs qui sont activés pendant une période de détection correspondant à un signal lumineux transmis. Le système peut également comprendre un convertisseur analogique-numérique (CAN) configuré pour générer une sortie numérique sur la base de la sortie analogique. En outre, le système peut comprendre un circuit de traitement de signal configuré pour générer un histogramme d'une ou de plusieurs impulsions de retour sur la base de la sortie numérique.
PCT/US2024/049471 2023-10-04 2024-10-01 Circuit de photodiode à discrimination multiniveau Pending WO2025221290A1 (fr)

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Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060261996A1 (en) * 2002-07-31 2006-11-23 Augusto Carlos J Multi-mode ADC and its application to CMOS image sensors
US20180299552A1 (en) * 2017-03-01 2018-10-18 Ouster, Inc. Accurate photo detector measurements for lidar
US20220057496A1 (en) * 2020-08-21 2022-02-24 Argo AI, LLC Systems and methods for intra-shot dynamic adjustment of lidar detector gain
US20230194676A1 (en) * 2021-12-17 2023-06-22 Waymo Llc Two-Step Return Calibration for Lidar Cross-Talk Mitigation

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060261996A1 (en) * 2002-07-31 2006-11-23 Augusto Carlos J Multi-mode ADC and its application to CMOS image sensors
US20180299552A1 (en) * 2017-03-01 2018-10-18 Ouster, Inc. Accurate photo detector measurements for lidar
US20220057496A1 (en) * 2020-08-21 2022-02-24 Argo AI, LLC Systems and methods for intra-shot dynamic adjustment of lidar detector gain
US20230194676A1 (en) * 2021-12-17 2023-06-22 Waymo Llc Two-Step Return Calibration for Lidar Cross-Talk Mitigation

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