WO2018216066A1 - Onboard apparatus, traveling assistance method and traveling assistance program - Google Patents

Abstract

This onboard device (100) is installed in a vehicle. If an obstruction is present ahead of the vehicle and an object appears from a blind spot behind the obstruction and moves towards the path of travel that the vehicle is travelling on, a collision point assessing unit (303) assesses whether or not a collision point at which the vehicle and the object will collide is present on the path of travel. A travel management unit (309) determines the traveling state of the vehicle on the basis of the presence or absence of the collision point.

Description

In-vehicle device, driving support method, and driving support program

The present invention relates to driving support for vehicles.

Autonomous vehicles are being developed. Such a vehicle is equipped with a sensor. As a sensor mounted on such a vehicle, there is a distance measuring sensor, a visible light sensor, or an infrared sensor using a laser, a radio wave or a sound wave. The vehicle detects an obstacle around the vehicle using a sensor, and controls steering or acceleration so as to avoid a collision with the detected obstacle.
Such a vehicle is not only controlled to avoid a collision with a detected obstacle, but as disclosed in Patent Document 1, an object hidden behind a blind spot behind a shielding object pops out. In anticipation, control is performed to avoid collisions. The technique of Patent Document 1 detects a dangerous place where a jump is expected, and decelerates or stops the vehicle in response to the dangerous place.

Japanese Patent No. 5062364

In the technique disclosed in Patent Document 1, the vehicle is centrally decelerated based on the relative distance between the dangerous location and the vehicle, and stops so as not to approach the dangerous location more than a certain distance. In the technique disclosed in Patent Document 1, the vehicle needs to pass through a route that avoids the dangerous part far away in order not to stop. However, there is a problem that the vehicle has no choice but to stop when it cannot pass a far-winding route due to restrictions on the lane or road width.

The main object of the present invention is to solve the above-described problems. More specifically, the present invention verifies the possibility that the vehicle and the object will collide when the object jumps out from the blind spot, and determines the appropriate traveling state of the vehicle according to the possibility that the vehicle and the object collide. The main purpose is to determine

The in-vehicle device according to the present invention is
An in-vehicle device mounted on a vehicle,
When there is a shielding object in front of the vehicle, if the object appears from a blind spot behind the shielding object and moves toward the travel route on which the vehicle travels, the vehicle and the object will collide. A collision point determination unit that determines whether or not a collision point exists on the travel route;
A travel management unit that determines a travel state of the vehicle based on the presence or absence of the collision point.

In the present invention, it is determined whether or not there is a collision point on the travel route, the possibility of collision between the vehicle and the object is verified, and the vehicle travels appropriately according to the possibility of collision between the vehicle and the object. The state can be determined.

FIG. 2 is a diagram illustrating a configuration example of a vehicle according to the first embodiment. FIG. 3 is a diagram illustrating a hardware configuration example of the in-vehicle device according to the first embodiment. FIG. 3 is a diagram illustrating a functional configuration example of the in-vehicle device according to the first embodiment. FIG. 3 shows an object and laser light according to Embodiment 1. FIG. 3 shows an object and laser light according to Embodiment 1. 5 is a flowchart illustrating an operation example of the in-vehicle device according to the first embodiment. 5 is a flowchart showing blind spot detection processing according to the first embodiment. The figure which shows the laser beam based on Embodiment 1, a building, and a blind spot. The figure which shows the minimum width and threshold value of a blind spot concerning Embodiment 1. FIG. The figure which shows the blind spot, the assumed appearance position, and virtual object which concern on Embodiment 1. FIG. FIG. 3 is a diagram showing a plurality of points on a travel route according to the first embodiment. The figure which shows the relationship between the vehicle arrival time which concerns on Embodiment 1, virtual object arrival time, and a collision point. The figure which shows the example of the collision point which considered the width | variety of the vehicle which concerns on Embodiment 1, and the width | variety of a virtual object.

Hereinafter, embodiments of the present invention will be described with reference to the drawings. In the following description of the embodiments and drawings, the same reference numerals denote the same or corresponding parts.

Embodiment 1 FIG.
*** Explanation of configuration ***
FIG. 1 shows a configuration example of a vehicle 110 according to the present embodiment.
The vehicle 110 is equipped with an in-vehicle device 100. The in-vehicle device 100 performs driving support for the vehicle 110. The operation performed by the in-vehicle device 100 corresponds to a driving support method and a driving support program.
A plurality of surrounding monitoring sensors 111 are mounted on the vehicle 110.
In the example of FIG. 1, the periphery monitoring sensor 111 is mounted on the front, rear, side, and roof of the vehicle 110. The periphery monitoring sensor 111 scans the periphery of the vehicle 110 to detect an obstacle.

FIG. 2 shows a hardware configuration example of the in-vehicle device 100.
The in-vehicle device 100 is a computer.
The in-vehicle device 100 includes a processor 101, a memory 102, an auxiliary storage device 103, a peripheral situation acquisition interface 104, and a vehicle control interface 105 as hardware.
The in-vehicle device 100 includes a sensor data acquisition unit 301, a blind spot detection unit 302, a collision point determination unit 303, a virtual object arrival time prediction unit 306, a vehicle arrival time prediction unit 307, a collision point prediction unit 308, and a travel configuration. A management unit 309 and a travel control unit 310 are provided. Details of the sensor data acquisition unit 301, blind spot detection unit 302, collision point determination unit 303, virtual object arrival time prediction unit 306, vehicle arrival time prediction unit 307, collision point prediction unit 308, travel management unit 309, and travel control unit 310 It will be described later.
The auxiliary storage device 103 includes a sensor data acquisition unit 301, a blind spot detection unit 302, a collision point determination unit 303, a virtual object arrival time prediction unit 306, a vehicle arrival time prediction unit 307, a collision point prediction unit 308, a travel management unit 309, and A program for realizing the function of the traveling control unit 310 is stored.
These programs are loaded into the memory 102. The processor 101 reads these programs from the memory 102 and executes these programs. As a result, the processor 101 includes a sensor data acquisition unit 301, a blind spot detection unit 302, a collision point determination unit 303, a virtual object arrival time prediction unit 306, a vehicle arrival time prediction unit 307, a collision point prediction unit 308, and a travel management unit, which will be described later. 309 and the travel control unit 310 are operated.
In FIG. 2, the processor 101 includes a sensor data acquisition unit 301, a blind spot detection unit 302, a collision point determination unit 303, a virtual object arrival time prediction unit 306, a vehicle arrival time prediction unit 307, a collision point prediction unit 308, a travel management unit 309, and The state which is executing the program which realizes the function of run control part 310 is typically expressed.
The surrounding situation acquisition interface 104 is connected to the surrounding monitoring sensor 111. The peripheral situation acquisition interface 104 acquires sensor data that is a scanning result of the peripheral monitoring sensor 111 from the peripheral monitoring sensor 111.
The vehicle control interface 105 is connected to a control mechanism in the vehicle 110. The vehicle control interface 105 transmits a command for controlling the traveling of the vehicle to the control mechanism. For example, the vehicle control interface 105 transmits a command that instructs the vehicle 110 to decelerate. The vehicle control interface 105 may be connected to an interface device used by the driver. For example, the vehicle control interface 105 transmits a message asking the driver to decelerate the vehicle 110 to a dashboard display, speaker, or HUD (Head-Up Display).

The surrounding monitoring sensor 111 is, for example, Laser Imaging Detection and Ranging (LiDAR).
LiDAR irradiates a laser beam, and measures the distance from the time required for the laser beam to return after being reflected by the object and the speed of the light. LiDAR measures the distance to surrounding objects by rotating the direction of laser light irradiation in the horizontal direction. In the distance information obtained by LiDAR, an azimuth angle and an elevation angle indicating the direction of laser irradiation are represented. In the distance information, the surface of the object is represented as a point group by the measured distance.
4 and 5 show an object existing around the LiDAR and the laser light emitted from the LiDAR. FIG. 4 shows a state where the LiDAR and the objects 201 to 203 are looked down from above. FIG. 5 shows a state where the LiDAR, the object 202, and the object 203 are viewed from the side.
4 and 5 show examples of distance information obtained by LiDAR installed at the point 200. FIG. 4 and 5 indicate that the distance can be measured by reflection of the laser beam. On the other hand, the broken lines in FIGS. 4 and 5 indicate that the laser beam is not reflected and the distance cannot be measured. If the distance cannot be measured, the distance is treated as infinity. As a result, distance information of positions indicated by black dots as part of the shapes of the objects 201 to 203 is obtained. Depending on the type of LiDAR, surrounding shape information can be obtained by executing the same processing for different angles in the vertical direction.
As the surrounding monitoring sensor 111, a millimeter wave radar may be used instead of LiDAR.
Millimeter wave radar is a sensor that measures the distance to an object from the time required for the radio wave to irradiate and reflected by the object and returning. When the millimeter wave radar is used, peripheral distance information can be obtained in a fan shape around the millimeter wave radar.
Further, as the periphery monitoring sensor 111, a stereo camera may be used instead of LiDAR. Further, as the periphery monitoring sensor 111, a sonic sonar or an ultrasonic sonar may be used instead of LiDAR.
Regardless of which sensor is used as the periphery monitoring sensor 111, sensor data including distance information is obtained from the periphery monitoring sensor 111.

FIG. 3 shows a functional configuration example of the in-vehicle device 100 according to the present embodiment.
As described above, the in-vehicle device 100 includes the sensor data acquisition unit 301, blind spot detection unit 302, collision point determination unit 303, virtual object arrival time prediction unit 306, vehicle arrival time prediction unit 307, collision point prediction unit 308, travel management. Part 309 and travel control part 310 are included. The sensor data acquisition unit 301, blind spot detection unit 302, collision point determination unit 303, virtual object arrival time prediction unit 306, vehicle arrival time prediction unit 307, collision point prediction unit 308, travel management unit 309, and travel control unit 310 are programs. Realized. The sensor data acquisition unit 301, blind spot detection unit 302, collision point determination unit 303, virtual object arrival time prediction unit 306, vehicle arrival time prediction unit 307, collision point prediction unit 308, travel management unit 309, and travel control unit 310 A program to be realized is executed by the processor 101.

The sensor data acquisition unit 301 acquires sensor data from the peripheral monitoring sensor 111 via the peripheral state acquisition interface 104.

The blind spot detection unit 302 detects the blind spot based on the sensor data. More specifically, the blind spot detection unit 302 detects a blind spot behind the shield based on the sensor data when there is a shield in front of the vehicle 110. That is, the blind spot detection unit 302 estimates the position of the blind spot. Also, the blind spot detection unit 302 estimates the width of the blind spot based on the sensor data.
Also, the blind spot detection unit 302 calculates an assumed appearance position that is a position where a virtual object appears from the blind spot.

The collision point determination unit 303 determines whether or not there is a collision point on the travel route where the vehicle and the virtual object collide when the virtual object appears from the blind spot and moves toward the travel route on which the vehicle 110 travels. Is determined based on the position of the blind spot and the width of the blind spot. The collision point determination unit 303 notifies the traveling management unit 309 of the collision point when there is a collision point on the travel route. The collision point determination unit 303 is a point where the vehicle 110 and the object of the plurality of points collide when the virtual object appears from the blind spot and moves toward each of the plurality of points on the travel route. To the travel management unit 309 as a collision point.
The process performed by the collision point determination unit 303 corresponds to the collision point determination process.
The collision point determination unit 303 includes a virtual object arrival time prediction unit 306, a vehicle arrival time prediction unit 307, and a collision point prediction unit 308.
Hereinafter, the virtual object arrival time prediction unit 306, the vehicle arrival time prediction unit 307, and the collision point prediction unit 308 will be described.

The virtual object arrival time prediction unit 306 predicts the arrival time of the virtual object to each of a plurality of points on the travel route.
For example, the virtual object arrival time predicting unit 306 calculates the arrival time of the virtual object at each of the plurality of points based on the moving speed of the object estimated from the blind spot width. More specifically, the virtual object arrival time prediction unit 306 determines the type of virtual object (vehicle, person, bicycle, etc.) from the width of the blind spot, and estimates the moving speed of the object from the determined type of virtual object. Then, the virtual object arrival time predicting unit 306 calculates the arrival time of the virtual object at each of the plurality of points based on the estimated moving speed of the virtual object.

The vehicle arrival time prediction unit 307 predicts the arrival time of the vehicle 110 at each of a plurality of points.
For example, the vehicle arrival time prediction unit 307 calculates the arrival time of the vehicle 110 at each of a plurality of points based on the current traveling speed of the vehicle 110.

The collision point prediction unit 308 determines whether or not a collision point exists on the travel route based on the calculation result of the virtual object arrival time prediction unit 306 and the calculation result of the vehicle arrival time prediction unit 307.
Specifically, if there is a point where the arrival time of the vehicle 110 and the arrival time of the virtual object match, the collision point prediction unit 308 determines that there is a collision point, and the arrival time of the vehicle 110 and the arrival of the virtual object A point where the time coincides is extracted as a collision point. Then, the collision point prediction unit 308 notifies the traveling management unit 309 of the extracted collision point.

The traveling management unit 309 determines the traveling state of the vehicle 110 based on the presence / absence of a collision point.
Specifically, the traveling management unit 309 determines to maintain the current traveling state of the vehicle 110 when there is no collision point. Specifically, the travel management unit 309 determines to keep the vehicle 110 traveling at the current travel speed.
On the other hand, when there is a collision point, the traveling management unit 309 determines to drive the vehicle 110 in a state where the vehicle 110 can stop before the collision point notified by the collision point prediction unit 308. More specifically, the travel management unit 309 generates a travel plan that can stop the vehicle 110 before the collision point. For example, the travel management unit 309 generates a travel plan that can decelerate the vehicle 110 at a certain rate and stop the vehicle 110 before the collision point. The point before the collision point is any point within a range of 1 to 5 meters before the collision point.
When the travel plan is generated, the travel management unit 309 notifies the travel control unit 310 of the generated travel plan.
The process performed by the travel management unit 309 corresponds to a travel management process.

The travel control unit 310 controls the travel of the vehicle 110 according to the travel plan notified by the travel management unit 309.
Specifically, if vehicle 110 is an automatic traveling vehicle, traveling control unit 310 outputs a command for realizing a traveling plan to a control mechanism in vehicle 110 via vehicle control interface 105. For example, the travel control unit 310 outputs a command for instructing deceleration at a timing specified in the travel plan. On the other hand, if the vehicle 110 is not an automatic traveling vehicle, the traveling control unit 310 outputs a message addressed to the driver for realizing the traveling plan to the interface device used by the driver via the vehicle control interface 105. For example, the travel control unit 310 outputs a message requesting the driver to decelerate at a timing specified in the travel plan.

*** Explanation of operation ***
FIG. 6 shows an operation example of the in-vehicle device 100.
The in-vehicle device 100 periodically performs a series of processes from step S401 to step S409.
The travel management unit 309 generates a travel plan across the repetition process for each blind spot. For this reason, the travel management unit 309 stores in the memory 102 the travel plan generated by the processing of steps S403 to S408 for one blind spot. Then, the travel management unit 309 updates the travel plan in the memory 102 if it is necessary to update the travel plan in the processing of steps S403 to S408 for another blind spot. In this way, the travel management unit 309 obtains a final travel plan when the processes of steps S403 to S408 are performed for all blind spots.
If a travel plan having a travel speed slower than the current travel plan is obtained during the repetition of steps S403 to S408, the travel management unit 309 overwrites the current travel plan with the newly obtained travel plan. The prediction of the vehicle arrival time in step S405 is performed based on the travel speed of the latest travel plan.
Note that the broken line connecting step S403 and step S408 in FIG. 6 indicates the correspondence between the start point and end point of the iterative processing from step S403 to S408.
Hereinafter, each step of FIG. 6 will be described.

In step S <b> 401, the sensor data acquisition unit 301 acquires sensor data of the periphery monitoring sensor 111 via the surrounding state acquisition interface 104.

In step S <b> 402, the blind spot detection unit 302 detects a blind spot ahead of the vehicle 110. That is, the vehicle 110 estimates the position of the blind spot and the width of the blind spot.
Details of step S402 will be described.

FIG. 8 shows laser light emitted radially from the LiDAR mounted on the vehicle 110 traveling on the travel route 800.
The laser beam 801 is reflected by the building 803 that is a shield. The distance measured by this result is referred to as distance A. A laser beam 802 adjacent to the laser beam 801 is reflected by the building 803. The distance measured by this result is referred to as distance B. The laser beam 804 is reflected by the building 805 which is a shield. The distance measured by this result is referred to as distance C. At this time, the distance A and the distance B are distances to a close point on the same surface of the building 803. For this reason, the difference between the distance A and the distance B is small. On the other hand, the distance B and the distance C are distances to different building surfaces. For this reason, the difference between the distance B and the distance C is large. The blind spot detection unit 302 determines that there is a blind spot 806 when the difference in the distance between two adjacent laser beams exceeds the threshold T. The threshold T is approximately obtained from the angle θ formed by the travel path 800 of the vehicle 110 and the laser beam and the minimum width W of the blind spot to be detected.
In FIG. 9 obtained by excluding unnecessary elements from FIG. 8, the distance 900 between the building 803 and the building 805 is the minimum width W of the blind spot to be detected. Further, the threshold T corresponds to the distance 902 when the angle 901 is an angle θ formed by the travel route 800 and the laser beam. For this reason, the threshold value T is obtained by the equation (1).
T = W / cos (θ) Equation (1)

When it is determined that there is a blind spot, the blind spot detection unit 302 calculates the position of the blind spot angle 807 based on the shorter laser light of the two adjacent laser lights whose distance difference exceeds the threshold T. . In the example of FIG. 8, the blind spot detection unit 302 calculates a reflection position based on the azimuth angle and distance of the laser beam 802, which is a laser beam having a short distance between the laser beam 802 and the laser beam 804, and calculates the calculated reflection position. Designated as a blind spot angle 807.

The blind spot detection unit 302 further calculates an assumed appearance position based on the position of the blind spot angle 807. The assumed appearance position is a position where a virtual object is assumed to appear. The blind spot detection unit 302 may approximately treat the blind spot angle 807 as an assumed appearance position. This method is the simplest method.
Further, the blind spot detection unit 302 calculates an assumed appearance position based on the ability of the object detection algorithm for detecting the appearance of the object, the assumed virtual object width W ₁ , and the distance d between the assumed virtual object and the building. Also good. The object detection algorithm is generally an algorithm that can detect an object by measuring a part of the object with a sensor. For example, when an algorithm for detecting a vehicle using a camera is used, the vehicle can be detected if a certain ratio or more of the front surface of the vehicle is reflected in the camera. When this algorithm is used, a ratio α that needs to be sensed among the front surface of the object is set as the object detection condition. The minimum value of α is 0, and the maximum value is 1. α = 1 means that an object detection condition is that the object detection algorithm senses the entire front surface of the object.

FIG. 10 shows a positional relationship between the blind spot angle 807 and the assumed appearance position 1001 of the virtual object 1000 popping out from the blind spot. That is, FIG. 10 shows that the assumed appearance position 1001 is located at a point moved from the blind spot angle 807 by a distance indicated by reference numeral 1002 in the horizontal direction and a distance indicated by reference numeral 1003 in the vertical direction. Note that β = 1−α. The vertical distance y (reference numeral 1003) can be obtained by Expression (2).
y = d _{+ W} 1/2 Equation (2)
Further, the lateral distance x (reference numeral 1002) can be obtained by Expression (3).
x = (d + βW ₁ ) / tan (90−θ) Equation (3)
It is assumed that the ratio α is set based on detection conditions of an object detection algorithm that is separately mounted on the vehicle 110. The width W ₁ of the virtual object 1000 to assume is set according to the type of object to be assumed. Further, the interval d between the virtual object 1000 and the building 803 may be 0, or the width between the roadside belt and the sidewalk obtained from nearby map data may be set as the interval d. The map data is stored in the auxiliary storage device 103, for example.

The blind spot detection unit 302 may register the calculated expected appearance position 1001 and the interval d in a database on the auxiliary storage device 103. In addition, the blind spot detection unit 302 may register the position of the blind spot, for example, the position of the blind spot corner 807 in the database. Further, the blind spot detection unit 302 may register a blind spot width described later in a database.
The blind spot detection unit 302 includes, for example, an assumed appearance position 1001 represented by a relative position from the vehicle 110, an interval d, a blind spot angle 807, a blind spot width, and an absolute position of the vehicle 110 (for example, latitude and longitude). Are registered in a database. The absolute position of the vehicle 110 is obtained, for example, by GPS (Global Positioning System) positioning.
When the vehicle 110 arrives at the same place after registration of these values in the database, the blind spot detection unit 302 has the assumed appearance position 1001, the interval d, the blind spot angle 807, and the blind spot width registered in the database. You may acquire at least 1 or more. And the collision point determination part 303 may determine the presence or absence of a collision point using these values acquired by the blind spot detection part 302. FIG.

FIG. 7 shows a processing procedure of the blind spot detection unit 302 described above.
The broken line connecting step S501 and step S505 in FIG. 7 indicates the correspondence between the start point and end point of the iterative processing from step S501 to step S505. A broken line connecting step 502 and step 504 indicates the correspondence between the start point and end point of the iterative processing from step S502 to step S504.

The blind spot detection unit 302 notifies the virtual object arrival time prediction unit 306 of the assumed appearance position 1001 obtained by the above procedure. The blind spot detection unit 302 instructs the vehicle arrival time prediction unit 307 to predict the vehicle arrival time.

Next, in step S404 of FIG. 6, the virtual object arrival time prediction unit 306 assumes that the virtual object 1000 has jumped out of the blind spot at the assumed appearance position 1001 at the current time, and the virtual object 1000 has a plurality of points on the travel route 800. The time (virtual object arrival time) at which each point is reached is predicted.
Since it is unclear to which point on the travel route 800 the virtual object 1000 jumps out, the virtual object arrival time prediction unit 306 predicts virtual object arrival times at a plurality of points on the travel route 800.

In step S405, the vehicle arrival time prediction unit 307 predicts the time (vehicle arrival time) at which the vehicle 110 reaches each of a plurality of points on the travel route 800.

As illustrated in FIG. 11, the virtual object arrival time prediction unit 306 is configured to move the virtual object 1000 from the expected appearance position 1001 toward a plurality of points on the travel route 800 of the vehicle 110 (each black circle in FIG. 11 represents a point). When moving, the time when the virtual object 1000 reaches each point is predicted.
The virtual object arrival time prediction unit 306 obtains the virtual object arrival time at each point by dividing the distance between each point on the travel route 800 and the assumed appearance position 1001 by the assumed movement speed of the virtual object 1000. A method for obtaining the assumed moving speed of the virtual object 1000 will be described later.
The vehicle arrival time prediction unit 307 predicts the time to reach a plurality of points on the travel route 800.
The vehicle arrival time prediction unit 307 obtains the vehicle arrival time at each point by dividing the distance between the current position of the vehicle 110 and each point on the travel route 800 by the current travel speed of the vehicle 110.
The virtual object arrival time prediction unit 306 outputs the virtual object arrival time at each point to the collision point prediction unit 308. Further, the vehicle arrival time prediction unit 307 outputs the vehicle arrival time at each point to the collision point prediction unit 308.

Next, in step S406 of FIG. 6, the collision point prediction unit 308 predicts a collision point.
Specifically, the collision point prediction unit 308 extracts a point where the virtual object arrival time and the vehicle arrival time match as a collision point.
FIG. 12 shows the relationship between the virtual object arrival time and the vehicle arrival time.
In FIG. 12, the origin 1200 indicates the current position of the vehicle 110.
A curve 1201 is a curve obtained by plotting the virtual object arrival time at each point on the travel route 800. A straight line 1202 is a straight line obtained by plotting the vehicle arrival time at each point on the travel route 800.
The collision point prediction unit 308 extracts a point indicated by reference numeral 1203 where the virtual object arrival time and the vehicle arrival time coincide with each other as a collision point.
In FIG. 12, the vehicle 110 reaches and passes through a point on the left side of the point indicated by reference numeral 1203, so that no collision occurs. Further, since the virtual object reaches and passes through the point on the right side of the point indicated by reference numeral 1203, no collision occurs.
In this embodiment, since it is assumed that both the virtual object 1000 and the vehicle 110 travel at a constant speed, the plot result of the virtual object arrival time is a curve 1201, and the plot result of the vehicle arrival time is a straight line 1202. Become.
However, the virtual object 1000 and the vehicle 110 may not travel at a constant speed. Even when at least one of the virtual object 1000 and the vehicle 110 does not travel at a constant speed, the collision point prediction unit 308 determines a point where the imaginary object arrival time and the vehicle arrival time coincide with each other according to the same procedure as described above. Can be extracted as
When the collision point prediction unit 308 extracts the collision point, the collision point prediction unit 308 notifies the traveling management unit 309 of the extracted collision point.

The virtual object arrival time predicting unit 306 can predict the virtual object arrival time using a fixed speed as the assumed movement speed of the virtual object 1000. For example, the virtual object arrival time prediction unit 306 can predict the virtual object arrival time using the legal speed of the vehicle when the type of the virtual object 1000 is assumed to be a vehicle. Further, the virtual object arrival time predicting unit 306 may determine the type of the virtual object 1000 based on the width of the blind spot, and predict the virtual object arrival time using the assumed moving speed according to the type of the virtual object 1000. .

The blind spot width is calculated by the blind spot detection unit 302 from the distance difference between adjacent laser beams obtained when calculating the assumed appearance position 1001. More specifically, the blind spot detection unit 302 can obtain the width of the blind spot from the difference in distance between adjacent laser beams and the angle 901 in FIG. Reference numeral 8000 in FIG. 10 is an imaginary line obtained by translating the travel route 800. The width of the blind spot corresponds to the distance 900 between the building 803 and the building 805 in FIG.
For example, when the width of the blind spot exceeds the minimum lane width defined by the law, the virtual object arrival time prediction unit 306 determines the vehicle as the type of the virtual object 1000. Then, the virtual object arrival time prediction unit 306 estimates a high speed such as 60 km / h as the moving speed of the vehicle. On the other hand, when the width of the blind spot does not exceed the minimum width of the lane, the virtual object arrival time prediction unit 306 determines a pedestrian or a bicycle as the type of the virtual object 1000. Then, the virtual object arrival time prediction unit 306 estimates a low speed such as 20 km / h as the moving speed of the pedestrian or the bicycle. Note that the minimum lane width defined by laws and regulations is, for example, 2.75 meters for the fourth-class first-class small road of the Road Structure Ordinance in Japan.

11 and 12, the collision point prediction unit 308 extracts a point where the virtual object arrival time and the vehicle arrival time coincide with each other as a collision point.
Instead of this, the collision point prediction unit 308 may extract the collision point in consideration of the width of the vehicle 110 and the width of the virtual object 1000.
FIG. 13 shows an example in which the collision point prediction unit 308 extracts a collision point in consideration of the width of the vehicle 110 and the width of the virtual object 1000.
In the example of FIG. 13, when the virtual object 1000 moves from the assumed appearance position 1001 toward the point 1301 on the travel route 800, the front part of the virtual object 1000 and the rear part of the vehicle 110 are in contact with each other.
Although the vehicle arrival time and the virtual object arrival time at the point 1301 are different, the collision point prediction unit 308 extracts the point 1301 as a collision point in consideration of the width 1302 of the vehicle 110 and the width 1303 of the virtual object 1000.

In step S407 in FIG. 6, when the collision point prediction unit 308 is notified of the collision point, the traveling management unit 309 generates a traveling plan V that can stop the vehicle 110 before the collision point.
Here, it is assumed that the travel management unit 309 generates a travel plan V that can stop the vehicle 110 at a point 1 meter before the collision point (hereinafter referred to as a collision avoidance point). The distance from the current position of the vehicle 110 to the collision avoidance point is L meters. For this reason, the distance from the current position of the vehicle 110 to the collision point is (L + 1) meters.
The travel management unit 309 decelerates the vehicle 110 at an acceleration a (negative value) meter every second, and a travel plan V that can stop the vehicle 110 at a collision avoidance point that is L meters away from the current position of the vehicle 110, As a function V (l) of the distance l to an arbitrary point on the travel route 800, it is obtained according to the equations (4) and (5).
V (l) = √ (−2al) (l ≦ L) Equation (4)
V (l) = 0 (L <l) Formula (5)
In general, the minimum acceleration A (negative value) at which the absolute value at which the vehicle 110 can be stopped is maximized is represented by A = −μG using the road friction coefficient μ and the gravitational acceleration G. The acceleration a is set so as to satisfy −μG ≦ a ≦ 0. In order to stop the vehicle 110 by sudden braking after the virtual object 1000 jumps out of the assumed appearance position 1001, the travel management unit 309 sets a travel plan V for decelerating the vehicle 110 with acceleration a = A = −μG. Generate. On the other hand, when giving priority to the passenger comfort of the vehicle 110, the travel management unit 309 sets the acceleration a to a value close to 0 and generates the travel plan V.
The travel management unit 309 notifies the travel control unit 310 of the generated travel plan V.
When the collision point is not notified from the collision point prediction unit 308, the travel management unit 309 does not generate a travel plan. That is, the travel management unit 309 does not limit the travel speed of the vehicle 110.

In step S409 in FIG. 6, the travel control unit 310 controls the travel speed of the vehicle 110 based on the travel plan V when the travel plan V is notified from the travel management unit 309.
Specifically, if vehicle 110 is an automatic traveling vehicle, traveling control unit 310 outputs a command for realizing a traveling plan to a control mechanism in vehicle 110 via vehicle control interface 105. For example, the travel control unit 310 outputs a command for instructing deceleration at a timing specified in the travel plan. On the other hand, if the vehicle 110 is not an automatic traveling vehicle, the traveling control unit 310 outputs a message addressed to the driver for realizing the traveling plan to the interface device used by the driver via the vehicle control interface 105. For example, the travel control unit 310 outputs a message requesting the driver to decelerate at a timing specified in the travel plan.

*** Explanation of the effect of the embodiment ***
As described above, in the present embodiment, the in-vehicle device 100 determines whether or not a collision point exists on the travel route, verifies the possibility that the vehicle and the object collide, and the vehicle and the object are An appropriate traveling state of the vehicle can be determined according to the possibility of collision.
Specifically, the in-vehicle device 100 can continue the current traveling state of the vehicle if there is no possibility that the vehicle and the object collide. On the other hand, when there is a possibility that the vehicle and the object collide, the in-vehicle device 100 can enter a traveling state in which the vehicle can be stopped before the collision point in order to avoid the collision.

Embodiment 2. FIG.
In the first embodiment, the blind spot detection unit 302 estimates the position of the blind spot and the width of the blind spot based on the sensor data of the periphery monitoring sensor 111 and specifies the assumed appearance position 1001.
In the present embodiment, the blind spot detection unit 302 refers to the map data, estimates the position of the blind spot and the width of the blind spot, and specifies the assumed appearance position 1001.
Also in the present embodiment, a configuration example of the vehicle 110 is as shown in FIG. An example of the hardware configuration of the in-vehicle device 100 is also as shown in FIG. Furthermore, a functional configuration example of the in-vehicle device 100 is also as shown in FIG.
Hereinafter, differences from the first embodiment will be mainly described.
Note that matters not described below are the same as those in the first embodiment.

As described above, it is assumed that map data is stored in the auxiliary storage device 103 shown in FIG.
The blind spot detection unit 302 can access the map data in the auxiliary storage device 103.
In the present embodiment, a positioning unit (not shown in FIG. 3) calculates the latitude and longitude of the position where the vehicle 110 is located by GPS positioning. The blind spot detection unit 302 can access the map data only in the vicinity of the location of the vehicle 110.
The blind spot detection unit 302 refers to the map data and detects a shielding object present in front of the vehicle 110. For example, the blind spot detection unit 302 detects the building 803 and the building 805 shown in FIG. 8 with reference to the map data. Then, the space behind the building 803 is specified as a blind spot 806. Also, the blind spot detection unit 302 refers to the map data, calculates the width of the blind spot, and sets the assumed appearance position 1001.
Thereafter, the processing after step S403 in FIG. 6 is performed on the assumed appearance position 1001 set by the blind spot detection unit 302.

As described above, in the present embodiment, the in-vehicle device 100 can specify the position of the blind spot by referring to the map data without using the sensor data. For this reason, it is possible to determine whether or not there is a collision point on the travel route without using sensor data, and to determine an appropriate travel state of the vehicle according to the possibility of collision between the vehicle and the object. can do.

Although the embodiments of the present invention have been described above, these two embodiments may be combined and implemented.
Alternatively, one of these two embodiments may be partially implemented.
Alternatively, these two embodiments may be partially combined.
In addition, this invention is not limited to these embodiment, A various change is possible as needed.

*** Explanation of hardware configuration ***
Finally, a supplementary description of the hardware configuration of the in-vehicle device 100 will be given.
A processor 101 illustrated in FIG. 2 is an IC (Integrated Circuit) that performs processing.
The processor 101 is a CPU (Central Processing Unit), a DSP (Digital Signal Processor), or the like.
The memory 102 shown in FIG. 2 is a RAM (Random Access Memory).
The auxiliary storage device 103 shown in FIG. 2 is a ROM (Read Only Memory), a flash memory, an HDD (Hard Disk Drive), or the like.

The auxiliary storage device 103 also stores an OS (Operating System).
At least a part of the OS is loaded into the memory 102 and executed by the processor 101.
While executing at least a part of the OS, the processor 101 performs sensor data acquisition unit 301, blind spot detection unit 302, collision point determination unit 303, virtual object arrival time prediction unit 306, vehicle arrival time prediction unit 307, and collision point prediction unit 308. The program for realizing the functions of the travel management unit 309 and the travel control unit 310 is executed.
When the processor 101 executes the OS, task management, memory management, file management, communication control, and the like are performed.
Further, the in-vehicle device 100 may include a plurality of processors that replace the processor 101. The plurality of processors include a sensor data acquisition unit 301, a blind spot detection unit 302, a collision point determination unit 303, a virtual object arrival time prediction unit 306, a vehicle arrival time prediction unit 307, a collision point prediction unit 308, a travel management unit 309, and a travel It shares the execution of the program that realizes the function of the control unit 310. Each processor is an IC that performs processing in the same manner as the processor 101.
The sensor data acquisition unit 301, blind spot detection unit 302, collision point determination unit 303, virtual object arrival time prediction unit 306, vehicle arrival time prediction unit 307, collision point prediction unit 308, travel management unit 309, and travel control unit 310 At least one of information, data, a signal value, and a variable value indicating a processing result is stored in at least one of the memory 102, the auxiliary storage device 103, a register in the processor 101, and a cache memory.
The sensor data acquisition unit 301, blind spot detection unit 302, collision point determination unit 303, virtual object arrival time prediction unit 306, vehicle arrival time prediction unit 307, collision point prediction unit 308, travel management unit 309, and travel control unit 310 The program that realizes the function may be stored in a portable storage medium such as a magnetic disk, a flexible disk, an optical disk, a compact disk, a Blu-ray (registered trademark) disk, or a DVD.

The sensor data acquisition unit 301, blind spot detection unit 302, collision point determination unit 303, virtual object arrival time prediction unit 306, vehicle arrival time prediction unit 307, collision point prediction unit 308, travel management unit 309, and travel control unit 310 “Part” may be read as “circuit” or “process” or “procedure” or “processing”.
The in-vehicle device 100 may be realized by a processing circuit such as a logic IC (Integrated Circuit), a GA (Gate Array), an ASIC (Application Specific Integrated Circuit), or an FPGA (Field-Programmable Gate Array).
In this case, the sensor data acquisition unit 301, blind spot detection unit 302, collision point determination unit 303, virtual object arrival time prediction unit 306, vehicle arrival time prediction unit 307, collision point prediction unit 308, travel management unit 309, and travel control unit 310 is realized as a part of each processing circuit.
In this specification, the superordinate concept of the processor, the memory, the combination of the processor and the memory, and the processing circuit is referred to as “processing circuitry”.
That is, the processor, the memory, the combination of the processor and the memory, and the processing circuit are specific examples of “processing circuitries”.

100 in-vehicle device, 101 processor, 102 memory, 103 auxiliary storage device, 104 peripheral status acquisition interface, 105 vehicle control interface, 110 vehicle, 111 peripheral monitoring sensor, 301 sensor data acquisition unit, 302 blind spot detection unit, 303 collision point determination unit 306, virtual object arrival time prediction unit, 307, vehicle arrival time prediction unit, 308 collision point prediction unit, 309 travel management unit, 310 travel control unit, 800 travel route, 1000 virtual object, 1001 assumed appearance position.

Claims (15)

An in-vehicle device mounted on a vehicle,
When there is a shielding object in front of the vehicle, if the object appears from a blind spot behind the shielding object and moves toward the travel route on which the vehicle travels, the vehicle and the object will collide. A collision point determination unit that determines whether or not a collision point exists on the travel route;
A vehicle-mounted device comprising: a travel management unit that determines a travel state of the vehicle based on the presence or absence of the collision point. The collision point determination unit
When the collision point exists on the travel route, notify the travel management unit of the collision point,
The travel management unit
The in-vehicle device according to claim 1, wherein the vehicle is determined to travel in a state where the vehicle can be stopped before the collision point notified by the collision point determination unit. The collision point determination unit
When the object appears from the blind spot and moves toward each of a plurality of points on the travel route, a point where the vehicle and the object collide with each other among the plurality of points is determined as the collision. The in-vehicle device according to claim 2, wherein the travel management unit is notified as a point. The collision point determination unit
Calculating the arrival time of the vehicle and the arrival time of the object to each of the plurality of points;
The in-vehicle device according to claim 3, wherein a point at which the arrival time of the vehicle coincides with the arrival time of the object is notified to the travel management unit as the collision point. The collision point determination unit
The arrival time of the vehicle at each of the plurality of points is calculated based on the current traveling speed of the vehicle, and to each of the plurality of points based on the moving speed of the object estimated from the width of the blind spot The in-vehicle device according to claim 4, wherein the arrival time of the object is calculated. The collision point determination unit
The type of the object is determined from the width of the blind spot, the moving speed of the object is estimated from the determined type of the object, and the object is moved to each of the plurality of points based on the estimated moving speed of the object. The in-vehicle device according to claim 5, wherein the arrival time is calculated. The in-vehicle device further includes
A blind spot detector for estimating the position of the blind spot and the width of the blind spot;
The collision point determination unit
The in-vehicle device according to claim 1, wherein it is determined whether or not the collision point exists based on the position of the blind spot and the width of the blind spot estimated by the blind spot detection unit. The blind spot detector is
The in-vehicle device according to claim 7, wherein a position of the blind spot and a width of the blind spot are estimated using a scanning result of a sensor mounted on the vehicle and scanning the front of the vehicle. The blind spot detector is
The in-vehicle device according to claim 8, wherein the estimated position of the blind spot and the width of the blind spot are registered in a database. The blind spot detector is
After registering the position of the blind spot and the width of the blind spot in the database, the position of the blind spot registered in the database and the position when the vehicle reaches the position where the shielding object exists in the front again and the Get the width of the blind spot,
The collision point determination unit
The in-vehicle device according to claim 9, wherein the collision point is extracted based on the position of the blind spot and the width of the blind spot acquired by the blind spot detection unit. The travel management unit
The in-vehicle device according to claim 2, wherein it is determined to decelerate the vehicle at a constant rate. The travel management unit
The in-vehicle device according to claim 2, wherein the vehicle is determined to travel in a state where the vehicle can be stopped at any point within a range of 1 meter to 5 meters before the collision point. The blind spot detector is
The in-vehicle device according to claim 7, wherein the position of the blind spot and the width of the blind spot are estimated with reference to map data. When a computer mounted on a vehicle has a shielding object in front of the vehicle and an object appears from a blind spot behind the shielding object and moves toward a travel route on which the vehicle travels, the vehicle and the vehicle Determine whether there is a collision point on the travel route where the object will collide,
A traveling support method in which the computer determines a traveling state of the vehicle based on the presence or absence of the collision point. In the computer installed in the vehicle,
When there is a shielding object in front of the vehicle, if the object appears from a blind spot behind the shielding object and moves toward the travel route on which the vehicle travels, the vehicle and the object will collide. A collision point determination process for determining whether or not a collision point exists on the travel route;
A travel support program for executing a travel management process for determining a travel state of the vehicle based on the presence or absence of the collision point.

Images