Mobile body control device, mobile body control method, and storage medium

Abstract

The present application provides a mobile body control device, a mobile body control method, and a storage medium that can more efficiently utilize information of an object target existing in the periphery of a mobile body. The mobile body control device includes: a recognition unit that recognizes a periphery situation of a mobile body; a risk area setting unit that sets, based on the recognized periphery situation, a risk area in which the mobile body should avoid traveling, among a plurality of distance areas centered on the mobile body; a target track generation unit that generates a target track indicating a path in which the mobile body will travel, based on the set risk area; and a travel control unit that causes the mobile body to travel along the generated target track, the risk area setting unit setting different kinds of risk areas according to the plurality of distance areas.

Description

TECHNICAL FIELD

[0001] The present application relates to a mobile body control device, a mobile body control method, and a storage medium. BACKGROUND

[0002] In the past, there has been known a technology of determining an obstacle existing in a traveling direction of a mobile body, and controlling the travel of the mobile body to avoid the determined obstacle. For example, in Japanese Patent Application Publication No. 2018-197048, it is described that a different cost is set to a surrounding area of a host vehicle based on image information captured by a camera mounted on the host vehicle, and a target trajectory of the host vehicle is generated in such a manner that the cost becomes smaller.

[0003] In the technology described in Patent Document 1, a cost is set to all obstacles mapped into image information, and is used for generation of a target trajectory of the host vehicle. However, depending on the distance from the mobile body and the condition of the mobile body, it is sometimes inefficient to generate a target trajectory taking into account information of all object targets existing in the surroundings of the mobile body. SUMMARY

[0004] The present application is completed in consideration of such a situation, and one of the objects thereof is to provide a mobile body control device, a mobile body control method, and a storage medium that can more efficiently utilize information of object targets existing in the surroundings of a mobile body.

[0005] The mobile body control device, the mobile body control method, and the storage medium of the present application adopt the following structure.

[0006] (1) One aspect of the present application relates to a mobile body control device, wherein the mobile body control device includes: a recognition unit that recognizes a surrounding condition of a mobile body; a risk area setting unit that sets, based on the recognized surrounding condition, a risk area in which the mobile body should avoid traveling, among a plurality of distance areas centered on the mobile body; a target trajectory generation unit that generates a target trajectory indicating a path in which the mobile body will travel in the future, based on the set risk area; and a travel control unit that causes the mobile body to travel along the generated target trajectory, and the risk area setting unit sets different kinds of risk areas according to the plurality of distance areas.

[0007] (2) The aspect of the above (1) is based on the aspect, and the plurality of distance areas include a first distance area in which a distance from the mobile body is equal to or less than a first threshold value, a second distance area in which the distance from the mobile body is greater than the first threshold value and equal to or less than a second threshold value, and a third distance area in which the distance from the mobile body is greater than the second threshold value and equal to or less than a third threshold value.

[0008] (3): In the scheme of the above (2), the risk region setting section sets, as a plurality of the risk regions, all kinds of object targets in the first distance region, sets object targets in the second distance region that have a possibility of collision when the target track is corrected, and sets object targets in the third distance region other than the running road boundary and the side wall of the lane in which the moving body runs.

[0009] (4): In the scheme of the above (3), in a case where the steering operation is performed on the driving operation member of the moving body, the risk region setting section excludes the running road boundary from the plurality of the risk regions.

[0010] (5): In any one of the schemes of the above (2) to (4), the target track generating section calculates a risk value of each of track points constituting the target track based on the risk region, and generates the target track in such a manner that the sum of the calculated risk values becomes equal to or less than a threshold value.

[0011] (6): In the scheme of the above (5), the target track generating section generates a first target track in the first distance region, a second target track in the second distance region, and a third target track in the third distance region, respectively, by a circular arc model including track points in which the sum of the risk values becomes equal to or less than a threshold value, and generates the target track by connecting the generated first target track, second target track, and third target track.

[0012] (7): In any one of the schemes of the above (2) to (4), the target track generating section calculates a risk value of each of track points constituting the target track based on the risk region, and generates the target track in such a manner that the sum of the calculated risk values becomes the minimum.

[0013] (8): In the scheme of the above (7), the target track generating section generates a first target track in the first distance region, a second target track in the second distance region, and a third target track in the third distance region, respectively, by a circular arc model including track points in which the sum of the risk values becomes the minimum, and generates the target track by connecting the generated first target track, second target track, and third target track.

[0014] (9): Another aspect of the present invention relates to a mobile body control method, wherein the mobile body control method causes a computer to perform the following processing: identifying the surrounding conditions of the mobile body; based on the identified surrounding conditions, setting risk areas that the mobile body should avoid traveling in a plurality of distance regions centered on the mobile body; generating a target track representing the future path of the mobile body based on the set risk areas; causing the mobile body to travel along the generated target track; and setting different types of risk areas according to the plurality of distance regions.

[0015] (10): Another aspect of the present invention relates to a storage medium storing a program, wherein the program causes a computer to perform the following processing: identifying the surrounding conditions of a moving body; based on the identified surrounding conditions, setting risk areas that the moving body should avoid traveling in a plurality of distance regions centered on the moving body; generating a target track representing the future path of the moving body based on the set risk areas; causing the moving body to travel along the generated target track; and setting different types of risk areas according to the plurality of distance regions.

[0016] Invention Effects

[0017] According to the schemes (1) to (10), by setting different types of object targets as risk zones based on their distance from the moving body, the information of object targets existing around the moving body can be utilized more efficiently to generate target trajectories. Attached Figure Description

[0018] Figure 1 This is a structural diagram of a vehicle system utilizing the mobile body control device of this embodiment.

[0019] Figure 2 This is a functional structure diagram of the first control unit and the second control unit.

[0020] Figure 3 This is a diagram used to illustrate the outline of the processes performed by the Risk Area Setting Department.

[0021] Figure 4 It is a graph that shows the relationship between distance regions and the types of object targets defined as risk regions.

[0022] Figure 5 This diagram illustrates an example of a target trajectory modified by the action plan generation department based on the setting of risk zones.

[0023] Figure 6 This is another example of a target trajectory that has been modified by the action plan generation department based on the setting of risk areas.

[0024] Figure 7 is a diagram for explaining a detailed method of the action plan generation unit generating the target trajectory.

[0025] Figure 8 is a diagram for explaining details of a method of searching for the target trajectory using a circular arc model.

[0026] Figure 9 is a flowchart showing an example of a flow of processing performed by the automatic driving control device. DETAILED DESCRIPTION

[0027] Embodiments of a mobile body control device, a mobile body control method, and a storage medium of the present application will be described below with reference to the accompanying drawings. The mobile body in the present application is a four-wheeled vehicle, a two-wheeled vehicle, a micro mobile body, a robot, or the like. In the following description, the mobile body is a four-wheeled vehicle.

[0028] [Overall Structure]

[0029] Figure 1 is a structural diagram of a vehicle system 1 that utilizes the mobile body control device of the present embodiment. The vehicle on which the vehicle system 1 is mounted is, for example, a two-wheeled, three-wheeled, four-wheeled, or the like vehicle, and its drive source is an internal combustion engine such as a diesel engine or a gasoline engine, an electric motor, or a combination thereof. The electric motor operates using generated electric power from a generator coupled to the internal combustion engine, or discharge electric power from a secondary battery or a fuel cell.

[0030] The vehicle system 1 is provided with, for example, a camera 10, a radar device 12, a LIDAR (Light Detection and Ranging) 14, an object recognition device 16, a communication device 20, an HMI (Human Machine Interface) 30, a vehicle sensor 40, a navigation device 50, an MPU (Map Positioning Unit) 60, a driving operation member 80, an automatic driving control device 100, a travel drive power output device 200, a brake device 210, and a steering device 220. These devices and apparatuses are connected to each other through a multiplex communication line such as a CAN (Controller Area Network) communication line, a serial communication line, a wireless communication network, or the like. Note that, Figure 1 The structure shown is only an example, and a part of the structure can be omitted, or another structure can be further added.

[0031] The camera 10 is, for example, a digital camera that uses a solid-state image pickup element such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor). The camera 10 is mounted at an arbitrary position of a vehicle (hereinafter referred to as the host vehicle M) on which the vehicle system 1 is mounted. In the case of capturing an image of the front, the camera 10 is mounted on the upper portion of the front windshield glass, the back surface of the interior rearview mirror, or the like. The camera 10 repeatedly captures the surroundings of the host vehicle M periodically, for example. The camera 10 can also be a stereo camera.

[0032] The radar device 12 radiates millimeter waves or the like toward the surroundings of the host vehicle M and detects a wave reflected by an object (reflected wave) to detect at least the position (distance and direction) of the object. The radar device 12 is mounted at an arbitrary position of the host vehicle M. The radar device 12 can also detect the position and speed of an object by an FM-CW (Frequency Modulated Continuous Wave) method.

[0033] The LIDAR 14 radiates light (or an electromagnetic wave having a wavelength close to light) toward the surroundings of the host vehicle M and measures scattered light. The LIDAR 14 detects the distance to an object on the basis of the time from light emission to light reception. The radiated light is, for example, pulsed laser light. The LIDAR 14 is mounted at an arbitrary position of the host vehicle M.

[0034] The object recognition device 16 performs sensor fusion processing on the detection results detected by some or all of the camera 10, the radar device 12, and the LIDAR 14 to recognize the position, type, speed, and the like of an object. The object recognition device 16 outputs the recognition result to the automatic driving control device 100. The object recognition device 16 can output the detection results of the camera 10, the radar device 12, and the LIDAR 14 directly to the automatic driving control device 100.

[0035] The communication device 20 communicates with other vehicles existing in the surroundings of the host vehicle M or communicates with various server devices via a wireless base station, for example, by using a cellular network, a Wi-Fi network, Bluetooth (registered trademark), DSRC (Dedicated Short Range Communication), or the like.

[0036] The HMI 30 prompts various information to the occupant of the host vehicle M and accepts an input operation by the occupant. The HMI 30 includes various display devices, a speaker, a buzzer, a touch panel, switches, buttons, and the like.

[0037] The vehicle sensors 40 include a vehicle speed sensor that detects the speed of the host vehicle M, an acceleration sensor that detects acceleration, a yaw rate sensor that detects the angular velocity about the vertical axis, a direction sensor that detects the orientation of the host vehicle M, and the like.

[0038] The navigation device 50 has, for example, a GNSS (Global Navigation Satellite System) receiver 51, a navigation HMI 52, and a route decision section 53. The navigation device 50 holds first map information 54 in a storage device such as a HDD (Hard Disk Drive) or a flash memory. The GNSS receiver 51 determines the position of the host vehicle M based on signals received from GNSS satellites. The position of the host vehicle M can also be determined or supplemented by an INS (Inertial Navigation System) that uses the outputs of the vehicle sensors 40. The navigation HMI 52 includes a display device, a speaker, a touch panel, a button, and the like. The navigation HMI 52 can also be partly or wholly shared with the aforementioned HMI 30. The route decision section 53 decides a route (hereinafter referred to as an on-map route) from the position of the host vehicle M determined by the GNSS receiver 51 (or an arbitrary position input) to a destination input by an occupant using the navigation HMI 52, for example, with reference to the first map information 54. The first map information 54 is, for example, information that represents the shape of a road by road segments and nodes that connect the road segments. The first map information 54 can also include the curvature of a road, POI (Point Of Interest) information, and the like. The on-map route is output to the MPU 60. The navigation device 50 can also perform route guidance using the navigation HMI 52 based on the on-map route. The navigation device 50 can also be realized by the functions of a terminal device such as a smartphone or a tablet terminal held by an occupant. The navigation device 50 can also transmit the current position and the destination to a navigation server via the communication device 20 and acquire a route equivalent to the on-map route from the navigation server.

[0039] The MPU 60 includes, for example, a recommended lane decision section 61 that holds second map information 62 in a storage device such as a HDD or a flash memory. The recommended lane decision section 61 divides the on-map route provided from the navigation device 50 into a plurality of blocks (for example, every 100 [m] in the direction of travel of the vehicle) and decides a recommended lane for each block with reference to the second map information 62. The recommended lane decision section 61 performs the decision of the lane on which to travel, such as the lane on the left. The recommended lane decision section 61 decides the recommended lane so that the host vehicle M can travel on a reasonable route to a branched destination in the case where there is a branch point in the on-map route.

[0040] The second map information 62 is map information having higher precision than the first map information 54. The second map information 62 includes, for example, information on the center of a lane or information on the boundary of a lane, and the like. In addition, the second map information 62 can include road information, traffic restriction information, dwelling information (dwelling, postal code), facility information, telephone number information, and the like. The second map information 62 can be updated at any time by the communication device 20 communicating with other devices.

[0041] The driving operation member 80 includes, for example, an accelerator pedal, a brake pedal, a shift lever, a steering wheel, a special-shaped steering wheel, a joystick, and other operation members. A sensor that detects an operation amount or the presence or absence of an operation is installed in the driving operation member 80, and the detection result is output to some or all of the automatic driving control device 100, or the travel driving force output device 200, the brake device 210, and the steering device 220.

[0042] The automatic driving control device 100 includes, for example, a first control section 120 and a second control section 160. The first control section 120 and the second control section 160 are each realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). In addition, some or all of these components can be realized by hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), a GPU (Graphics Processing Unit), and can also be realized by a combination of software and hardware. The program can be stored in advance in a storage device (a storage device including a non-transitory storage medium) such as an HDD or a flash memory of the automatic driving control device 100, can be stored in a removable storage medium such as a DVD or a CD-ROM, and can be installed in the HDD or the flash memory of the automatic driving control device 100 by mounting the storage medium (non-transitory storage medium) in a drive device.

[0043] Figure 2This is a functional structure diagram of the first control unit 120 and the second control unit 160. The first control unit 120, for example, includes an identification unit 130 and an action plan generation unit 140. The first control unit 120, for example, implements AI (Artificial Intelligence) based functions and functions based on pre-given models in parallel. For example, the function of "identifying intersections" can be achieved by "parallel execution of intersection identification based on deep learning, etc., and identification based on pre-given conditions (the existence of signals capable of pattern matching, road signs, etc.), and comprehensively evaluating both sides by scoring them." This ensures the reliability of autonomous driving.

[0044] The identification unit 130 identifies the position, speed, and acceleration of objects surrounding the vehicle M based on information input from the camera 10, radar device 12, and LIDAR 14 via the object identification device 16. The position of an object is identified, for example, as its position on absolute coordinates with a representative point of the vehicle M (center of gravity, drive shaft center, etc.) as the origin, and is used for control. The position of an object can also be represented by representative points such as its center of gravity or corners, or by a defined area. The "state" of an object can also include its acceleration, jerk, or "action state" (e.g., whether it is changing lanes or intends to change lanes).

[0045] Additionally, the identification unit 130 identifies, for example, the lane in which the vehicle M is traveling (driving lane). For instance, the identification unit 130 identifies the driving lane by comparing the pattern of road markings (e.g., an arrangement of solid and dashed lines) obtained from the second map information 62 with the pattern of road markings surrounding the vehicle M identified from the image captured by the camera 10. It should be noted that the identification unit 130 is not limited to road markings; it can also identify driving lanes by identifying road markings, including road shoulders, curbs, median strips, guardrails, and other road boundaries (road boundaries). In this identification, the position of the vehicle M obtained from the navigation device 50 and the processing results from the INS may also be taken into consideration. Furthermore, the identification unit 130 identifies temporary stop lines, obstacles, red lights, toll booths, and other road phenomena.

[0046] When identifying a driving lane, the identification unit 130 identifies the position and posture of the vehicle M relative to the driving lane. For example, the identification unit 130 may identify the deviation of the reference point of the vehicle M from the center of the lane, and the angle formed by the direction of travel of the vehicle M relative to the line connecting the centers of the lanes, as the relative position and posture of the vehicle M relative to the driving lane. Alternatively, the identification unit 130 may identify the position of the reference point of the vehicle M relative to any side end (road dividing line or road boundary) of the driving lane as the relative position of the vehicle M relative to the driving lane. In this embodiment, the identification unit 130 includes a risk area setting unit 132, but details of the function of the risk area setting unit 132 will be described later.

[0047] The action plan generation unit 140 generates a target trajectory for the future automatic (driver-independent) travel of the vehicle M, in a manner that allows it to travel in the recommended lane determined by the recommended lane determination unit 61 and is able to cope with the surrounding conditions of the vehicle M. The target trajectory includes, for example, a speed element. For instance, the target trajectory is represented by a track of locations (track points) that the vehicle M should reach sequentially. Track points are locations that the vehicle M should reach at predetermined travel distances (e.g., a few meters), but target speeds and target accelerations are generated as part of the target trajectory at predetermined sampling times (e.g., a few tenths of a second). Alternatively, track points can also be positions that the vehicle M should reach at predetermined sampling times. In this case, the target speed and target acceleration information are represented by the intervals between track points.

[0048] When generating a target track, the action plan generation unit 140 can set events for automatic driving. These events include constant speed driving events, low-speed following events, lane change events, branching events, merging events, and takeover events. The action plan generation unit 140 generates target tracks corresponding to the initiated events.

[0049] The second control unit 160 controls the driving force output device 200, the braking device 210 and the steering device 220 so that the vehicle M passes through the target track generated by the action plan generation unit 140 at a predetermined time.

[0050] return Figure 2The second control unit 160 includes, for example, an acquisition unit 162, a speed control unit 164, and a steering control unit 166. The acquisition unit 162 acquires information about the target track (track point) generated by the action plan generation unit 140 and stores this information in a memory (not shown). The speed control unit 164 controls the driving force output device 200 or the braking device 210 based on the speed elements associated with the target track stored in the memory. The steering control unit 166 controls the steering device 220 based on the curvature of the target track stored in the memory. The processing of the speed control unit 164 and the steering control unit 166 is achieved, for example, through a combination of feedforward control and feedback control. As an example, the steering control unit 166 combines feedforward control corresponding to the curvature of the road ahead of the vehicle M with feedback control based on deviation from the target track.

[0051] The driving force output device 200 outputs driving force (torque) for vehicle movement to the drive wheels. The driving force output device 200 includes, for example, a combination of an internal combustion engine, an electric motor, and a transmission, as well as an ECU (Electronic Control Unit) that controls them. The ECU controls the above-mentioned structure according to information input from the second control unit 160 or from the driving operation device 80.

[0052] The braking device 210 includes, for example, a brake caliper, a hydraulic cylinder that transmits hydraulic pressure to the brake caliper, an electric motor that generates hydraulic pressure in the hydraulic cylinder, and a braking ECU. The braking ECU controls the electric motor according to information input from the second control unit 160 or from the driving control unit 80, so that braking torque corresponding to the braking operation is output to each wheel. The braking device 210 may include, as a backup, a mechanism for transmitting hydraulic pressure generated by the operation of the brake pedal included in the driving control unit 80 via the master hydraulic cylinder to the hydraulic cylinder. It should be noted that the braking device 210 is not limited to the structure described above; it may also be an electronically controlled hydraulic braking device that controls the actuator according to information input from the second control unit 160, thereby transmitting hydraulic pressure from the master hydraulic cylinder to the hydraulic cylinder.

[0053] The steering system 220 includes, for example, a steering ECU and an electric motor. The electric motor applies force to a rack and pinion mechanism to change the direction of the steering wheels. The steering ECU drives the electric motor to change the direction of the steering wheels according to information input from the second control unit 160 or from the driving operation unit 80.

[0054] [action]

[0055] Next, the process performed by the risk area setting unit 132 will be explained. Figure 3 This is a diagram illustrating the outline of the processes performed by the risk area setting unit 132. Figure 3In the attached figures, reference numeral DR1 indicates the first distance region within a radius r1 centered on the vehicle M; reference numeral DR2 indicates the second distance region within a radius r2 centered on the vehicle M, which is larger than radius r1; reference numeral DR3 indicates the third distance region within a radius r3 centered on the vehicle M, which is larger than radius r2; reference numeral B indicates a bicycle as a traffic participant; reference numeral RA indicates a risk area set by the risk area setting unit 132; reference numeral P indicates a pedestrian as a traffic participant; and reference numeral L indicates a road dividing line (driving road boundary). Figure 3 The following is an example of a scenario where the vehicle M is traveling along the target track TT1 generated by the action plan generation unit 140. The radius r1 of the first distance region DR1 is an example of a "first threshold", the radius r2 of the second distance region DR2 is an example of a "second threshold", and the radius r3 of the third distance region DR3 is an example of a "third threshold".

[0056] Based on the surrounding conditions of the vehicle M identified by the identification unit 130, the risk area setting unit 132 sets risk areas RA that the vehicle M should avoid driving in for each of the multiple distance areas DR (i.e., the first distance area DR1, the second distance area DR2, and the third distance area DR3 in this embodiment) centered on the vehicle M. More specifically, the risk area setting unit 132 identifies different types of object targets for each distance area and sets a risk value R, which is larger the closer the target is to the identified object (i.e., a negative value), in the surrounding area of ​​that object target, thereby setting it as a risk area RA. Figure 3 In this case, the risk zone setting unit 132 sets the bicycle B and the road dividing line L as the risk zone RA in the first distance zone DR1. In the risk zone RA, the darker colored areas indicate a higher risk value, and the lighter colored areas indicate a lower risk value.

[0057] Figure 4 This is a graph showing the relationship between distance regions and the types of objects designated as risk regions. For example... Figure 4As shown, the risk zone setting unit 132 sets all types of object targets existing in the first distance zone DR1 as risk zones, sets object targets existing in the second distance zone DR2 except for road dividing lines L, and sets object targets existing in the third distance zone DR3 except for road dividing lines L and the side walls of the lane in which the vehicle M is traveling. That is, the first distance zone DR1 is a risk zone for avoiding collisions with all object targets close to the vehicle M, the second distance zone DR2 is a risk zone for avoiding collisions with object targets at a medium distance from the vehicle M that pose a secondary collision risk, and the third distance zone DR3 is a risk zone for allowing for advance avoidance of collisions with object targets at a greater distance from the vehicle M. Figure 4 In this example, objects other than road dividing lines L are set as objects with the possibility of secondary collision. However, the present invention is not limited to such a structure. Generally, any object with the possibility of secondary collision is acceptable, and the system administrator can set any object.

[0058] The action plan generation unit 140 calculates a risk value for each track point constituting the target trajectory. If the sum of the calculated risk values ​​exceeds a threshold, the target trajectory is adjusted to be below that threshold. For example, in... Figure 3 In the case of this, the action plan generation unit 140 calculates the risk values ​​for track points TP1 and TP2 that constitute the target track TT1. If the sum of the calculated risk values ​​is above a threshold, the target track TT1 is modified. When modifying the target track TT1, for example, a target track TT2 is generated where the track points do not intersect with the risk area RA (i.e., the risk value is zero).

[0059] However, generally, when the target trajectory is corrected so that the vehicle M travels along the corrected target trajectory, a secondary collision with an obstacle existing on the corrected target trajectory is possible. For example, in Figure 3 In the event that vehicle M travels along the corrected target trajectory TT2, a secondary collision with pedestrian P is possible. Given this scenario, as shown in reference... Figure 4 As explained, in this embodiment, the risk area setting unit 132 sets different types of object targets as risk areas RA for each distance area, and the action plan generation unit 140 generates the target track of the vehicle M based on the risk areas RA set for each distance area.

[0060] Figure 5 This diagram illustrates an example of a target trajectory modified by the action plan generation unit 140 based on the setting of the risk area RA. (See diagram below.) Figure 5As shown, the risk area setting unit 132 sets a risk area RA corresponding to the road dividing line L and bicycle B in the first distance area DR1, and sets a risk area RA corresponding to pedestrian P who has the possibility of secondary collision in the second distance area DR2. Therefore, the action plan generation unit 140, based on these set risk areas RA, shifts the track points in such a way that the sum of the risk values ​​of the track points constituting the target track is less than a threshold, thereby correcting the target track. Figure 5 As can be seen from the example, the target orbit TT3 was generated through modification.

[0061] Figure 6 This is another example of a target trajectory that has been modified by the action plan generation unit 140 based on the setting of the risk area RA. Figure 6 This indicates the following situation: with Figure 5 Unlike in the case where there is no pedestrian P, the risk area setting unit 132 only sets the risk area RA corresponding to the road dividing line L and the bicycle B. In this case, the risk area setting unit 132 does not set any object targets other than the road dividing line L in the second distance area DR2, and can more reliably avoid the bicycle B compared to the case where the road dividing line L is set as the risk area.

[0062] Next, refer to Figure 7 This will explain the detailed method for generating the target orbit. Figure 7 This is a diagram illustrating the detailed method by which the action plan generation unit 140 generates the target trajectory. Figure 7 In the diagram, TT3_1 represents the local target orbit generated with respect to the first distance region DR1, TT3_2 represents the local target orbit generated with respect to the second distance region DR2, and TT3_3 represents the local target orbit generated with respect to the third distance region DR3.

[0063] like Figure 7As shown, the action plan generation unit 140 uses an arc model to search for trajectory points whose sum of risk values ​​is less than a threshold for each distance region DR, and generates the searched arc models as local target trajectories. For example, regarding the first distance region DR1, the action plan generation unit 140 first changes the parameters of the arc model to make the sum of risk values ​​of the trajectory points less than the threshold, and generates the arc models with the sum of risk values ​​of the trajectory points less than the threshold as local target trajectories TT3_1. Next, regarding the second distance region DR2, the action plan generation unit 140 sets the end point of local target trajectories TT3_1 as the starting point, and changes the parameters of the arc model to make the sum of risk values ​​of the trajectory points less than the threshold, and generates the arc models with the sum of risk values ​​of the trajectory points less than the threshold as local target trajectories TT3_2. Next, regarding the second distance region DR3, the action plan generation unit 140 sets the end point of local target trajectories TT3_2 as the starting point, and changes the parameters of the arc model to make the sum of risk values ​​of the trajectory points less than the threshold, and generates the arc models with the sum of risk values ​​of the trajectory points less than the threshold as local target trajectories TT33.

[0064] Figure 8 This diagram illustrates in detail the method of using a circular arc model to search for a target trajectory. Figure 8 In the attached drawings, reference numerals TT3_1_C1, TT3_1_C2, and TT3_1_C3 denote the candidate circular arc models for the local target orbit TT3_1 (hereinafter, sometimes collectively referred to as "TT3_1_C"), and reference numerals TT3_2_C1 and TT3_1_C2 denote the candidate circular arc models for the local target orbit TT3_2 (hereinafter, sometimes collectively referred to as "TT3_2_C"). For example... Figure 8 As shown, the action plan generation unit 140 first determines the position of the vehicle M (within the first distance area DR1) based on the location of the vehicle M. Figure 8Using the center of the front end as a reference, the parameters of the circular arc model TT3_1_C (e.g., the endpoint and curvature of the arc) are varied. If the sum of the risk values ​​of the track points existing on the circular arc model TT3_1_C is less than a threshold, the circular arc model TT3_1_C is determined as the local target track TT3_1. The action plan generation unit 140 then sets the endpoint CP of the determined circular arc model TT3_1_C as the starting point, and varies the parameters of the circular arc model TT3_2_C so that the sum of the risk values ​​of the track points is less than a threshold. If the sum of the risk values ​​of the track points is less than the threshold, the circular arc model TT3_2_C is determined as the local target track TT3_2. Similarly, the action plan generation unit 140 generates a local target track TT3_3 starting from the endpoint CP of the determined circular arc model TT3_1_C. The generated local target trajectory TT3_1 in the first distance region DR1 can be considered a circular arc trajectory used to avoid all collision risks. The local target trajectory TT3_2 in the second distance region DR2 can be considered a circular arc trajectory used to avoid secondary collision risks. The local target trajectory TT3_3 in the third distance region DR3 can be considered a circular arc trajectory used to leave a margin for collision avoidance with the target object. It should be noted that, at this point, the action plan generation unit 140 can also perform a predetermined number of searches based on the circular arc model without using a threshold, determining the circular arc model that minimizes the sum of risk values ​​of the trajectory points as the local target trajectory.

[0065] The action plan generation unit 140 connects the generated local target trajectories TT3_1, TT3_2, and TT3_3 to generate the target trajectory TT3. At this time, the action plan generation unit 140 can also calculate the sum of the risk values ​​of the generated target trajectory TT3 and store it as a candidate target trajectory. Among the multiple candidate target trajectories obtained by performing the aforementioned local target trajectory search and connection process a predetermined number of times, the candidate target trajectory with the smallest sum of risk values ​​is determined as the final target trajectory. Furthermore, the action plan generation unit 140 can not only simply connect the generated local target trajectories TT3_1, TT3_2, and TT3_3, but also use a smooth curve to fit the trajectory, thereby obtaining the final target trajectory.

[0066] It should be noted that in the above-described embodiment, all types of object targets are set as the first distance zone DR1. However, for example, it is also possible that when an occupant of the vehicle M performs a steering operation on the driving control unit 80, the risk zone setting unit 132 excludes road marking lines from the set risk zone RA. This is because when an occupant of the vehicle M performs a steering operation on the driving control unit 80, a highly urgent situation may occur, and including road marking lines as risk zone RA is not a priority.

[0067] Furthermore, in the above embodiment, three distance regions are set as multiple distance regions: a first distance region DR1, a second distance region DR2, and a third distance region DR3. However, the present invention is not limited to that structure, and two or more distance regions may be set, with different types of object targets designated as risk areas for each distance region.

[0068] Next, refer to Figure 9 This will explain the processing flow performed by the automatic driving control device 100. Figure 9 This is a flowchart illustrating an example of the processing flow performed by the automatic driving control device 100.

[0069] First, the identification unit 130 identifies the surrounding conditions of the vehicle M based on the information input via the object recognition device 16 (step S100). Next, the risk area setting unit 132 sets a risk area RA for each distance area of ​​the identified surrounding conditions (step S102).

[0070] Next, the action plan generation unit 140 generates a circular arc for the local target track of the vehicle M for each distance region based on the risk region RA set by the risk region setting unit 132 (step S104). More specifically, the action plan generation unit 140 changes the parameters of the circular arc model so that the sum of the risk values ​​of each track point calculated based on the risk region RA is below a threshold, and determines the circular arc model in which the sum of the risk values ​​of the track points is below the threshold as the local target track.

[0071] Next, the action plan generation unit 140 generates a target track by connecting the generated local target tracks together (step S106). Then, the second control unit 160 causes the vehicle M to travel along the generated target track (step S108). Thus, the processing of this flowchart ends.

[0072] According to this embodiment as described above, different types of object targets are designated as risk zones based on their distance from the vehicle. For each distance zone, a local target track is generated as an arc such that the sum of the risk values ​​of each track point calculated based on the designated risk zone is below a threshold. By connecting the generated local target tracks, a target track is generated, and the vehicle travels along the generated target track. This allows for more efficient utilization of information about object targets existing around the moving vehicle.

[0073] The implementation methods described above can be performed as follows.

[0074] The moving body control device is configured to include:

[0075] A storage device containing a program; and

[0076] Hardware processor,

[0077] The hardware processor executes the program stored in the storage device to perform the following processing:

[0078] Identify the surroundings of the moving object;

[0079] Based on the identified surrounding conditions, risk areas that the mobile body should avoid traveling in are set in multiple distance regions centered on the mobile body;

[0080] Based on the defined risk area, a target track representing the future path of the mobile body is generated.

[0081] To make the moving body travel along the generated target trajectory; and

[0082] Different types of risk zones are defined based on the multiple distance zones.

[0083] The above description illustrates specific embodiments of the present invention, but the present invention is not limited to such embodiments in any way, and various modifications and substitutions can be made without departing from the spirit of the present invention.

Claims

1. A mobile body control device, wherein, The moving body control device includes: The identification unit identifies the surrounding environment of the moving object; The risk zone setting unit, based on the identified surrounding conditions and the identified object targets, sets risk zones that the moving body should avoid traveling in multiple distance zones centered on the moving body. The target trajectory generation unit generates a target trajectory representing the future path of the moving body based on the set risk area. as well as A travel control unit that causes the moving body to travel along the generated target track. The risk zone setting unit sets different types of object targets as risk zones based on the multiple distance zones.

2. The moving body control device according to claim 1, wherein, The plurality of distance regions include a first distance region where the distance from the moving body is below a first threshold, a second distance region where the distance from the moving body is greater than the first threshold and below a second threshold, and a third distance region where the distance from the moving body is greater than the second threshold and below a third threshold.

3. The moving body control device according to claim 2, wherein, The risk zone setting unit sets all types of object targets in the first distance zone as multiple risk zones, sets object targets in the second distance zone that have the possibility of collision when the target trajectory is corrected, and sets object targets in the third distance zone except for the driving road boundary and the side wall of the lane in which the mobile body travels.

4. The moving body control device according to claim 3, wherein, When the driving control unit of the moving body is steered, the risk area setting unit excludes the driving road boundary from the multiple risk areas.

5. The moving body control device according to any one of claims 2 to 4, wherein, The target trajectory generation unit calculates the risk value of each trajectory point constituting the target trajectory based on the risk area, and generates the target trajectory in such a way that the sum of the calculated risk values ​​is below a threshold.

6. The moving body control device according to claim 5, wherein, The target trajectory generation unit generates a first target trajectory in the first distance region, a second target trajectory in the second distance region, and a third target trajectory in the third distance region using an arc model that includes trajectory points where the sum of the risk values ​​is below a threshold. The unit then connects the generated first target trajectory, second target trajectory, and third target trajectory to generate the target trajectory.

7. The moving body control device according to any one of claims 2 to 4, wherein, The target trajectory generation unit calculates the risk value of each trajectory point constituting the target trajectory based on the risk area, and generates the target trajectory in a manner that minimizes the sum of the calculated risk values.

8. The moving body control device according to claim 7, wherein, The target trajectory generation unit generates a first target trajectory in the first distance region, a second target trajectory in the second distance region, and a third target trajectory in the third distance region using an arc model that includes trajectory points where the sum of the risk values ​​is minimized. The unit then connects the generated first target trajectory, second target trajectory, and third target trajectory to generate the target trajectory.

9. A method for controlling a moving body, wherein, The moving body control method causes the computer to perform the following processing: Identify the surroundings of the moving object; Based on the identified surrounding conditions and according to the identified object target, risk areas that the moving body should avoid traveling are set in multiple distance regions centered on the moving body. Based on the defined risk area, a target track representing the future path of the mobile body is generated. The moving body is made to travel along the generated target trajectory; as well as Based on the multiple distance regions, different types of object targets are designated as risk zones.

10. A storage medium storing a program, wherein, The program causes the computer to perform the following processing: Identify the surroundings of the moving object; Based on the identified surrounding conditions and according to the identified object target, risk areas that the moving body should avoid traveling are set in multiple distance regions centered on the moving body. Based on the defined risk area, a target track representing the future path of the mobile body is generated. The moving body is made to travel along the generated target trajectory; as well as Based on the multiple distance regions, different types of object targets are designated as risk zones.

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