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EP3811351A1 - Adaptation de la trajectoire d'un égo-véhicule à des objets étrangers en mouvement - Google Patents

Adaptation de la trajectoire d'un égo-véhicule à des objets étrangers en mouvement

Info

Publication number
EP3811351A1
EP3811351A1 EP19727327.9A EP19727327A EP3811351A1 EP 3811351 A1 EP3811351 A1 EP 3811351A1 EP 19727327 A EP19727327 A EP 19727327A EP 3811351 A1 EP3811351 A1 EP 3811351A1
Authority
EP
European Patent Office
Prior art keywords
ego vehicle
foreign objects
movement
determined
vehicle
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
EP19727327.9A
Other languages
German (de)
English (en)
Inventor
Seyed Jalal ETESAMI
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.)
Robert Bosch GmbH
Original Assignee
Robert Bosch GmbH
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 Robert Bosch GmbH filed Critical Robert Bosch GmbH
Publication of EP3811351A1 publication Critical patent/EP3811351A1/fr
Pending legal-status Critical Current

Links

Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/08—Active safety systems predicting or avoiding probable or impending collision or attempting to minimise its consequences
    • B60W30/095—Predicting travel path or likelihood of collision
    • B60W30/0956—Predicting travel path or likelihood of collision the prediction being responsive to traffic or environmental parameters
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W60/00—Drive control systems specially adapted for autonomous road vehicles
    • B60W60/001—Planning or execution of driving tasks
    • B60W60/0011—Planning or execution of driving tasks involving control alternatives for a single driving scenario, e.g. planning several paths to avoid obstacles
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2415—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on parametric or probabilistic models, e.g. based on likelihood ratio or false acceptance rate versus a false rejection rate
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G06V20/58—Recognition of moving objects or obstacles, e.g. vehicles or pedestrians; Recognition of traffic objects, e.g. traffic signs, traffic lights or roads
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/40—Dynamic objects, e.g. animals, windblown objects
    • B60W2554/404—Characteristics
    • B60W2554/4049—Relationship among other objects, e.g. converging dynamic objects
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/20—Data confidence level
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W2556/00—Input parameters relating to data
    • B60W2556/45—External transmission of data to or from the vehicle
    • B60W2556/65—Data transmitted between vehicles
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N20/00—Machine learning

Definitions

  • the present invention relates to trajectory planning for the at least partially automated method, in particular in mixed traffic with human-controlled foreign objects.
  • Vehicles that are at least partially automated in traffic will not suddenly replace human-controlled vehicles and will not be isolated from human-controlled traffic on separate routes. Rather, these vehicles will have to move safely in mixed traffic with human-controlled foreign objects, whereby these foreign objects also include pedestrians as weaker road users. In the case of human-controlled foreign objects, there is always an uncertainty as to which movement action these foreign objects will carry out next.
  • a control system for at least partially automated driving is therefore dependent on at least partially tapping the future behavior of foreign objects by observing the previous behavior.
  • WO 2017/197 170 A1 discloses a control unit for a moving autonomous unit, which can be a robot or a vehicle.
  • the control unit first determines a basic trajectory with which the primary goal of the autonomous unit, such as a destination, is tracked.
  • the basic trajectory is then modified by a security module such that a collision with people or other human-controlled units is avoided.
  • the ego vehicle is the vehicle whose trajectory is to be acted on in order to avoid a collision with the foreign objects.
  • the foreign objects can in particular be people or vehicles controlled by people, such as conventional motor vehicles or bicycles. However, foreign objects that cannot be controlled or can only be controlled to a limited extent are also considered, such as a vehicle that rolls away after being parked on a slope or a trailer that has torn itself away from its towing vehicle.
  • the foreign objects are initially identified.
  • a time series of physical observations of the environment can be used for this, such as a sequence of camera images or a sequence of
  • V2V vehicle-to-vehicle
  • V 21 vehicle-to-infra structure
  • identifying means at least recording which foreign objects in the environment of the ego vehicle can be moved independently of one another.
  • Movement is in progress. How this investigation is carried out in detail depends on the information available. For example, it can be extrapolated from the time course of the trajectory that certain short-range targets are more likely than others. The more additional information is used, the more accurate the prediction of the near target becomes. If, for example, it is recognized that a vehicle has set a turn signal as a foreign object, then a turning process is very likely planned. A vehicle as a foreign object can also, for example, announce its current short-range or long-range destination directly via V2V communication.
  • the basic rules according to which the movement of the foreign objects takes place can in particular include the rules of the road traffic regulations and also depend on the type of the foreign objects. For example
  • Vehicles use the lane and the right of two lanes.
  • Pedestrians are, for example, required to walk on sidewalks and, when crossing paths such as traffic lights or crosswalks, for crossing the
  • the basic rules can in particular include the rules of the road traffic regulations and need not be the same in all situations. For example, the permissible maximum speed is limited separately when the vehicle is pulling a trailer or driving with snow chains.
  • the determination of the basic rules can also include, for example, an analysis of the configuration of the ego vehicle.
  • a quality function R I-4 is set up both for the ego vehicle and for the foreign objects, which assigns a measure for an overall situation x formed from the current states of the ego vehicle and the foreign objects and a possible next movement action ai- 4 , how good the action ai- 4 is in the current overall situation x for the road user under consideration.
  • the quality function R I-4 can in particular, for example include a measure of the extent to which the movement action ai- 4 works in situation x to achieve the respective short-term goal and to comply with the rules.
  • the numerical indices ranging from 1 to 4 are not to be understood as restrictive with regard to the number of treatable foreign objects, but are merely illustrative in order to be able to explain the method using an example. In general, one can also speak of quality functions R, and next movement actions a.
  • states generally encompasses the quantities with which the contribution of the ego vehicle or the foreign objects to the traffic situation can be characterized.
  • the states can in particular be positions or time derivatives thereof, that is to say speeds and
  • a quality measure QI 4 is respectively positioned the x of the overall situation and the possible next move action ai 4 in addition to the value Ri 4 (x, ai 4) and the expected value E ( P (x ')) of a distribution of the probabilities P (x') of
  • the quality measure QI- 4 can be a weighted sum of the value Ri- 4 (x, ai- 4 ) of the quality function and the expected value E (P (x ')).
  • Those optimal movement strategies pi- 4 of the ego vehicle and the foreign objects that maximize the quality measures QI- 4 are determined .
  • the sought trajectories of the ego vehicle and the foreign objects are determined from the optimal movement strategies pi- 4 .
  • the concept of the movement strategy generally encompasses any function pi- 4 which assigns a numerical value ni- 4 (x, ai- 4 ) to an overall situation x and a next movement action ai- 4 .
  • the term is therefore generalized compared to the usual use of language, in which it is associated with deterministic rules.
  • a deterministic rule can, for example, specify that if a certain overall situation x is present, exactly one next Movement action ai- 4 is to be carried out by the ego vehicle or is carried out by the foreign objects.
  • the behavior of the foreign objects in particular does not always follow deterministic rules. If the foreign object is controlled by a human, for example, the control is intelligent, but does not necessarily lead to the movement action that is optimal for the pursuit of the respective near target. This applies even if a human driver basically chooses the correct driving maneuver. For example, turning left from a road on which no route is explicitly marked can scatter around the ideal driving line. The vehicle will also come to a stop at the stop line every time there are a large number of braking in front of a red traffic light, but the time course of the speed may vary. For example, the driver can step on the brake pedal harder and weaker at the beginning and later unconsciously readjust the brake pressure in order to come to a stop at the right place at the end. A deeper reason for this is that the
  • Driving task as a whole is too complex to be carried out fully consciously.
  • a learning driver In order to be able to manage multitasking at the required speed, a learning driver must first “automate” certain processes in the subconscious.
  • the deceleration of the ego vehicle can vary, for example, depending on the condition of the road and the temperature and water content of the brake fluid.
  • Movement strategies pi- 4 of all road users can also be probabilistic, the reaction of the ego vehicle to the overall situation x can thus be refined so that it is more likely to actually be traffic-friendly and in particular to avoid collisions.
  • the predictive driving that every human driver has to learn in the driving school is technically simulated so that a system for at least partially automated driving can cope with the driving task at least as well as a human driver.
  • quality measures Q 1-4 are chosen whose optima with respect to the movement strategies pi- 4 are given by the Bellman optimum. In a way, this is a combination of recursive definition and mutual coupling of the quality measures Q I-4 .
  • V * (x ') softmax Q * (x', a ').
  • E runs through the probabilistic state transitions and the strategies of the other road users whose index is different from i. It is given by
  • the optimal movement strategies pi- 4 are determined on the condition that they are independent of one another with the same history H 1 :
  • Equations (1) to (3) form a set of M coupled equations, where M is the number of road users considered.
  • the equations can be summarized as
  • Equation (4) has exactly one optimal solution Q *, which is available with the following algorithm:
  • the quality function Q of the i-th road user has the form in the fully optimized state at the time step te [t, t + T]
  • a feature function F I-4 is set up for the ego vehicle as well as for the foreign objects such that the application of F I-4 to a set of qi- 4 still free parameters is a quality function R I -4 supplies, said quality function R x I-4 an overall situation formed from the current states of the ego vehicle and the foreign objects and a possible next move action ai 4 assigns a measure of how well the action ai 4 in the current
  • the quality function R I-4 can in particular include, for example, a measure of the extent to which the movement action ai- 4 in situation x works towards the achievement of the respective short-term goal and compliance with the rules.
  • the feature function F I-4 can, for example, embody properties and destinations of the respective road user, such as the destination to which a pedestrian is moving, or his walking speed. At a In addition to the destination, the vehicle can, for example, include the requirement that the journey should be safe, smooth and comfortable in the feature function F I-4 .
  • the feature function F 1-4 can therefore in particular be composed, for example, of several parts which relate to different goals, wherein these goals can also be opposite.
  • the set qi- 4 of parameters can then embody, for example, the weights with which different goals and requirements are contained in the final quality function R I-4 .
  • the set qi- 4 of parameters can in particular be present, for example, as a vector of parameters and contain, for example, coefficients with which a linear combination of different targets contained in the feature function F I-4 enters the quality function R I-4 .
  • the movement strategies pi- 4 of the ego vehicle and the foreign objects are determined as those strategies which lead to a maximum causal entropy H (ai- 4
  • the trajectories sought are determined from the movement strategies pi- 4 .
  • the final result obtained has the same advantages as the result obtained according to the previously described method.
  • the advantage of this method in particular is that even less information about the respective road users is required for the determination of the parameter set qi- 4 than for the direct determination of the quality function R I-4 . Any additional information, regardless of the source, can be considered on the other side in the parameter set qi- 4 .
  • the free parameters qi 4 are pi- in the optimization in response to movement strategies 4 determined.
  • x) with respect to the movement strategies pi- 4 is advantageously determined under the boundary condition that both the ego vehicle and the foreign objects have the expected value of the respective feature function F I-4 over all possible overall situations x and all possible next movement actions ai- 4 are equal to the mean value of the feature functions F1-4 observed empirically in the previous trajectories. This mean can be empirical, especially across all
  • Motion strategies pi- 4 with the same history H l are independent of one another and that they are each statistically distributed around a strategy that maximizes the respective quality function RI- 4 can be used using
  • Wi T (H T , ai (x)) plays the role of the quality measure Q , and the
  • Quality functions R are composed of the characteristic functions F as a linear combination.
  • an “inverse reinforcement learning” can therefore be carried out from the perspective of the ego vehicle, ie, if the quality function Ri of the ego vehicle is known, only by observing the others
  • Algorithm 3 MMCE-IRL for the ego vehicle
  • the basic rules of motion may depend on the type of object.
  • the classification can be made on the basis of the physical observations and / or on the basis of the information received via the wireless interface.
  • the determination of the trajectory of the ego vehicle which is adapted to the presence of moving foreign objects, is not an end in itself, but aims to improve the suitability of at least partially automated vehicles, especially for mixed traffic with human-controlled foreign objects.
  • the invention therefore relates also to a method for controlling an ego vehicle in a
  • the trajectory of the ego vehicle which is adapted to the behavior of the foreign objects, is determined using one of the methods described above.
  • the adapted trajectory is transmitted to a movement planner of the ego vehicle.
  • a control program for a drive system, a steering system and / or a brake system of the ego vehicle is determined by the motion planner, the control program being designed to bring the actual behavior of the vehicle within the system limits as well as possible into agreement with the determined trajectory.
  • the drive system, steering system and / or braking system is controlled in accordance with the control program.
  • the method can be implemented in any existing control device of the ego vehicle, since, thanks to the internal networking via CAN bus, access to those recorded with a sensor system or obtained via the wireless interface is typically possible from anywhere in the vehicle
  • the motion planner can also be controlled from anywhere in the vehicle via the CAN bus.
  • the method can be implemented, for example, in the form of software that can be sold as an update or upgrade for such a control device and, in this respect, represents a separate product. Therefore, the invention also relates to a computer program with machine-readable
  • Control device to be executed, cause the computer and / or the control device to carry out a method provided by the invention.
  • the invention also relates to a machine-readable
  • FIG. 2 embodiment of the method 200
  • FIG. 3 embodiment of the method 300
  • Figure 4 Exemplary traffic scene with ego vehicle 1 and three
  • FIG. 1 shows an exemplary embodiment of the method 100.
  • step 110 a time series 11a-11c of physical observations of the surroundings 11 of the ego vehicle 1 (not shown in FIG. 1) is processed together with information 12a that was received via the wireless interface 12.
  • This information 12a comes from the foreign objects 2-4 in the vehicle environment 11 itself, and / or from an infrastructure 5.
  • step 110 the foreign objects 2-4 are identified, i.e. it is found that there are three foreign objects 2-4 that move in different ways.
  • Foreign objects 2-4 are classified in step 115 according to types 2d-4d.
  • step 120 the short-range targets 2b-4b aimed at by the foreign objects 2-4 are predicted, and the basic rules 2c-4c are determined according to which the movement of the foreign objects 2-4 takes place. Analogously to this, it is determined in step 130 to which short-range target 1b the movement of the ego vehicle 1 leads and according to which basic rules 1c this movement takes place.
  • step 140 the respective quality function R I-4 is set up for the ego vehicle 1 and for the foreign objects 2-4 on the basis of the available information, with the respective type 2d-4d according to the optional substep 141 of the foreign object 2-4 can be used if this was determined in the optional step 115.
  • step 150 the quality functions R I-4 are expanded to quality measures Q I-4 , which also include the expected value E (P (x ')) of a distribution of the
  • Quality steps Q I-4 are selected in accordance with substep 151, the optima of which for the movement strategies pi- 4 are given by the Bellman optimum.
  • sub-step 152 a Boltzmann-Gibbs distribution is selected as the distribution of the probabilities P (x ') of changes in state x'.
  • step 160 those movement strategies pi- 4 of the ego vehicle and of the foreign objects 2-4 are determined which maximize the quality measures Q I-4 .
  • step 170 the sought trajectories 2a-4a become
  • FIG. 2 shows an exemplary embodiment of method 200. Steps 210, 215, 220 and 230 are identical to steps 110, 115, 120 and 130 of the
  • step 240 of the method 200 in contrast to step 140 of the method 100, a complete quality function R I-4 is not determined, but instead
  • Feature functions F I-4 which are parameterized with a set of qi- 4 still free parameters and only form the complete quality function R I-4 in connection with these parameters qi- 4 . If the types 2d-4d of the foreign objects 2-4 were determined in step 215, these can be used in the optional sub-step 241 to select the respective feature function F 2-4 .
  • step 250 the movement strategies pi- 4 of the ego vehicle and the foreign objects are determined as those strategies that maximize the maximum causal entropy.
  • the parameters qi- 4 of the feature functions F I-4 are also determined.
  • sub-step 251 a Boundary condition specified that a recursive determination of the
  • Movement strategies pi- 4 enables.
  • step 260 analogously to step 170 of the method 100, the sought trajectories 2a-4a of the foreign objects 2-4 and the target trajectory la of the ego vehicle 1 adapted to them are determined from the movement strategies pi- 4 .
  • FIG. 3 shows an exemplary embodiment of the method 300.
  • the target trajectory 1 a for the ego vehicle 1 which is adapted to the behavior of the foreign objects 2-4 in the environment 11 of the ego vehicle 1, is determined using the method 100 or 200.
  • This adapted trajectory la is transmitted to the movement planner 13 of the ego vehicle 1 in step 320.
  • a control program 13a for a drive system 24, a steering system 15 and / or a brake system 16 of the ego vehicle 1 is determined by the movement planner 13.
  • trajectory generally refers to a path in combined space and time coordinates. This means that a trajectory is not just a change in the
  • Direction of movement can be changed, but also by changing the speed, such as braking, waiting and starting again later.
  • step 340 the drive system 14, the steering system 15, or the
  • FIG. 4 shows a complex traffic scene in which the described methods 100, 200, 300 can be used advantageously.
  • the described methods 100, 200, 300 can be used advantageously.
  • the lane of a road 50 drives the ego vehicle 1 straight in the direction of the near destination lb.
  • the first foreign object 2 is a further vehicle, the blinker 2e of which indicates that its driver intends to turn into the side street 51 leading to the near target 2b of the vehicle 2.
  • the second foreign object 3 is another vehicle which, from the perspective of the ego vehicle 1, is traveling straight ahead on the opposite lane of the road 50 in the direction of its near destination 3b.
  • the third foreign object 4 is a pedestrian who is a short-range target 4b from his point of view
  • the pedestrian 4 must use the crossing 52 over the road 50, which at the same time causes the driver of the vehicle 3 to
  • Vehicle 2 that will do the best for him would accelerate ego vehicle 1. However, if the driver of vehicle 2 misjudges the situation in that he first has to let vehicle 3 pass in oncoming traffic (which would also be correct without pedestrian 4 on crossing 52), the ego vehicle drives onto the vehicle from behind 2 on.
  • oncoming traffic which would also be correct without pedestrian 4 on crossing 52
  • the speed for the onward journey can be limited to such an extent that in the event that the vehicle 2 actually stops, a collision can still be prevented with full braking.

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Abstract

L'invention concerne un procédé (100, 200) de prédiction des trajectoires (2a-4a) d'objets étrangers (2-4) dans l'environnement (11) d'un égo-véhicule (1) et de détermination d'une trajectoire future propre adaptée (la) de l'égo-véhicule (1). Le procédé comprend les étapes suivantes : • on identifie (110) les objets étrangers (2-4); • on détermine (120) vers quelle cible à courte portée (2b-4b) le mouvement de chacun des objets étrangers (2-4) mène et selon quelles règles de base (2c-4c) ce mouvement s'effectue ; • on détermine (130) vers quelle cible à courte portée (lb) le mouvement de l'égo-véhicule (1) mène et selon quelles règles de base (lc) ce mouvement s'effectue ; • on établit (140) une fonction de qualité RI-4 pour l'égo-véhicule (1) ainsi que pour chacun des objets étrangers (2-4) ; • on établit (150) une mesure de qualité Q1-4 pour l'égo-véhicule (1) ainsi que pour chacun des objets étrangers (2-4) ; • on détermine (160) les stratégies de mouvement optimales π1 -4 de l'égo-véhicule et des objets étrangers (2-4) qui maximisent les mesures de qualité Q1-4 ; • on détermine (170) à partir des stratégies de mouvement optimales πι-4 les trajectoires recherchées (la-4a). L'invention concerne également un procédé (300) de commande de l'égo-véhicule (1) et qu'un programme informatique associé.
EP19727327.9A 2018-06-25 2019-05-22 Adaptation de la trajectoire d'un égo-véhicule à des objets étrangers en mouvement Pending EP3811351A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102018210280.5A DE102018210280A1 (de) 2018-06-25 2018-06-25 Anpassung der Trajektorie eines Ego-Fahrzeugs an bewegte Fremdobjekte
PCT/EP2019/063232 WO2020001867A1 (fr) 2018-06-25 2019-05-22 Adaptation de la trajectoire d'un égo-véhicule à des objets étrangers en mouvement

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EP3811351A1 true EP3811351A1 (fr) 2021-04-28

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US (1) US11858506B2 (fr)
EP (1) EP3811351A1 (fr)
CN (1) CN112292719B (fr)
DE (1) DE102018210280A1 (fr)
WO (1) WO2020001867A1 (fr)

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US20210171061A1 (en) 2021-06-10
US11858506B2 (en) 2024-01-02

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