Vehicle control device

Abstract

To provide a vehicle control device that can detect targets existing around a vehicle from sensed point group data, more than before.SOLUTION: A vehicle control device according to the present invention, which comprises a distance measurement sensor that senses a peripheral point group that is a point group of feature points around an own vehicle, further comprises: generating means that generates compartment information for partitioning the peripheral point group into predetermined areas on the basis of peripheral point group information showing the peripheral point group sensed by the distance measurement sensor; and identifying means that when a percentage indicating existence of the peripheral point groups in the predetermined areas partitioned based on the compartment information is over a predetermined percentage, identifies, as a target to be detected existing around the own vehicle, the point group included in the compartment information.SELECTED DRAWING: Figure 3

Description

[Technical field]

[0001] The present invention relates to a vehicle control device. [Background technology]

[0002] In recent years, research has been conducted into autonomous driving, which allows a vehicle to travel without being driven by a user. In autonomous driving control, for example, a technology has been disclosed that relates to a vehicle control device that detects targets using a lidar mounted on the vehicle and identifies whether the targets are present around the vehicle based on the detected point cloud data. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2023-32069 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, when a vehicle control device identifies a target from point cloud data, for example, if a structure on the side of the road (such as a street light, utility pole, or road tree) is approximately the same size as a human, the structure may be identified as a human, so there is room for further improvement.

[0005] The object of the present invention has been made in consideration of the above-mentioned problems, and is to provide a vehicle control device that can detect targets existing around the vehicle from detected point cloud data, compared to conventional devices. [Means for solving the problem]

[0006] In order to solve the above-mentioned problems and achieve the objective, the vehicle control device of the present invention is a vehicle control device equipped with a ranging sensor that detects a surrounding point cloud, which is a point cloud of characteristic points around the host vehicle, and is equipped with a generation means for generating partitioning information that partitions the surrounding point cloud into specified areas based on surrounding point cloud information indicating the surrounding point cloud detected by the ranging sensor, and an identification means for identifying the surrounding point cloud included in the partitioning information as a detection target existing around the host vehicle when the proportion of the surrounding point cloud existing in the specified area of ​​the partitioning information exceeds a specified proportion.

[0007] According to this configuration, the vehicle control device divides the surrounding point cloud detected by the distance measurement sensor into a predetermined area, and when the proportion of the surrounding point cloud exceeds a predetermined proportion, identifies the detected object as a detection target existing around the vehicle. Therefore, the vehicle control device can identify the detected object of the surrounding point cloud as a detection target regardless of the size of the detected object. Furthermore, even if the detected object is a roadside tree of the same height as a person, the vehicle control device can distinguish between the roadside tree and the person and identify the detected object as a detection target. Therefore, the vehicle control device can detect objects existing around the vehicle from the detected point cloud data more effectively than in the past.

[0008] The surrounding point cloud is located in a three-axis orthogonal coordinate system with the position of the vehicle as the origin, and the generating means converts the surrounding point cloud located in three-dimensional coordinates in the three-axis orthogonal coordinate system into two-dimensional coordinates corresponding to a two-axis orthogonal coordinate system with the position of the vehicle as the origin by deleting the coordinate axis in the vehicle travel direction from the three-axis orthogonal coordinate system, and generates the partitioning information. As a result, the vehicle control device converts the coordinate system of the surrounding point cloud from three-dimensional coordinates to two-dimensional coordinates in the three-axis orthogonal coordinate system. As a result, the vehicle control device can reduce the processing time for determining targets existing around the vehicle from the detected point cloud data compared to the conventional method.

[0009] Furthermore, the vehicle control device includes a determination means for determining that the target object is an obstacle to the vehicle when the feature point of the target object exists in the vehicle travel direction and exists within the vehicle area set based on the vehicle width and height of the vehicle, and the determination means determines that the target object is not an obstacle to the vehicle when the feature point of the target object exists in the vehicle travel direction and does not exist within the vehicle area. The predetermined ratio is 50%. As a result, for example, when the target object is a road structure outside the vehicle path, the vehicle control device determines that the target object that does not exist within the vehicle area is not an obstacle even if a part of the target object overlaps with the vehicle path. Therefore, the vehicle control device can reduce the possibility of the vehicle being stopped erroneously due to erroneous detection of targets existing around the vehicle. Effect of the Invention

[0010] According to the present invention, compared to the prior art, targets existing around a vehicle can be detected from detected point cloud data. [Brief description of the drawings]

[0011] [Figure 1] FIG. 1 is a block diagram showing an example of a system configuration of a vehicle in which a vehicle control device according to an embodiment is mounted. [Diagram 2] FIG. 2 is a block diagram illustrating an example of a functional configuration of an autonomous driving ECU of a vehicle according to an embodiment. [Diagram 3] FIG. 3 is a block diagram illustrating an example of a functional configuration of an autonomous driving ECU of a vehicle according to an embodiment. [Figure 4] FIG. 4 is a schematic diagram for explaining the processing performed by the autonomous driving ECU of the vehicle according to the embodiment. [Diagram 5] FIG. 5 is a schematic diagram for explaining the processing performed by the autonomous driving ECU of the vehicle according to the embodiment. [Figure 6] FIG. 6 is a schematic diagram for explaining the processing performed by the autonomous driving ECU of the vehicle according to the embodiment. [Figure 7] FIG. 7 is a flowchart showing an example of the flow of operation of the autonomous driving ECU of the vehicle according to the embodiment. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0012] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. The configuration of the embodiment described below and the actions and effects brought about by the configuration are merely examples, and the present invention is not limited to the following description.

[0013] 1 is a block diagram showing an example of a system configuration of a vehicle 1 equipped with a vehicle control device according to an embodiment. The vehicle 1 is equipped with an automatic driving function, and is capable of traveling by automatic driving without the driving operation of a user (driver). Note that automatic driving includes semi-automatic driving in which some of the operations for traveling of the vehicle 1 are automated (requiring partial driving operation by the user).

[0014] A plurality of ECUs (Electronic Control Units) are mounted on the vehicle 1 to control various parts. Each ECU has a microcontroller unit (microcomputer), and the microcomputer has built-in, for example, a CPU (Central Processing Unit), a non-volatile memory such as a flash memory, and a volatile memory such as a DRAM (Dynamic Random Access Memory).

[0015] The multiple ECUs include a drive ECU 11, a steering ECU 12, a brake ECU 13, a meter ECU 14, and a body ECU 15. The drive ECU 11, the steering ECU 12, the brake ECU 13, the meter ECU 14, and the body ECU 15 are connected to each other so as to be capable of communication according to a CAN (Controller Area Network) communication protocol, that is, CAN communication.

[0016] The drive ECU 11 is a control unit that controls a drive device 21 of the vehicle 1. The drive device 21 may be configured to include an engine as a drive source, a motor as a drive source, or both an engine and a motor as drive sources. The drive device 21 includes a transmission that changes the speed of the drive force from the drive source and outputs it as necessary.

[0017] The steering ECU 12 is a control unit that controls the steering device 22 of the vehicle 1. The steering device 22 is, for example, an electric power steering device that applies the torque of an electric motor to a steering mechanism. The steering mechanism includes, for example, a rack-and-pinion steering gear, and is configured such that when a rack shaft moves in the vehicle width direction due to the torque of the electric motor, the left and right steered wheels are turned left and right in accordance with the movement of the rack shaft.

[0018] The brake ECU 13 is a control unit that controls the braking device 23 of the vehicle 1. The braking device 23 may be of a hydraulic or electric type. The hydraulic braking device 23 includes a brake actuator, and the function of this brake actuator distributes hydraulic pressure to wheel cylinders of the brakes provided on each wheel, and the hydraulic pressure drives the brakes to the driving wheels. A braking force is applied to the wheels including the

[0019] The meter ECU 14 is a control unit that controls each part of a meter panel (not shown) of the vehicle 1. The meter panel is provided with indicators such as a liquid crystal display for displaying various information, in addition to instruments for displaying the vehicle speed and engine RPM. An emergency stop switch 24 that is operated to issue an instruction to emergency stop the autonomous driving is also connected to the meter ECU 14.

[0020] The body ECU 15 is a control unit that controls various parts that need to operate even when the ignition switch of the vehicle 1 is in the off state, such as the left and right turn signals and the door lock motors.

[0021] The plurality of ECUs also include an autonomous driving ECU 31, a lidar ECU 32, and a monocular camera ECU 33 as control units for the autonomous driving function.

[0022] The automatic driving ECU 31 is a control center for automatic driving control. The automatic driving ECU 31 is an example of a vehicle control device. The automatic driving ECU 31 is connected to the drive ECU 11, the steering ECU 12, the brake ECU 13, the meter ECU 14, and the body ECU 15 so as to be able to communicate with them via CAN.

[0023] An omnidirectional lidar (LiDAR: Light Detection And Ranging) 34 is connected to the autonomous driving ECU 31 via, for example, a communication cable conforming to the Ethernet (registered trademark) standard. The omnidirectional lidar 34 is an example of a "distance measurement sensor" capable of acquiring distance measurement information indicating the distance from the vehicle 1 to an object present around the vehicle 1. The omnidirectional lidar 34 irradiates laser light in all directions of 360°, receives reflected light from an object present within a search range with an optical sensor, and outputs a detection signal according to the reflected light as distance measurement information. The distance measurement information may take the form of, for example, point cloud information indicating the distance from the vehicle 1 to the object at each position (voxel) in a three-dimensional space. The detection signal of the omnidirectional lidar 34 is input to the autonomous driving ECU 31.

[0024] Further, the automatic driving ECU 31 is connected to a GPS receiver 35 via, for example, a communication cable conforming to the USB (Universal Serial Bus) standard. The GPS receiver 35 is a receiver that receives positioning signals from GPS (Global Positioning System) satellites. The GPS receiver 35 is an example of a "positioning sensor" that can acquire positioning information indicating a position on the earth (e.g., latitude, longitude, altitude, etc.). The positioning signal received by the GPS receiver 35 is input from the GPS receiver 35 to the automatic driving ECU 31 as positioning information.

[0025] The LIDAR ECU 32 is connected to the autonomous driving ECU 31 so as to be able to communicate with it via, for example, an Ethernet standard communication cable. Six LIDARs 36 are connected to the LIDAR ECU 32. The LIDAR 36 irradiates a search range with a laser light, receives reflected light from an object present within the search range with an optical sensor, and outputs a detection signal according to the reflected light. The LIDARs 36 are arranged, for example, at the left end, center, and right end of the front bumper of the vehicle 1 and the left end, center, and right end of the rear bumper. The LIDARs 36 are an example of a distance measuring sensor. The LIDAR ECU 32 receives a detection signal output from each LIDAR 36. The LIDAR ECU 32 processes the detection signal output from each LIDAR 36 and transmits data obtained by the processing to the autonomous driving ECU 31.

[0026] The monocular camera ECU 33 is communicatively connected to the autonomous driving ECU 31 via, for example, a USB standard communication cable. A monocular camera 37 is connected to the monocular camera ECU 33. The monocular camera 37 is a camera capable of continuously capturing still images of a search range in front of the vehicle 1 at a predetermined frame rate. An image signal of a still image continuously output from the monocular camera 37 is input to the monocular camera ECU 33. The monocular camera ECU 33 processes the image signal input from the monocular camera 37, and transmits image data obtained by the processing to the autonomous driving ECU 31.

[0027] Fig. 2 is a block diagram showing an example of a functional configuration of the autonomous driving ECU 31 of the vehicle 1 according to the embodiment. The autonomous driving ECU 31 of the present embodiment includes an object recognition unit 41, a self-position estimation unit 42, a surrounding information integration unit 43, a route planning unit 44, and a vehicle control unit 45. These functional units 41 to 45 are configured by cooperation between hardware and software (programs, etc.) constituting the vehicle 1 as exemplified in Fig. 1. At least one of these functional units 41 to 45 may be configured by dedicated hardware (circuits, etc.). Note that the functional configuration of the autonomous driving ECU 31 is not limited to this.

[0028] The object recognition unit 41 recognizes targets such as other vehicles and pedestrians around the vehicle 1 from information on the distance to the targets (vehicles, pedestrians, buildings, curbs, and other obstacles) obtained from the detection signal of the omnidirectional lidar 34 and from images captured by the monocular camera 37.

[0029] For example, the object recognition unit 41 recognizes targets by performing the following processes. Specifically, the object recognition unit 41 performs a ground removal process to remove point clouds due to reflection from the ground from the point cloud data of the omnidirectional lidar 34. The object recognition unit 41 also performs a clustering process to group the point clouds into points that are close to each other. Furthermore, the object recognition unit 41 performs a boxing process to fit the grouped point clouds into a rectangular parallelepiped shape in order to recognize them as target objects to be detected that exist around the vehicle.

[0030] Then, the object recognition unit 41 performs a tracking process to calculate the relative speed of the target object by monitoring the boxed rectangular parallelepiped shape. This allows the object recognition unit 41 to recognize the target object. Note that the boxed information that fits the grouped point cloud into the rectangular parallelepiped shape is also called surrounding point cloud information that indicates the surrounding point cloud.

[0031] The self-position estimation unit 42 matches the point cloud data acquired from the detection signal of the omnidirectional LIDAR 34 with high-precision map data (point cloud data) 46, which is data of a high-precision map, to estimate the position (self-position) of the vehicle 1. The high-precision map is a high-precision three-dimensional map, and the high-precision map data 46 includes information such as the width and gradient of the road, and information on features such as dividing lines, shoulder lines, intersections, railroad crossings, stop lines, pedestrian crossings, and signs.

[0032] The high-precision map data 46 may be stored in a non-volatile memory built into the microcomputer of the automatic driving ECU 31, or may be stored in an HDD (Hard Disk Drive) or the like connected to the automatic driving ECU 31. Furthermore, the self-position estimation unit 42 integrates the self-position estimated by matching the point cloud data with the self-position based on the positioning signal received by the GPS receiver 35 to improve the estimation accuracy of the self-position.

[0033] The surrounding information integrating unit 43 receives as input the target recognition result by the object recognition unit 41, the self-position estimation result by the self-position estimation unit 42, and data obtained by processing detection signals output from each LIDAR 36 by the LIDAR ECU 32 (see FIG. 1). High-precision map data 46 is also input to the surrounding information integrating unit 43. The surrounding information integrating unit 43 creates surrounding information integrated map data in which targets such as the vehicle 1, vehicles other than the vehicle 1, and pedestrians are arranged on a high-precision map. The surrounding information integrating unit 43 then outputs the map information and object recognition information to an HMI device 47 (Human Machine Interface) such as a display arranged in the interior of the vehicle 1.

[0034] The route planning unit 44 receives the peripheral information integrated map data from the peripheral information integrating unit 43. The route planning unit 44 plans a travel route to the destination of the vehicle 1 from the input peripheral information integrated map data. The travel route includes the course of the vehicle 1 and a target vehicle speed at each point on the course. The route planning unit 44 outputs route data of the planned travel route to the HMI device 47.

[0035] The vehicle control unit 45 receives route data from the route planning unit 44. Based on the route data, the vehicle control unit 45 outputs commands to ECUs that control the operation of each part of the vehicle 1, such as the drive ECU 11, the steering ECU 12, and the brake ECU 13, so that the vehicle 1 travels by automatic driving along the travel route.

[0036] For example, after the destination of vehicle 1 is input on HMI device 47, an automatic driving start button displayed on HMI device 47 is pressed, whereby an instruction to start automatic driving is input from HMI device 47 to automatic driving ECU 31. When an instruction to start automatic driving is input to automatic driving ECU 31, a travel route to the destination of vehicle 1 is planned by route planner 44. The travel route is re-planned at a predetermined cycle while vehicle 1 is traveling by automatic driving.

[0037] Then, the vehicle 1 travels along the most recently planned travel route at the target vehicle speed for each point on the course included in the travel route. The autonomous driving ends, for example, when the vehicle 1 arrives at the destination or the emergency stop switch 24 is pressed and an instruction to stop the autonomous driving is input from the meter ECU 14 to the autonomous driving ECU 31.

[0038] However, since the autonomous driving ECU 31 determines whether an object is a target from point cloud data, for example, if a structure (such as a street lamp, utility pole, roadside tree, etc.) existing on the side of the road is approximately the same size as a human, the structure may be determined to be a human, so there is room for further improvement. Therefore, the autonomous driving ECU 31 of this embodiment has the functions shown in FIG. 3.

[0039] 3 is a block diagram showing an example of a functional configuration of the automatic driving ECU 31 according to the embodiment. For example, the surrounding information integration unit 43 included in the automatic driving ECU 31 includes a generating means 431, a first determining means 432, a specifying means 433, a second determining means 434, a third determining means 435, and a deciding means 436. Note that the functions included in the surrounding information integration unit 43 are not limited to these. In addition, in this embodiment, these functional units 431 to 436 are described as being included in the surrounding information integration unit 43, but other functional units of the automatic driving ECU 31 may include the functional units 431 to 436.

[0040] The generating means 431 generates partitioning information for partitioning the surrounding point cloud into predetermined regions based on surrounding point cloud information indicating the surrounding point cloud detected by the sensor. Specifically, the generating means 431 generates partitioning information for partitioning the surrounding point cloud into predetermined regions from the surrounding point cloud information indicating the surrounding point cloud processed by the object recognition unit 41. Here, the contents generated by the generating means 431 will be described with reference to Figs. 4 and 5. Figs. 4 and 5 are schematic diagrams for explaining the contents processed by the automatic driving ECU 31 of the vehicle 1. The schematic diagrams shown in Figs. 4 and 5 are diagrams for explaining the contents of the processing performed by the generating means 431 of the automatic driving ECU 31.

[0041] FIG. 4 shows a plurality of detected targets existing around the vehicle. The detected targets are, for example, a street lamp 51, a roadside tree 52, and a pedestrian 53. Here, the detected targets are targets detected by the omnidirectional lidar 34. The street lamp 51, the roadside tree 52, and the pedestrian 53 shown in FIG. 4 include surrounding point cloud information indicating a surrounding point cloud, which is a point cloud of characteristic points. Furthermore, the detected targets shown in FIG. 4 are located in a three-axis orthogonal coordinate system with the vehicle position as the origin. Here, the X-axis direction is the width direction of the vehicle. The Y-axis direction is the traveling direction of the vehicle. The Z-axis direction is the height direction of the vehicle.

[0042] First, the generating means 431 performs a process of surrounding the periphery of the surrounding point cloud in a rectangular shape for the surrounding point cloud information indicating the surrounding point cloud detected by the sensor. Specifically, the generating means 431 performs a process of surrounding the periphery of the surrounding point cloud located in three-dimensional coordinates in a three-axis orthogonal coordinate system by deleting the coordinate axis (Y-axis direction) in the vehicle travel direction in the three-axis orthogonal coordinate system, converting the surrounding point cloud into two-dimensional coordinates corresponding to a two-axis orthogonal coordinate system with the position of the host vehicle as the origin, and surrounding the periphery of the surrounding point cloud in a rectangular shape. The generating means 431 generates a first rectangular shape 511, a second rectangular shape 521, and a third rectangular shape 531 by surrounding the detected target as shown in FIG. 4 and FIG. 5.

[0043] Further, the generating means 431 generates partitioning information for partitioning the surrounding point group into predetermined regions from the generated rectangular shape. For example, the generating means 431 divides the generated rectangular shape into a lattice shape by dividing the predetermined region into 0.1 [m]. The generating means 431 then generates the partitioning information by expressing a point as "1" when a point exists in the lattice shape and a point as "0" when a point does not exist in the lattice shape. FIG. 5 shows the first partition information 54, the second partition information 55, and the third partition information 56 generated by the generating means 431. The first partition information 54 corresponds to the first rectangular shape 511. The second partition information 55 corresponds to the second rectangular shape 521. The third partition information 56 corresponds to the third rectangular shape 531. Note that the predetermined region is not limited to this.

[0044] Returning to Fig. 3, the first determination means 432 determines whether the proportion of surrounding point clouds in a predetermined region of the partitioning information exceeds a predetermined proportion. Specifically, the first determination means 432 determines whether the proportion of point clouds included in the partitioning information in a predetermined region of the partitioning information generated by the generation means 431 is a predetermined proportion. Here, the predetermined proportion is approximately 50%. The degree of the predetermined proportion is not limited to this.

[0045] When the ratio of the surrounding point group in the predetermined region of the partitioning information exceeds a predetermined ratio, the identification means 433 identifies the surrounding point group included in the partitioning information as a detection target existing around the host vehicle. Specifically, when the first determination means 432 determines that the ratio of the surrounding point group in the predetermined region of the partitioning information exceeds a predetermined ratio, the identification means 433 identifies the surrounding point group included in the partitioning information as a detection target existing around the host vehicle. Also, when the first determination means 432 determines that the ratio of the surrounding point group in the predetermined region of the partitioning information is below a predetermined ratio, the identification means 433 excludes the surrounding point group included in the partitioning information from the detection target existing around the host vehicle.

[0046] For example, assuming that the predetermined ratio is 50%, the ratio of surrounding point clouds in the predetermined area of ​​the first division information 54 and the second division information 55 shown in Fig. 5 is lower than the predetermined ratio. Therefore, the identification means 433 excludes the first division information 54 and the second division information 55 from the detection target existing around the host vehicle. On the other hand, the ratio of surrounding point clouds in the predetermined area of ​​the third division information 56 shown in Fig. 5 is higher than the predetermined ratio. Therefore, the identification means 433 identifies the third division information 56 as the detection target existing around the host vehicle.

[0047] 3, the second determination means 434 determines whether the feature point of the detection target exists on the travel route of the host vehicle. Specifically, the second determination means 434 determines whether the feature point of the detection target identified by the identification means 433 exists on the travel route of the host vehicle.

[0048] The third determination means 435 determines whether the feature point of the detection target exists within the host vehicle area set based on the vehicle width and height of the host vehicle. Specifically, the third determination means 435 determines whether the feature point of the detection target existing on the travel route of the host vehicle determined by the second determination means 434 exists within the host vehicle area set based on the vehicle width and height of the host vehicle. Here, the contents determined by the third determination means 435 will be described with reference to FIG. 6. The schematic diagram shown in FIG. 6 is a diagram for explaining the contents of the process performed by the third determination means 435 of the autonomous driving ECU 31.

[0049] 6 shows a host vehicle 61, a road 62, and a target 63 that exists around the host vehicle 61 and on a travel route of the host vehicle 61. The target 63 is a detected target detected by the omnidirectional lidar 34. Since the target 63 is a detected target, the generating means 431 generates partitioning information 631 corresponding to the target 63. Then, the identifying means 433 identifies the target 63 as a detected target that exists around the host vehicle 61 because the first determining means 432 determines that the proportion of the surrounding point group in a predetermined region of the partitioning information 631 is approximately 5.6%, which exceeds the predetermined proportion of 50%. In other words, the target 63 is a detected target.

[0050] Further, the sectionalization information 631 indicates a vehicle width 611 and a height 612 of the host vehicle 61. The third determination means 435 sets (superimposes) a host vehicle region 613, which is set from the vehicle width 611 and the height 612 of the host vehicle 61, in the sectionalization information 631. Then, the third determination means 435 determines whether a feature point of the detection target object (target object 63 shown in FIG. 6 ) exists within the host vehicle region 613, which is set from the vehicle width 611 and the height 612 of the host vehicle 61.

[0051] Here, the feature point of the detection target object included in the partitioning information 631 shown in Fig. 6 does not exist within the host vehicle region 613. In other words, in the positional relationship between the host vehicle 61, the road 62, and the target object 63 shown in Fig. 6, the third determination means 435 determines that the feature point of the detection target object (the target object 63 shown in Fig. 6) does not exist within the host vehicle region 613 set from the vehicle width 611 of the host vehicle 61 and the height 612 of the host vehicle 61.

[0052] Returning to Fig. 3, the determination means 436 determines that the detected object is a collidable obstacle. Specifically, the determination means 436 determines that the detected object, whose feature point is determined by the third determination means 435 to be present within the host vehicle area set from the host vehicle width and the host vehicle height, is a collidable obstacle. In other words, the collidable obstacle is an obstacle to the host vehicle.

[0053] Moreover, the determination means 436 determines that the detected object is a non-collidable obstacle. Specifically, the determination means 436 determines that the detected object, whose feature point is determined by the second determination means 434 not to exist on the travel path of the vehicle, is a non-collidable obstacle. Furthermore, the determination means 436 determines that the detected object, whose feature point is determined by the third determination means 435 not to exist within the vehicle region set from the vehicle width and height of the vehicle, is a non-collidable obstacle. Note that a non-collidable obstacle can be said to be a negligible detected object. In other words, a non-collidable obstacle can be said to be not an obstacle to the vehicle.

[0054] 7 is a flowchart showing an example of the flow of the operation of the autonomous driving ECU 31 of the vehicle 1 according to the embodiment. In particular, the flow of the process performed by the surrounding information integration unit 43 will be described here.

[0055] The generating means 431 generates partitioning information for partitioning the surrounding point cloud into predetermined regions based on the surrounding point cloud information indicating the surrounding point cloud detected by the sensor (step S1). Next, the first determining means 432 determines whether the ratio of the surrounding point cloud existing in the predetermined region of the partitioning information exceeds a predetermined ratio (step S2). Here, if the first determining means 432 determines whether the ratio of the surrounding point cloud existing in the predetermined region of the partitioning information is below a predetermined ratio (step S2: No), the process proceeds to step S3. On the other hand, if the first determining means 432 determines whether the ratio of the surrounding point cloud existing in the predetermined region of the partitioning information exceeds a predetermined ratio (step S2: Yes), the process proceeds to step S4.

[0056] In step S3, when the first determination means 432 determines that the ratio of the surrounding point group in the predetermined region of the partitioning information is lower than a predetermined ratio, the identification means 433 excludes the surrounding point group included in the partitioning information from the detection target existing around the vehicle (step S3). In step S4, when the ratio of the surrounding point group in the predetermined region of the partitioning information is higher than a predetermined ratio, the identification means 433 identifies the surrounding point group included in the partitioning information as the detection target existing around the vehicle (step S4).

[0057] In step S5, the second determination means 434 determines whether the feature point of the detection target exists on the travel route of the vehicle (step S5). If the second determination means 434 determines that the feature point of the detection target does not exist on the travel route of the vehicle (step S5: No), the process proceeds to step S8. On the other hand, if the second determination means 434 determines that the feature point of the detection target exists on the travel route of the vehicle (step S5: Yes), the process proceeds to step S6.

[0058] In step S6, the third determination means 435 determines whether the feature point of the detection target exists within the host vehicle region set based on the host vehicle width and height (step S6). If the third determination means 435 determines that the feature point of the detection target does not exist within the host vehicle region set based on the host vehicle width and height (step S6: No), the process proceeds to step S8. On the other hand, if the third determination means 435 determines that the feature point of the detection target exists within the host vehicle region set based on the host vehicle width and height (step S6: Yes), the process proceeds to step S7.

[0059] In step S7, the determining means 436 determines that the detected object is a collidable obstacle (step S7). In step S8, the determining means 436 determines that the detected object is a non-collidable obstacle (step S8). When this process ends, the surrounding information integrating unit 43 creates surrounding information integrated map data in which targets such as the vehicle 1, vehicles other than the vehicle 1, and pedestrians are arranged on a high-precision map.

[0060] As described above, according to the autonomous driving ECU 10 of the vehicle 1 in this embodiment, partitioning information is generated that partitions the surrounding point cloud into specified areas based on surrounding point cloud information indicating the surrounding point cloud detected by the ranging sensor, and if the proportion of the surrounding point cloud existing in the specified area of ​​the partitioning information exceeds a specified proportion, the surrounding point cloud included in the partitioning information is identified as a detection target existing around the vehicle.

[0061] As a result, the autonomous driving ECU 10 of the vehicle 1 divides the surrounding point cloud detected by the distance measurement sensor into a predetermined area, and when the ratio of the surrounding point cloud exceeds a predetermined ratio, identifies the detected target as a detection target existing around the vehicle. Therefore, the autonomous driving ECU 10 of the vehicle 1 can identify the target of the detected surrounding point cloud as a detection target regardless of the size of the detected target. Furthermore, even if the detected target is a roadside tree of the same height as a person, the autonomous driving ECU 10 of the vehicle 1 can distinguish between the roadside tree and the person and identify the detected target as a detection target. Therefore, the autonomous driving ECU 10 of the vehicle 1 can detect targets existing around the vehicle from the detected point cloud data more than before.

[0062] The surrounding point cloud is located in a three-axis Cartesian coordinate system with the position of the vehicle as the origin, and the autonomous driving ECU 10 of the vehicle 1 deletes the coordinate axis of the vehicle travel direction of the vehicle from the three-axis Cartesian coordinate system for the surrounding point cloud located in three-dimensional coordinates in the three-axis Cartesian coordinate system, converts the surrounding point cloud located in three-dimensional coordinates in the three-axis Cartesian coordinate system to two-dimensional coordinates corresponding to the two-axis Cartesian coordinate system with the position of the vehicle as the origin, and generates partition information. As a result, the autonomous driving ECU 10 of the vehicle 1 converts the coordinate system of the surrounding point cloud from three-dimensional coordinates to two-dimensional coordinates in the three-axis Cartesian coordinate system. Therefore, the autonomous driving ECU 10 of the vehicle 1 can reduce the processing time for determining targets existing around the vehicle from the detected point cloud data compared to the conventional method.

[0063] Furthermore, the autonomous driving ECU 10 of the vehicle 1 determines that the target object is an obstacle to the vehicle when the feature point of the target object exists in the vehicle travel direction and exists within the vehicle area set based on the vehicle width and height of the vehicle, and determines that the target object is not an obstacle to the vehicle when the feature point of the target object exists in the vehicle travel direction and does not exist within the vehicle area. The predetermined ratio is 50%. As a result, for example, when the target object is a road structure outside the vehicle route, the autonomous driving ECU 10 of the vehicle 1 determines that the target object that does not exist within the vehicle area is not an obstacle even if a part of the target object overlaps with the vehicle route. Therefore, the autonomous driving ECU 10 of the vehicle 1 can reduce the possibility of the vehicle being stopped erroneously due to erroneous detection of targets existing around the vehicle.

[0064] The program that causes a computer (e.g., the autonomous driving ECU 31, etc.) to execute processes for implementing various functions in the control device of the vehicle 1 as described above can be provided by recording it in an installable or executable format on a computer-readable recording medium such as a CD (Compact Disc)-ROM, a flexible disk (FD), a CD-R (Recordable), or a DVD (Digital Versatile Disk). The program may also be provided or distributed via a network such as the Internet. The program may also be provided by being pre-installed in a ROM, etc.

[0065] Although the embodiment of the present invention has been described above, the above-mentioned embodiment is presented as an example and is not intended to limit the scope of the present invention. This new embodiment can be implemented in various other forms. In addition, various omissions, substitutions, and modifications can be made without departing from the gist of the invention. In addition, this embodiment is included in the scope and gist of the invention, and is included in the scope of the invention and its equivalents described in the claims. [Explanation of symbols]

[0066] 1...vehicle, 11...drive ECU, 12...steering ECU, 13...brake ECU, 14...Meter ECU, 15...Body ECU, 24...Emergency stop switch, 31...Autonomous driving ECU, 32...LIDAR ECU, 33...Monocular camera ECU, 34 ... omnidirectional lidar, 35 ... GPS receiver, 36 ... lidar, 37 ... monocular camera, 41: object recognition unit, 42: self-location estimation unit, 43: surrounding information integration unit, 44: route planning unit, 45: vehicle control unit, 431: generation means, 432: first determination means, 433...Specifying means, 434...Second determining means, 435...Third determining means, 436...Determining means

Claims

1. A vehicle control device including a distance measuring sensor that detects a surrounding point cloud, which is a point cloud of characteristic points around a host vehicle, a generating means for generating partitioning information for partitioning the surrounding point cloud into predetermined regions based on surrounding point cloud information indicating the surrounding point cloud detected by the distance measuring sensor; an identification means for identifying the surrounding point cloud included in the partitioning information as a detection target existing around the vehicle when a ratio of the surrounding point cloud existing in the predetermined area of ​​the partitioning information exceeds a predetermined ratio; A vehicle control device comprising:

2. the surrounding point cloud is located in a three-axis Cartesian coordinate system having the position of the host vehicle as its origin, the generating means converts the surrounding point cloud located at three-dimensional coordinates in the three-axis orthogonal coordinate system into two-dimensional coordinates corresponding to a two-axis orthogonal coordinate system having an origin at the position of the host vehicle by deleting a coordinate axis in a vehicle traveling direction in the three-axis orthogonal coordinate system, and generates the partitioning information. The vehicle control device according to claim 1.

3. a determination means for determining that a feature point of the detection target object is an obstacle to the host vehicle when the feature point of the detection target object exists in the vehicle travel direction and within a host vehicle area that is set based on a vehicle width and a height of the host vehicle, the determining means determines that the feature point of the detection target object is not an obstacle to the host vehicle when the feature point of the detection target object exists in the vehicle travel direction and does not exist within the host vehicle area. The vehicle control device according to claim 2.

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