Mobile body control device, mobile body control method, and program

Abstract

To efficiently use information of targets existing in the periphery of a mobile body.SOLUTION: A mobile body control device includes: a recognition section for recognizing a peripheral state of a mobile body; a risk area setting section for setting a risk area where the mobile body has to travel by avoidance in a plurality of distance areas with the mobile body as a center based on the recognized peripheral state; a target track generation section for generating a target track which indicates a travel route of the mobile body in future based on the set risk area; and a travel control section for allowing the mobile body to travel along the generated target track. The risk area setting section sets the different kinds of risk areas in response to the distance areas.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a mobile object control device, a mobile object control method, and a program. [Background technology]

[0002] Conventionally, there is known a technique for identifying obstacles present in the traveling direction of a moving body and controlling the traveling of the moving body so as to avoid the identified obstacles. For example, Patent Document 1 describes a technique for setting different costs in the surrounding area of ​​a vehicle based on image information captured by a camera mounted on the vehicle, and generating a target trajectory of the vehicle so as to reduce the costs. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2018-197048 Summary of the Invention [Problem to be solved by the invention]

[0004] The technology described in Patent Document 1 sets a cost for every obstacle captured in image information and uses the cost to generate a target trajectory for the vehicle. However, depending on the distance from the moving object and the situation of the moving object, generating a target trajectory by taking into account information on every target object present around the moving object may be inefficient.

[0005] The present invention has been made in consideration of these circumstances, and one of its objectives is to provide a mobile body control device, a mobile body control method, and a program that can more efficiently utilize information about targets present around a mobile body. [Means for solving the problem]

[0006] A mobile object control device, a mobile object control method, and a program according to the present invention employ the following configuration. (1): A mobile body control device according to one embodiment of the present invention comprises a recognition unit that recognizes the surrounding conditions of a mobile body; a risk area setting unit that sets risk areas that the mobile body should avoid in multiple distance areas centered on the mobile body based on the recognized surrounding conditions; a target trajectory generation unit that generates a target trajectory indicating a path along which the mobile body will travel in the future based on the set risk areas; and a travel control unit that causes the mobile body to travel along the generated target trajectory, wherein the risk area setting unit sets different types of risk areas according to the multiple distance areas.

[0007] (2): In the above aspect (1), the plurality of distance regions include a first distance region in which the distance from the moving body is within a range equal to or less than a first threshold, a second distance region in which the distance from the moving body is greater than the first threshold and less than a second threshold, and a third distance region in which the distance from the moving body is greater than the second threshold and less than a third threshold.

[0008] (3): In the above aspect (2), the risk area setting unit sets all types of targets in the first distance area as the multiple risk areas, sets targets in the second distance area that may have a collision risk when the target trajectory is corrected, and sets targets in the third distance area excluding lane boundaries and side walls of the lane in which the moving body is traveling.

[0009] (4) In the above aspect (3), the risk area setting unit excludes the lane boundary from the plurality of risk areas when a driving operator of the moving body is steered.

[0010] (5): In any of the above aspects (2) to (4), the target trajectory generation unit calculates a risk value at each of the trajectory points constituting the target trajectory based on the risk area, and generates the target trajectory so that the sum of the calculated risk values ​​is equal to or less than a threshold value.

[0011] (6): In the above aspect (5), the target trajectory generation unit generates each of the first target trajectory in the first distance region, the second target trajectory in the second distance region, and the third target trajectory in the third distance region using an arc model including trajectory points such that the sum of the risk values ​​is equal to or less than a threshold, and generates the target trajectory by connecting the generated first target trajectory, the second target trajectory, and the third target trajectory.

[0012] (7): In any of the above aspects (2) to (4), the target trajectory generation unit calculates a risk value at each of the trajectory points constituting the target trajectory based on the risk area, and generates the target trajectory so that the sum of the calculated risk values ​​is minimized.

[0013] (8): In the above aspect (7), the target trajectory generation unit generates each of the first target trajectory in the first distance region, the second target trajectory in the second distance region, and the third target trajectory in the third distance region using an arc model including trajectory points that minimize the sum of the risk values, and generates the target trajectory by connecting the generated first target trajectory, the second target trajectory, and the third target trajectory.

[0014] (9): Another aspect of the mobile body control method of the present invention involves a computer recognizing the surrounding conditions of a mobile body, setting risk areas in multiple distance regions centered on the mobile body that the mobile body should avoid based on the recognized surrounding conditions, generating a target trajectory indicating the path the mobile body will travel in the future based on the set risk areas, causing the mobile body to travel along the generated target trajectory, and setting different types of risk areas depending on the multiple distance regions.

[0015] (10): Another aspect of the present invention provides a program that causes a computer to recognize the surrounding conditions of a moving body, and based on the recognized surrounding conditions, set risk areas in multiple distance regions centered on the moving body that the moving body should avoid when traveling, generate a target trajectory indicating the path the moving body will travel in the future based on the set risk areas, cause the moving body to travel along the generated target trajectory, and set different types of risk areas depending on the multiple distance regions. [Effects of the Invention]

[0016] According to aspects (1) to (10), by setting different types of targets as risk areas depending on the distance from the moving body, it is possible to generate a target trajectory by more efficiently utilizing information about targets existing around the moving body. [Brief explanation of the drawings]

[0017] [Figure 1] 1 is a configuration diagram of a vehicle system 1 that uses a mobile object control device according to the present embodiment. [Figure 2] 2 is a functional configuration diagram of a first control unit 120 and a second control unit 160. FIG. [Figure 3] 10 is a diagram for explaining an outline of the processing executed by the risk area setting unit 132. FIG. [Figure 4] FIG. 10 is a diagram showing the relationship between a distance region DR and the type of target set as a risk region. [Figure 5] FIG. 10 is a diagram showing an example of a target trajectory corrected by the action plan generating unit 140 in accordance with the setting of a risk area RA. [Figure 6] FIG. 10 is a diagram showing another example of a target trajectory corrected by the action plan generating unit 140 in accordance with the setting of the risk area RA. [Figure 7] FIG. 10 is a diagram for explaining in detail a method in which the behavior plan generating unit 140 generates a target trajectory. [Figure 8] 10A and 10B are diagrams for explaining details of a method for searching for a target trajectory using an arc model. [Figure 9] 3 is a flowchart showing an example of the flow of processing executed by the automatic driving control device 100. DETAILED DESCRIPTION OF THE INVENTION

[0018] Hereinafter, embodiments of a mobile object control device, a mobile object control method, and a program according to the present invention will be described with reference to the drawings. The mobile object in the present invention includes a four-wheeled vehicle, a two-wheeled vehicle, micromobility, a robot, etc. In the following description, the mobile object is assumed to be a four-wheeled vehicle.

[0019] [Overall configuration] 1 is a configuration diagram of a vehicle system 1 that uses a mobile object control device according to this embodiment. The vehicle on which the vehicle system 1 is mounted may be, for example, a two-wheeled, three-wheeled, or four-wheeled vehicle, and its drive source may be an internal combustion engine such as a diesel engine or a gasoline engine, an electric motor, or a combination of these. The electric motor operates using power generated by a generator connected to the internal combustion engine, or discharged power from a secondary battery or a fuel cell.

[0020] The vehicle system 1 includes, 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, vehicle sensors 40, a navigation device 50, an MPU (Map Positioning Unit) 60, a driving operator 80, an automatic driving control device 100, a driving force output device 200, a braking device 210, and a steering device 220. These devices and equipment are connected to each other via multiplexed communication lines such as a CAN (Controller Area Network) communication line, serial communication lines, a wireless communication network, etc. Note that the configuration shown in FIG. 1 is merely an example, and some of the configuration may be omitted, or other configurations may be added.

[0021] The camera 10 is, for example, a digital camera using a solid-state imaging element such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor). The camera 10 is attached to any location of a vehicle (hereinafter referred to as the host vehicle M) in which the vehicle system 1 is installed. When capturing an image of the front, the camera 10 is attached to the top of the front windshield, the back of the rearview mirror, or the like. The camera 10, for example, periodically and repeatedly captures images of the surroundings of the host vehicle M. The camera 10 may be a stereo camera.

[0022] The radar device 12 emits radio waves such as millimeter waves around the vehicle M and detects radio waves reflected by an object (reflected waves) to detect at least the position (distance and direction) of the object. The radar device 12 is attached to any location on the vehicle M. The radar device 12 may detect the position and speed of an object using an FM-CW (Frequency Modulated Continuous Wave) method.

[0023] The LIDAR 14 irradiates the surroundings of the vehicle M with light (or electromagnetic waves with wavelengths similar to light) and measures the scattered light. The LIDAR 14 detects the distance to the target based on the time between light emission and light reception. The irradiated light is, for example, pulsed laser light. The LIDAR 14 is attached to any location on the vehicle M.

[0024] The object recognition device 16 performs sensor fusion processing on the detection results from some or all of the camera 10, radar device 12, and LIDAR 14 to recognize the position, type, speed, etc. of the object. The object recognition device 16 outputs the recognition results to the autonomous driving control device 100. The object recognition device 16 may output the detection results from the camera 10, radar device 12, and LIDAR 14 to the autonomous driving control device 100 as they are.

[0025] The communication device 20 communicates with other vehicles in the vicinity of the vehicle M, for example, using a cellular network, a Wi-Fi network, Bluetooth (registered trademark), DSRC (Dedicated Short Range Communication), etc., or communicates with various server devices via a wireless base station.

[0026] The HMI 30 presents various information to the occupants of the vehicle M and accepts input operations by the occupants. The HMI 30 includes various display devices, a speaker, a buzzer, a touch panel, switches, keys, and the like.

[0027] The vehicle sensor 40 includes 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 angular velocity around a vertical axis, a direction sensor that detects the direction of the host vehicle M, and the like.

[0028] The navigation device 50 includes, for example, a GNSS (Global Navigation Satellite System) receiver 51, a navigation HMI 52, and a route determination unit 53. The navigation device 50 stores first map information 54 in a storage device such as a hard disk drive (HDD) or flash memory. The GNSS receiver 51 identifies the position of the vehicle M based on signals received from GNSS satellites. The position of the vehicle M may be identified or supplemented by an inertial navigation system (INS) that uses the output of the vehicle sensors 40. The navigation HMI 52 includes a display device, a speaker, a touch panel, keys, etc. The navigation HMI 52 may share some or all of the components with the HMI 30 described above. The route determination unit 53 determines, for example, a route (hereinafter, a map route) from the position of the vehicle M identified by the GNSS receiver 51 (or any input position) to a destination input by the occupant using the navigation HMI 52, with reference to the first map information 54. The first map information 54 is information that represents road shapes using, for example, links indicating roads and nodes connected by the links. The first map information 54 may also include information such as road curvature and POI (Point of Interest) information. The route on the map is output to the MPU 60. The navigation device 50 may provide route guidance using the navigation HMI 52 based on the route on the map. The navigation device 50 may be realized, for example, by the functions of a terminal device such as a smartphone or tablet device owned by the occupant. The navigation device 50 may transmit the current position and destination to a navigation server via the communication device 20 and obtain a route equivalent to the route on the map from the navigation server.

[0029] The MPU 60 includes, for example, a recommended lane determination unit 61, and stores second map information 62 in a storage device such as an HDD or flash memory. The recommended lane determination unit 61 divides the route on the map provided by the navigation device 50 into a plurality of blocks (for example, by dividing it into 100 m intervals in the vehicle travel direction), and determines a recommended lane for each block by referring to the second map information 62. The recommended lane determination unit 61 determines, for example, which lane from the left the vehicle should travel in. When there is a branch point on the route on the map, the recommended lane determination unit 61 determines a recommended lane so that the vehicle M can travel on a reasonable route to the branch point.

[0030] The second map information 62 is map information with higher accuracy than the first map information 54. The second map information 62 includes, for example, information on the center of lanes or information on lane boundaries. The second map information 62 may also include road information, traffic regulation information, address information (address and postal code), facility information, telephone number information, etc. The second map information 62 may be updated as needed by the communication device 20 communicating with other devices.

[0031] The driving operators 80 include, for example, an accelerator pedal, a brake pedal, a shift lever, a steering wheel, an irregular steering wheel, a joystick, and other operators. The driving operators 80 are fitted with sensors that detect the amount of operation or the presence or absence of operation, and the detection results are output to the automatic driving control device 100 or some or all of the driving force output device 200, the braking device 210, and the steering device 220.

[0032] The automatic driving control device 100 includes, for example, a first control unit 120 and a second control unit 160. The first control unit 120 and the second control unit 160 are each realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Some or all of these components may 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), or a GPU (Graphics Processing Unit), or may be realized by a combination of software and hardware. The program may be stored in advance in a storage device (a storage device having a non-transitory storage medium) such as a HDD or flash memory of the automatic driving control device 100, or may be stored in a removable storage medium such as a DVD or CD-ROM, and installed in the HDD or flash memory of the automatic driving control device 100 by inserting the storage medium (non-transitory storage medium) into a drive device.

[0033] FIG. 2 is a functional configuration diagram of the first control unit 120 and the second control unit 160. The first control unit 120 includes, for example, a recognition unit 130 and an action plan generation unit 140. The first control unit 120, for example, implements a function based on AI (Artificial Intelligence) and a function based on a pre-given model in parallel. For example, the function of "recognizing intersections" may be implemented by executing in parallel recognition of intersections using deep learning or the like and recognition based on pre-given conditions (such as traffic lights and road markings that can be pattern-matched), and then scoring and comprehensively evaluating both. This ensures the reliability of autonomous driving.

[0034] The recognition unit 130 recognizes the position, speed, acceleration, and other states of objects around the vehicle M based on information input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16. The position of an object is recognized as a position on an absolute coordinate system with a representative point of the vehicle M (such as the center of gravity or the center of the drive shaft) as the origin, and is used for control. The position of an object may be represented by a representative point such as the center of gravity or a corner of the object, or may be represented by a represented area. The "state" of an object may include the acceleration or jerk of the object, or the "behavioral state" (for example, whether the object is changing lanes or is about to change lanes).

[0035] The recognition unit 130 also recognizes, for example, the lane in which the host vehicle M is traveling (driving lane). For example, the recognition unit 130 recognizes the driving lane by comparing the pattern of road dividing lines (e.g., an arrangement of solid lines and dashed lines) obtained from the second map information 62 with the pattern of road dividing lines around the host vehicle M recognized from an image captured by the camera 10. Note that the recognition unit 130 may recognize the driving lane by recognizing road boundaries (road boundaries) including not only road dividing lines but also road dividing lines, shoulders, curbs, medians, guardrails, etc. In this recognition, the position of the host vehicle M obtained from the navigation device 50 and the processing results by the INS may be taken into consideration. The recognition unit 130 also recognizes stop lines, obstacles, red lights, toll booths, and other road phenomena.

[0036] When recognizing the driving lane, the recognition unit 130 recognizes the position and attitude of the host vehicle M with respect to the driving lane. For example, the recognition unit 130 may recognize the deviation of the reference point of the host vehicle M from the center of the lane and the angle it forms with a line connecting the centers of the lanes in the traveling direction of the host vehicle M as the relative position and attitude of the host vehicle M with respect to the driving lane. Alternatively, the recognition unit 130 may recognize the position of the reference point of the host vehicle M with respect to either side edge of the driving lane (a road dividing line or a road boundary) as the relative position of the host vehicle M with respect to the driving lane. In this embodiment, the recognition unit 130 includes a risk area setting unit 132, and the function of the risk area setting unit 132 will be described in detail later.

[0037] The behavior plan generation unit 140 generates a target trajectory along which the host vehicle M will travel in the future automatically (without relying on the driver's operation) so that the host vehicle M will, in principle, travel along the recommended lane determined by the recommended lane determination unit 61 and can respond to the surrounding conditions of the host vehicle M. The target trajectory includes, for example, a speed element. For example, the target trajectory is expressed as a sequence of points (trajectory points) that the host vehicle M should reach. The trajectory points are points that the host vehicle M should reach at every predetermined travel distance (for example, about several meters) along the road. Separately, target speeds and target accelerations are generated as part of the target trajectory for every predetermined sampling time (for example, about a few tenths of a second). Furthermore, the trajectory points may be positions that the host vehicle M should reach at each sampling time for each predetermined sampling time. In this case, information on the target speed and target acceleration is expressed as the interval between trajectory points.

[0038] The behavior plan generation unit 140 may set an autonomous driving event when generating the target trajectory. The autonomous driving events include a constant speed driving event, a low-speed following driving event, a lane change event, a branching event, a merging event, a takeover event, etc. The behavior plan generation unit 140 generates a target trajectory according to the activated event.

[0039] The second control unit 160 controls the traveling driving force output device 200, the braking device 210, and the steering device 220 so that the host vehicle M passes through the target trajectory generated by the action plan generation unit 140 at the scheduled time.

[0040] Returning to FIG. 2, the 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 on the target trajectory (trajectory points) generated by the action plan generation unit 140 and stores it in a memory (not shown). The speed control unit 164 controls the driving force output device 200 or the brake device 210 based on a speed element associated with the target trajectory stored in the memory. The steering control unit 166 controls the steering device 220 according to the curvature of the target trajectory stored in the memory. The processing of the speed control unit 164 and the steering control unit 166 is realized by, for example, a combination of feedforward control and feedback control. As an example, the steering control unit 166 executes a combination of feedforward control according to the curvature of the road ahead of the host vehicle M and feedback control based on the deviation from the target trajectory.

[0041] The driving force output device 200 outputs a driving force (torque) for the vehicle to travel to the drive wheels. The driving force output device 200 includes, for example, a combination of an internal combustion engine, an electric motor, a transmission, etc., and an ECU (Electronic Control Unit) that controls these. The ECU controls the above components according to information input from the second control unit 160 or information input from the driving operator 80.

[0042] Braking device 210 may include, for example, a brake caliper, a cylinder that transmits hydraulic pressure to the brake caliper, an electric motor that generates hydraulic pressure in the cylinder, and a brake ECU. The brake ECU controls the electric motor according to information input from second control unit 160 or information input from driving operation device 80, so that a brake torque corresponding to the braking operation is output to each wheel. Braking device 210 may include a backup mechanism that transmits hydraulic pressure generated by operation of a brake pedal included in driving operation device 80 to the cylinder via a master cylinder. Note that braking device 210 is not limited to the configuration described above, and may also be an electronically controlled hydraulic brake device that controls an actuator according to information input from second control unit 160 to transmit hydraulic pressure from a master cylinder to the cylinder.

[0043] The steering device 220 includes, for example, a steering ECU and an electric motor. The electric motor applies a force to a rack and pinion mechanism to change the direction of the steered wheels. The steering ECU drives the electric motor in accordance with information input from the second control unit 160 or information input from the driving operator 80 to change the direction of the steered wheels.

[0044] [Operation] Next, the processing executed by the risk area setting unit 132 will be described. FIG. 3 is a diagram for explaining an outline of the processing executed by the risk area setting unit 132. In FIG. 3, symbol DR1 indicates a first distance area within a radius r1 centered on the host vehicle M, symbol DR2 indicates a second distance area within a radius r2 centered on the host vehicle M, symbol DR3 indicates a third distance area within a radius r3 centered on the host vehicle M, symbol B indicates a motorcycle that is a traffic participant, symbol RA indicates a risk area set by the risk area setting unit 132, symbol P indicates a pedestrian that is a traffic participant, and symbol L indicates a road division line (lane boundary). FIG. 3 illustrates, as an example, a scene in which the host vehicle M is traveling along a target trajectory TT1 generated by the behavior plan generating 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."

[0045] Based on the surrounding conditions of the host vehicle M recognized by the recognition unit 130, the risk area setting unit 132 sets a risk area RA that the host vehicle M should avoid for each of multiple distance areas DR (i.e., a first distance area DR1, a second distance area DR2, and a third distance area DR3 in this embodiment) centered on the host vehicle M. More specifically, the risk area setting unit 132 identifies different types of targets for each distance area, and sets a risk value R, which takes a larger value (i.e., a negative value) the closer the identified target is to the target, to the surrounding area of ​​the target, thereby setting the risk area RA. In the example of FIG. 3, the risk area setting unit 132 sets the motorcycle B and the road dividing line L as the risk area RA in the first distance area DR1. Within the risk area RA, darker colored parts represent a higher risk value, and lighter colored parts represent a lower risk value.

[0046] FIG. 4 is a diagram illustrating the relationship between distance regions and the types of targets set as risk regions. As shown in FIG. 4, the risk region setting unit 132 sets all types of targets present in a first distance region DR1, targets present in a second distance region DR2 excluding road dividing lines L, and targets present in a third distance region DR3 excluding road dividing lines L and the sidewalls of the lane in which the host vehicle M is traveling, as risk regions. That is, the first distance region DR1 is a risk setting region for avoiding collisions with all targets present in a short distance from the host vehicle M, the second distance region DR2 is a risk setting region for avoiding collisions with targets present in a medium distance from the host vehicle M that pose a risk of secondary collision, and the third distance region DR3 is a risk setting region for avoiding collisions with targets present in a long distance from the host vehicle M in advance. In FIG. 4, as an example, targets excluding road dividing lines L are set as targets that may cause a secondary collision. However, the present invention is not limited to such a configuration. In general, any target may be set by the system administrator as long as it poses a risk of secondary collision.

[0047] The behavior plan generation unit 140 calculates a risk value for each trajectory point that constitutes the target trajectory, and if the sum of the calculated risk values ​​is equal to or greater than a threshold, corrects the target trajectory so that the target trajectory is less than the threshold. For example, in the case of FIG. 3, the behavior plan generation unit 140 calculates a risk value for each of the trajectory points TP1 and TP2 that constitute the target trajectory TT1, and if the sum of the calculated risk values ​​is equal to or greater than a threshold, corrects the target trajectory TT1. When correcting the target trajectory TT1, for example, it is possible to generate a target trajectory TT2 whose trajectory points do not intersect with the risk area RA (i.e., whose risk value is zero).

[0048] However, in general, when the target trajectory is corrected and the host vehicle M is caused to travel along the corrected target trajectory, a secondary collision with an obstacle present on the corrected target trajectory may occur. For example, in the case of FIG. 3, when the host vehicle M travels along the corrected target trajectory TT2, a secondary collision with a pedestrian P may occur. In light of this situation, the risk area setting unit 132 according to this embodiment sets different types of targets as risk areas RA for each distance area, as described with reference to FIG. 4, and the action plan generation unit 140 generates a target trajectory for the host vehicle M based on the risk areas RA set for each distance area.

[0049] FIG. 5 is a diagram showing an example of a target trajectory corrected by the behavior plan generation unit 140 in accordance with the setting of the risk area RA. As shown in FIG. 5, the risk area setting unit 132 sets a risk area RA corresponding to the road dividing line L and the motorcycle B in the first distance area DR1, and sets a risk area RA corresponding to the pedestrian P who is at risk of a secondary collision in the second distance area DR2. Therefore, based on these set risk areas RA, the behavior plan generation unit 140 corrects the target trajectory by displacing the trajectory points so that the sum of the risk values ​​of the trajectory points constituting the target trajectory is less than a threshold. In the example of FIG. 5, it can be seen that a target trajectory TT3 has been generated as a result of the correction.

[0050] Fig. 6 is a diagram showing another example of a target trajectory corrected by the action plan generation unit 140 in accordance with the setting of the risk area RA. Unlike Fig. 5, Fig. 6 shows a case where there is no pedestrian P and the risk area setting unit 132 sets only the risk area RA corresponding to the road dividing line L and the motorcycle B. In this case, the risk area setting unit 132 can more reliably avoid the motorcycle B than when no targets other than the road dividing line L are set in the second distance area DR2 and the road dividing line L is set as the risk area.

[0051] Next, a detailed method for generating a target trajectory will be described with reference to Fig. 7. Fig. 7 is a diagram for explaining a detailed method for generating a target trajectory by the behavior plan generating unit 140. In Fig. 7, TT3_1 indicates a partial target trajectory generated for the first distance region DR1, TT3_2 indicates a partial target trajectory generated for the second distance region DR2, and TT3_3 indicates a partial target trajectory generated for the third distance region DR3.

[0052] 7, the behavior plan generation unit 140 uses an arc model to search for trajectory points for which the sum of risk values ​​is less than a threshold for each distance region DR, and generates the searched arc model as a partial target trajectory. For example, for the first distance region DR1, the behavior plan generation unit 140 first changes the parameters of the arc model so that the sum of risk values ​​of the trajectory points is less than the threshold, and generates an arc model for which the sum of risk values ​​of the trajectory points is less than the threshold as a partial target trajectory TT3_1. Next, for the second distance region DR2, the behavior plan generation unit 140 sets the end point of the partial target trajectory TT3_1 as the start point and changes the parameters of the arc model so that the sum of risk values ​​of the trajectory points is less than the threshold, and generates an arc model for which the sum of risk values ​​of the trajectory points is less than the threshold as a partial target trajectory TT3_2. Next, the behavior plan generation unit 140 changes the parameters of the arc model for the second distance region DR3 so that the starting point is the end point of the partial target trajectory TT3_2 and the sum of the risk values ​​of the trajectory points is less than a threshold, and generates an arc model as the partial target trajectory TT3_3, in which the sum of the risk values ​​of the trajectory points is less than the threshold.

[0053] 8 is a diagram for explaining details of a method for searching for a target trajectory using an arc model. In FIG. 8, symbols TT3_1_C1, TT3_1_C2, and TT3_1_C3 indicate arc models (hereinafter, may be collectively referred to as "TT3_1_C") that are candidates for the partial target trajectory TT3_1, and symbols TT3_2_C1 and TT3_1_C2 indicate arc models (hereinafter, may be collectively referred to as "TT3_2_C") that are candidates for the partial target trajectory TT3_2. As shown in FIG. 8, the behavior plan generating unit 140 first changes parameters (e.g., the end point and curvature of the arc) of the arc model TT3_1_C for the first distance region DR1 based on the position of the host vehicle M (the center of the front end in FIG. 8), and when the sum of the risk values ​​of the trajectory points present on the arc model TT3_1_C is less than a threshold, the behavior plan generating unit 140 determines the arc model TT3_1_C as the partial target trajectory TT3_1. The behavior plan generation unit 140 then changes the parameters of the arc model TT3_2_C, starting from the end point CP of the determined arc model TT3_1_C, so that the sum of the risk values ​​of the trajectory points is less than a threshold, and determines the arc model TT3_2_C as the partial target trajectory TT3_2 when the sum of the risk values ​​of the trajectory points is less than the threshold. Similarly, the behavior plan generation unit 140 generates a partial target trajectory TT3_3, starting from the end point CP of the determined arc model TT3_1_C. It can be said that the partial target trajectory TT3_1 of the first distance region DR1 generated in this way is an arc trajectory for avoiding any collision risk, the partial target trajectory TT3_2 of the second distance region DR2 is an arc trajectory for avoiding a secondary collision risk, and the partial target trajectory TT3_3 of the third distance region DR3 is an arc trajectory for avoiding a collision with a target with ample margin. At this time, the behavior plan generating unit 140 may perform a search using an arc model a predetermined number of times without using a threshold value, and determine the arc model that minimizes the sum of the risk values ​​of the trajectory points as the partial target trajectory.

[0054] The behavior plan generation unit 140 generates a target trajectory TT3 by interconnecting the generated partial target trajectories TT3_1, TT3_2, and TT3_3. At this time, the behavior plan generation unit 140 may calculate the sum of risk values ​​of the generated target trajectory TT3, store the sum as a target trajectory candidate, and determine the target trajectory candidate having the smallest sum of risk values ​​as the final target trajectory among multiple target trajectory candidates obtained by performing the partial target trajectory search and connection process a predetermined number of times. Furthermore, the behavior plan generation unit 140 may not simply connect the generated partial target trajectories TT3_1, TT3_2, and TT3_3, but may also determine the final target trajectory by fitting them using a smooth curve.

[0055] In the above embodiment, all types of targets are set as the first distance area DR1. However, for example, if the driver of the vehicle M steers the driving operator 80, the risk area setting unit 132 may exclude road dividing lines from the set risk area RA. This is because if the driver of the vehicle M steers the driving operator 80, a highly urgent situation may occur, and it may not be a priority to consider the road dividing lines as the risk area RA.

[0056] Furthermore, in the above embodiment, three distance regions, i.e., a first distance region DR1, a second distance region DR2, and a third distance region DR3, are set as the multiple distance regions. However, the present invention is not limited to such a configuration, and two distance regions or four or more distance regions may be set, and different types of targets may be set as risk regions in each distance region.

[0057] Next, the flow of processing executed by the automatic driving control device 100 will be described with reference to Fig. 9. Fig. 9 is a flowchart showing an example of the flow of processing executed by the automatic driving control device 100.

[0058] First, the recognition unit 130 recognizes the surrounding conditions of the vehicle M based on 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 recognized surrounding conditions (step S102).

[0059] Next, the behavior plan generation unit 140 generates a partial target trajectory of the vehicle M as an arc for each distance region based on the risk region RA set by the risk region setting unit 132 (step S104). More specifically, the behavior plan generation unit 140 changes the parameters of the arc model so that the sum of the risk values ​​of each trajectory point calculated based on the risk region RA is equal to or less than a threshold, and determines the arc model for which the sum of the risk values ​​of the trajectory points is equal to or less than the threshold as the partial target trajectory.

[0060] Next, the behavior plan generating unit 140 generates a target trajectory by connecting the generated partial target trajectories with each other (step S106). Next, the second control unit 160 causes the host vehicle M to travel along the generated target trajectory (step S108). This ends the processing of this flowchart.

[0061] According to the present embodiment described above, different types of targets are set as risk areas depending on the distance from the vehicle, a partial target trajectory is generated as an arc for each distance area so that the sum of the risk values ​​of each trajectory point calculated based on the set risk area is equal to or less than a threshold, a target trajectory is generated by connecting the generated partial target trajectories, and the vehicle is caused to travel along the generated target trajectory. This makes it possible to more efficiently utilize information about targets present around the moving body.

[0062] The above-described embodiment can be expressed as follows. a storage device storing a program; a hardware processor; The hardware processor executes the program stored in the storage device, Recognize the surrounding situation of the moving object, Based on the recognized surrounding conditions, risk areas are set within a plurality of distance areas centered on the moving object, which the moving object should avoid when traveling; generating a target trajectory indicating a route along which the moving object will travel in the future based on the set risk area; causing the moving body to travel along the generated target trajectory; setting different types of risk areas according to the plurality of distance areas; A mobile object control device configured as above.

[0063] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0064] 10 Camera 12 Radar equipment 14 LIDAR 16 Object recognition device 100 Automatic driving control device 120 First Control Section 130 Recognition part 132 Risk Area Setting Department 140 Action Plan Generation Unit 160 Second Control Section 162 Acquisition Department 164 Speed ​​control section 166 Steering control unit

Claims

1. a recognition unit that recognizes the surrounding situation of the moving object; a risk area setting unit that sets risk areas that the moving object should avoid in a plurality of distance areas centered on the moving object from the recognized target based on the recognized surrounding situation; a target trajectory generation unit that generates a target trajectory indicating a path along which the moving object will travel in the future based on the set risk area; a travel control unit that causes the moving body to travel along the generated target trajectory, the risk area setting unit sets different types of targets as the risk areas according to the plurality of distance areas; Mobile control device.

2. The plurality of distance regions include a first distance region in which the distance from the moving body is within a range equal to or less than a first threshold, a second distance region in which the distance from the moving body is greater than the first threshold and within a range equal to or less than a second threshold, and a third distance region in which the distance from the moving body is greater than the second threshold and within a range equal to or less than a third threshold. The mobile object control device according to claim 1 .

3. A recognition unit that recognizes the surrounding situation of a moving object; a risk area setting unit that sets risk areas that the moving object should avoid in a plurality of distance areas centered on the moving object based on the recognized surrounding conditions; a target trajectory generation unit that generates a target trajectory indicating a path along which the moving object will travel in the future based on the set risk area; a travel control unit that causes the moving body to travel along the generated target trajectory, the risk area setting unit sets different types of risk areas according to the plurality of distance areas, the plurality of distance regions include a first distance region in which the distance from the moving body is within a range equal to or less than a first threshold, a second distance region in which the distance from the moving body is greater than the first threshold and within a range equal to or less than a second threshold, and a third distance region in which the distance from the moving body is greater than the second threshold and within a range equal to or less than a third threshold, the risk area setting unit sets, as the plurality of risk areas, all kinds of targets in the first distance area, targets in the second distance area that may collide when the target trajectory is corrected if the sum of risk values ​​calculated for each trajectory point constituting the target trajectory becomes larger than a threshold, and targets in the third distance area that do not include lane boundaries and side walls of the lane on which the moving body is traveling. Mobile control device.

4. the risk area setting unit excludes the lane boundary from the plurality of risk areas when a driving operation control of the moving body is steered. The mobile object control device according to claim 3 .

5. the target trajectory generation unit calculates a risk value at each of trajectory points constituting the target trajectory based on the risk region, and generates the target trajectory so that the sum of the calculated risk values ​​is equal to or less than a threshold value. The mobile object control device according to claim 2 .

6. A recognition unit that recognizes the surrounding situation of a moving object; a risk area setting unit that sets risk areas that the moving object should avoid in a plurality of distance areas centered on the moving object based on the recognized surrounding conditions; a target trajectory generation unit that generates a target trajectory indicating a path along which the moving object will travel in the future based on the set risk area; a travel control unit that causes the moving body to travel along the generated target trajectory, the risk area setting unit sets different types of risk areas according to the plurality of distance areas, the plurality of distance regions include a first distance region in which the distance from the moving body is within a range equal to or less than a first threshold, a second distance region in which the distance from the moving body is greater than the first threshold and within a range equal to or less than a second threshold, and a third distance region in which the distance from the moving body is greater than the second threshold and within a range equal to or less than a third threshold, the target trajectory generation unit calculates a risk value at each of trajectory points constituting the target trajectory based on the risk region, and generates the target trajectory so that a sum of the calculated risk values ​​is equal to or less than a threshold; 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 including trajectory points that make the sum of the risk values ​​equal to or less than a threshold, and generates the target trajectory by connecting the generated first target trajectory, the second target trajectory, and the third target trajectory. Mobile control device.

7. the target trajectory generation unit calculates a risk value at each of trajectory points constituting the target trajectory based on the risk region, and generates the target trajectory so that the sum of the calculated risk values ​​is minimized. The mobile object control device according to claim 2 .

8. A recognition unit that recognizes the surrounding situation of a moving object; a risk area setting unit that sets risk areas that the moving object should avoid in a plurality of distance areas centered on the moving object based on the recognized surrounding conditions; a target trajectory generation unit that generates a target trajectory indicating a path along which the moving object will travel in the future based on the set risk area; a travel control unit that causes the moving body to travel along the generated target trajectory, the risk area setting unit sets different types of risk areas according to the plurality of distance areas, the plurality of distance regions include a first distance region in which the distance from the moving body is within a range equal to or less than a first threshold, a second distance region in which the distance from the moving body is greater than the first threshold and within a range equal to or less than a second threshold, and a third distance region in which the distance from the moving body is greater than the second threshold and within a range equal to or less than a third threshold, the target trajectory generation unit calculates a risk value at each of trajectory points constituting the target trajectory based on the risk region, and generates the target trajectory so that a sum of the calculated risk values ​​is minimized; 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 including trajectory points that minimize the sum of the risk values, and generates the target trajectory by connecting the generated first target trajectory, the second target trajectory, and the third target trajectory. Mobile control device.

9. The computer Recognize the surrounding situation of the moving object, Based on the recognized surrounding conditions, risk areas that the moving body should avoid are set within a plurality of distance areas centered on the moving body from the recognized target; generating a target trajectory indicating a route along which the moving object will travel in the future based on the set risk area; causing the moving body to travel along the generated target trajectory; setting different types of targets as the risk areas according to the plurality of distance areas; A mobile object control method.

10. On the computer, It allows the vehicle to recognize its surroundings, Based on the recognized surrounding conditions, risk areas that the moving body should avoid are set in a plurality of distance areas centered on the moving body from the recognized target; generating a target trajectory indicating a route along which the moving object will travel in the future based on the set risk area; causing the moving body to travel along the generated target trajectory; setting different types of targets as the risk areas according to the plurality of distance areas; program.

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