Vehicle control device and vehicle control method

Abstract

One embodiment of the present invention is provided with: an approach determination unit that determines the approach of a moving body to an own vehicle; a steering restriction setting unit that sets a relative yaw angle, which is an angle formed by the traveling direction of the own vehicle and the traveling direction of the moving body, or an allowable range of a relative yaw angle change amount; and a trajectory planning unit that plans a trajectory related to a movement of the own vehicle, such that the relative yaw angle after the movement of the own vehicle or the relative yaw angle change amount associated with the movement of the own vehicle becomes within the allowable range, when the approach determination unit determines that the moving body approaches the own vehicle.

Description

Vehicle control device and vehicle control method

[0001] The present invention relates to a vehicle control device and a vehicle control method.

[0002] In recent years, the development of autonomous driving technology for automobiles and other vehicles has progressed. However, when autonomous driving is performed, the road width on which the vehicle travels is not always appropriate. For example, on a narrow road where it is difficult for the vehicle and oncoming vehicles to pass each other, a different control method than that used during normal driving is required to move the vehicle to a place where they can pass each other.

[0003] For example, a technology has been disclosed that plans the actions of a vehicle, such as starting, stopping, and direction of travel, in order to control the vehicle to return to the entrance of the road where passing is possible on a road where there is no waiting space between the vehicle and an oncoming vehicle (see, for example, Patent Document 1).

[0004] Japanese Patent Application Laid-Open No. 2018-151177

[0005] However, with conventional methods, when using automated driving (and advanced driving assistance) to pass other vehicles on narrow urban streets or when using automated parking assistance in parking lots, occupants may feel uncomfortable or scared due to the proximity of nearby moving objects, especially when the vehicle is driving close to other moving objects in the vicinity.

[0006] Given the above situation, there has been a demand for a method to alleviate the discomfort and fear felt by passengers when approaching a nearby moving object when the vehicle is traveling in cooperation with other moving objects in the vicinity.

[0007] In order to solve the above problems, a vehicle control device of one aspect of the present invention includes an approach determination unit that determines whether a moving body is approaching the host vehicle; a steering limit setting unit that sets an allowable range for the relative yaw angle, which is the angle between the traveling direction of the host vehicle and the traveling direction of the moving body, or an allowable range for a change in the relative yaw angle; and a trajectory planning unit that, when the approach determination unit determines that the moving body is approaching the host vehicle, plans a trajectory for the movement of the host vehicle so that the relative yaw angle after the movement of the host vehicle or the change in the relative yaw angle accompanying the movement of the host vehicle is within the allowable range.

[0008] According to at least one aspect of the present invention, when a vehicle travels cooperatively with a nearby moving object, it is possible to plan a trajectory for the vehicle's movement that alleviates the discomfort or fear felt by occupants due to the proximity of the moving object. Problems, configurations, and effects other than those described above will become clear from the description of the following embodiments.

[0009] 1 is a block diagram showing an example of the configuration of a driving system and sensors of a vehicle equipped with a vehicle control device according to a first embodiment of the present invention. FIG. 2 is a block diagram showing an example of the configuration of a vehicle control device according to the first embodiment of the present invention. FIG. 3 is a block diagram showing an example of the configuration of a risk map generation unit of the vehicle control device according to the first embodiment of the present invention. FIG. 4 is a flowchart showing an example of a delay risk map generation process by the vehicle control device according to the first embodiment of the present invention. FIG. 5 is a diagram showing an example of a cooperative action plan and a delay risk map generated when a road boundary on the left side of the host vehicle is not linear and there is a turnout road, in the vehicle control device according to the first embodiment of the present invention. FIG. 6 is a diagram showing an example of a cooperative action plan and a delay risk map generated when a road boundary on the left side of the host vehicle is linear and a turnout road is on the opposite side (the right side as seen from the host vehicle), in the vehicle control device according to the first embodiment of the present invention. FIG. 7 is a block diagram showing an example of the configuration of an automatic driving planning unit of the vehicle control device according to the first embodiment of the present invention. FIG. 8 is a block diagram showing an example of the configuration of a cooperative behavior trajectory generation unit of the vehicle control device according to the first embodiment of the present invention. FIG. 9 is a flowchart showing an example of a turnout position and attitude generation process by the vehicle control device according to the first embodiment of the present invention. FIG. 10 is a diagram showing an example of a target turnout position and attitude candidate generation process by the vehicle control device according to the first embodiment of the present invention. FIG. 1 is a diagram showing an example of an arrival position candidate by the vehicle control device according to the first embodiment of the present invention. FIG. 2 is a diagram showing an example of a cooperative behavior trajectory (passing trajectory) by the vehicle control device according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of a retention risk map and a planned cooperative behavior trajectory (passing trajectory) by the vehicle control device according to the first embodiment of the present invention. FIG. 4 is a diagram showing an example of an example in which a sense of anxiety or fear is felt by an occupant of the host vehicle when traveling on the cooperative behavior trajectory according to the first embodiment of the present invention. FIG. 5 is a flowchart showing an example of a steering limit setting process when a peripheral object is approaching by the vehicle control device according to the first embodiment of the present invention. FIG. 6 is a flowchart showing an example of a turnout position generation process when a peripheral object is approaching by the vehicle control device according to the first embodiment of the present invention.1 is a block diagram showing an example of the configuration of a driving mode management unit of a vehicle control device according to a first embodiment of the present invention. FIG. 2 is a diagram showing an example of control of a steering angle when a peripheral object approaches in the vehicle control device according to the first embodiment of the present invention. FIG. 3 is a diagram showing an example of peripheral object approach determination processing by a peripheral object approach determination unit of a vehicle control device according to a second embodiment of the present invention. FIG. 4 is a diagram showing examples of vehicle movement, collision time, and threshold value in the vehicle control device according to the second embodiment of the present invention. FIG. 5 is a diagram showing an example of the relationship between the collision time and the threshold value when the differential value of the collision time is positive and negative in the vehicle control device according to the second embodiment of the present invention. FIG. 6 is a diagram showing an example of an evacuation behavior of the host vehicle when passing another vehicle in a vehicle control device according to a third embodiment of the present invention. FIG. 7 is a diagram showing an example of a change in the visual field range of an occupant accompanying the movement of the vehicle in the vehicle control device according to the third embodiment of the present invention. FIG. 8 is a diagram showing an example of peripheral object approach determination processing by a peripheral object approach determination unit of a vehicle control device according to the third embodiment of the present invention.

[0010] DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Hereinafter, examples of modes for carrying out the present invention (hereinafter referred to as "embodiments") will be described with reference to the accompanying drawings.

[0011] In this specification and the accompanying drawings, identical or similar components are denoted by the same reference numerals, and redundant explanations may be omitted or only explanations focusing on the differences may be given. The number of each component may be singular or plural unless otherwise specified.

[0012] First Embodiment A vehicle control device and a vehicle control method according to a first embodiment of the present invention will be described below with reference to FIGS.

[0013] [Vehicle Configuration] First, the overall configuration of a vehicle equipped with a vehicle control device according to a first embodiment of the present invention will be described with reference to Fig. 1. Fig. 1 is a block diagram showing an example configuration of a driving system and sensors of a vehicle 100 equipped with a vehicle control device 1 according to the first embodiment. The vehicle 100 is automatically driven under the control of the vehicle control device 1.

[0014] As shown in FIG. 1 , vehicle 100 has a left front wheel 101FL, a right front wheel 101FR, a left rear wheel 101RL, and a right rear wheel 101RR. Vehicle 100 is equipped with a forward recognition sensor 2, a left side recognition sensor 3, a right side recognition sensor 4, and a rear recognition sensor 5 as sensors for recognizing the external environment, and is capable of detecting the relative distance and relative speed between the vehicle and surrounding vehicles. For example, a camera may be used as the sensor for recognizing the external environment. Information (detection signals) from these sensors 2, 3, 4, and 5 is supplied to vehicle control device 1.

[0015] The vehicle control device 1 calculates command values ​​for a steering control mechanism 10, a brake control mechanism 13, and a throttle control mechanism 20 to control the traveling direction of the vehicle 100, based on information from sensors 2, 3, 4, and 5. The vehicle 100 is equipped with a steering control device 8 that controls the steering control mechanism 10 based on command values ​​from the vehicle control device 1, and a braking control device 15 that controls the brake control mechanism 13 and adjusts the braking force distribution to each wheel based on command values ​​from the vehicle control device 1. The vehicle 100 is further equipped with an acceleration control device 19 that controls the throttle control mechanism 20 and adjusts the torque output of the engine based on command values ​​from the vehicle control device 1, and a display device 24 that displays a travel plan for the vehicle 100, predicted behavior of nearby moving objects, etc.

[0016] The vehicle 100 also includes a communication device 23 that performs road-to-vehicle or vehicle-to-vehicle communication. The sensor configuration shown in Fig. 1 is an example and is not limited to the example shown in Fig. 1. The types of sensors may also be various types of sensors, such as ultrasonic sensors, stereo cameras, infrared cameras, radar, and LiDAR, or a combination of these sensors.

[0017] As will be described later with reference to Fig. 2, the vehicle control device 1 is configured as an arithmetic processing device including, for example, a CPU, a memory, etc. A program for performing vehicle driving control processing is stored in the vehicle control device 1, and the vehicle control device 1 generates a driving plan by executing the program. The vehicle control device 1 calculates command values ​​for each control mechanism (hereinafter also referred to as "actuators") 10, 13, 20 for controlling vehicle driving in accordance with the generated driving plan. Control devices 8, 15, 19 for each actuator 10, 13, 20 receive the command values ​​from the vehicle control device 1 via communication and control each actuator based on the command values.

[0018] The brake actuation configuration shown in Fig. 1 will now be described. When the driver is driving, the force generated by the driver stepping on the brake pedal 12 is boosted by a brake booster (not shown), and a corresponding hydraulic pressure is generated by a master cylinder (not shown). The generated hydraulic pressure is supplied via a brake control mechanism 13 to wheel cylinders 16FL, 16FR, 16RL, and 16RR arranged at each of the wheels 101FL to 101RR.

[0019] Each wheel cylinder 16FL-16RR is composed of a cylinder, a piston, a pad, etc. The piston is propelled by hydraulic fluid supplied from the master cylinder 9, and the pad connected to the piston is pressed against the disc rotor. The disc rotor rotates together with the wheel constituting each wheel 101FL-101RR. Therefore, the brake torque acting on the disc rotor becomes a braking force acting between the wheel and the road surface. With this configuration, braking force can be generated on each wheel in response to the driver's brake pedal operation.

[0020] The braking control device 15 is configured as an arithmetic processing device including, for example, a CPU, a memory, etc., similar to the vehicle control device 1. The braking control device 15 receives inputs from a combined sensor 14 capable of detecting longitudinal acceleration, lateral acceleration, and yaw rate, wheel speed sensors 11FL to 11RR installed on each wheel, a braking force command from the braking control device 15, and a sensor signal from a steering wheel angle detection device 21 via a steering control device 8 (described later). The output of the braking control device 15 is connected to a brake control mechanism 13 having a pump and a control valve, and is capable of generating any braking force at each wheel independently of the driver's brake pedal operation.

[0021] The braking control device 15 estimates vehicle spin, drift-out, and wheel lock based on the input of sensor signals, and generates braking forces on the corresponding wheels to suppress these, thereby improving the driver's driving stability. Furthermore, the vehicle control device 1 can generate any braking force in the vehicle 100 by sending a brake command to the braking control device 15, and plays a role in automatically braking the vehicle in autonomous driving without driver operation. However, the braking configuration is not limited to this configuration, and other actuators such as a brake-by-wire may also be used.

[0022] Next, the configuration for performing the steering operation will be described. While the driver is driving the vehicle 100, the steering torque detection device 7 detects the steering torque input by the driver via the steering wheel 6, and the steering wheel angle detection device 21 detects the steering wheel angle. Based on this information, the steering control device 8 controls the motor to generate assist torque. Like the vehicle control device 1, the steering control device 8 is also configured as an arithmetic processing device including, for example, a CPU, a memory, etc.

[0023] The resultant force of the driver's steering torque and the assist torque from the motor moves the steering control mechanism 10, changing the direction of the front wheels (turning the front wheels). Meanwhile, a reaction force from the road surface is transmitted to the steering control mechanism 10 according to the turning angle of the front wheels, and is then transmitted to the driver as a road reaction force.

[0024] The steering control device 8 can generate torque using a motor and control the steering control mechanism 10 independently of the driver's steering operation. Therefore, the vehicle control device 1 can control the front wheels to any turning angle by transmitting a steering force command to the steering control device 8, and therefore plays a role in automatically performing steering in autonomous driving where no driver operation is required. However, this embodiment is not limited to the steering control device 8 shown in Fig. 1, and a configuration in which steering operation is performed using another actuator such as a steer-by-wire may also be used.

[0025] Next, the configuration of the accelerator will be described. The amount of depression of the accelerator pedal 17 by the driver is detected by a stroke sensor 18 and input to an acceleration control device 19. Like the vehicle control device 1, this acceleration control device 19 is also configured as a processing device including, for example, a CPU, a memory, etc.

[0026] The acceleration control device 19 adjusts the throttle opening in accordance with the depression amount of the accelerator pedal 17 and controls the engine. In this way, the acceleration control device 19 can accelerate the vehicle 100 in accordance with the driver's accelerator pedal operation. Furthermore, the acceleration control device 19 can control the throttle opening independently of the driver's accelerator operation. Therefore, the vehicle control device 1 can generate any desired acceleration in the vehicle 100 by communicating an acceleration command to the acceleration control device 19, and plays a role in automatically accelerating the vehicle in autonomous driving where no driver operation is required.

[0027] [Configuration of Vehicle Control Device] Next, the configuration of the vehicle control device 1 that performs autonomous driving control and is implemented in the vehicle 100 of this embodiment will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example configuration of the vehicle control device 1. As shown outside the blocks in Fig. 2, the vehicle control device 1 is configured by, for example, a computer that is an information processing device. That is, the computer that serves as the vehicle control device 1 includes a CPU (Central Processing Unit) 211 that is a processor, a memory 212, an input / output unit 213, and an interface 214.

[0028] The memory 212 may be a memory such as a read-only memory (ROM) or a random access memory (RAM), or may be a storage device such as a hard disk drive (HDD) or a solid state drive (SSD). The memory 212 stores information such as programs, parameters (including thresholds), various risk maps, driving conditions, and history for operating the vehicle control device 1. Furthermore, by reading and executing programs under the control of the CPU 211, the memory 212 is configured with a plurality of processing function units, which will be described later.

[0029] 1, and also performs output processing of command values ​​of the actuators 10, 13, and 20. The interface 214 performs processing of transmitting information to and from other information processing devices in the vehicle 100, such as the braking control device 15, and also performs processing of sending and receiving information to and from the outside via the communication device 23 shown in FIG.

[0030] Next, we will explain the processing function units configured in the vehicle control device 1. The vehicle control device 1 is configured with the following processing function units: an automatic driving planning unit 201, an automatic parking planning unit 202, a vehicle motion control unit 203, an actuator control unit 204, and a risk map generation unit 205. These processing function units can communicate with each other via a vehicle network 206. The vehicle network 206 may be connected wirelessly as well as via a wired connection.

[0031] The autonomous driving planning unit 201 plans the operation of the host vehicle 100 to automatically drive the vehicle 100 to a destination. The autonomous parking planning unit 202 plans the operation of the host vehicle to automatically park the vehicle 100 in a parking space in a parking lot or the like. The vehicle motion control unit 203 generates command values ​​for controlling the motion of the vehicle 100 during autonomous driving. The actuator control unit 204 controls each actuator such as the engine, brake, and steering based on the command values ​​output from the vehicle motion control unit 203. The risk map generation unit 205 generates a risk map of vehicles and obstacles present in the vicinity of the host vehicle, including oncoming vehicles.

[0032] The actuator control unit 204 may be implemented in hardware different from that of the vehicle control device 1, such as an engine controller or a brake controller. Furthermore, each of these processing function units may be implemented as a different arithmetic processing device (controller).

[0033] [Configuration of Risk Map Generator] Next, the configuration of the risk map generator 205 will be described with reference to Fig. 3. Fig. 3 is a block diagram showing an example of the configuration of the risk map generator 205.

[0034] The risk map generating unit 205 is supplied with information from a radar 301, a stereo camera 302, a vehicle sensor 303, and a lidar 304 as sensors that recognize the outside world.

[0035] That is, the radar 301 can measure the distance and direction to the target by emitting radio waves toward the target and measuring the reflected waves. The information (point cloud data) on the distance and direction to the target obtained by the radar 301 is supplied to the risk map generation unit 205.

[0036] The stereo camera 302 can also obtain depth information by simultaneously capturing images of an object from a plurality of different directions. The depth information obtained by the stereo camera 302 is supplied to the risk map generator 205.

[0037] Furthermore, vehicle sensor 303 is a collective term for multiple sensors mounted on vehicle 100. Vehicle sensor 303 is a group of sensors that obtain information such as vehicle speed and tire rotation speed, information calculated using GNSS (Global Navigation Satellite System) for the average position of the autonomously driven vehicle, destination information input by an occupant of the autonomously driven vehicle using a navigation system as an interface, and destination information specified by an operator or the like in a remote location using wireless communication such as a telephone line. The information obtained by these vehicle sensors 303 is supplied to risk map generation unit 205.

[0038] The lidar 304 measures scattered light in response to pulsed laser irradiation and detects the distance to a distant target. Information (point cloud data) on the distance to a distant target obtained by the lidar 304 is supplied to the risk map generation unit 205. However, the lidar 304 is not necessarily required for the risk map generation unit 205.

[0039] The risk map generation unit 205 includes a sensor information processing unit 305, a map information processing unit 306, a three-dimensional object behavior prediction unit 307, a memory unit 308, a stay risk map generation unit 309A, and a self-position estimation processing unit 310. Furthermore, the risk map generation unit 205 includes a fixed obstacle risk map generation unit 309B, a moving obstacle risk map generation unit 309C, and the like.

[0040] The sensor information processing unit 305 receives input of detection information from external sensors including a radar 301, a stereo camera 302, a vehicle sensor 303, and a lidar 304. The sensor information processing unit 305 obtains information on the surrounding environment (for example, object information on moving objects present around the vehicle) from the detection information from the external sensors. The sensor information processing unit 305 functions as a recognition unit that performs recognition processing to recognize the situation around the vehicle 100 based on information detected by the external sensors provided in the vehicle 100.

[0041] Specific object information includes attribute information of pedestrians, bicycles, vehicles, etc., as well as their current positions and current velocity vectors. Moving objects also include parked vehicles, which may move in the future even if their current velocity is zero.

[0042] The storage unit 308 stores road information and traffic light information from the point where the vehicle starts autonomous driving to the destination point and the surrounding area, route information from the current position to the destination point, a traffic rule database for the section to be traveled, etc. The storage unit 308 also stores a point cloud database used by the self-position estimation processing unit 310.

[0043] The map information processing unit 306 obtains lane center line information and traffic light information for roads required for autonomous driving from the information stored in the memory unit 308. The map information processing unit 306 then organizes information such as the lighting status of traffic lights through which the autonomous vehicle is scheduled to pass, and converts this lane center line information and traffic light information into information in a usable format.

[0044] Furthermore, the self-position estimation processing unit 310 estimates the location of the vehicle based on surrounding information obtained by the external sensor, the point cloud database, the steering angle of the vehicle 100, the vehicle speed, and information obtained by GNSS.

[0045] The output of the sensor information processing unit 305 , the output of the map information processing unit 306 , and the output of the self-position estimation processing unit 310 are supplied to a three-dimensional object behavior prediction unit 307 .

[0046] The three-dimensional object behavior prediction unit 307 calculates future position and speed information (object prediction information) for each three-dimensional object based on this input information. A three-dimensional object is a general term for moving objects, stationary objects, etc. that exist around the vehicle. In order to predict the movement of each moving object, the three-dimensional object behavior prediction unit 307 first predicts the position R(X(T), Y(T)) of each object at a future time T based on the object information. As a specific prediction method, when the current position of a moving object is Rn0(Xn(0), Yn(0)) and the current speed is Vn(Vxn, Vyn), the three-dimensional object behavior prediction unit 307 performs prediction calculations based on, for example, the following linear prediction formula, [Equation 1]:

[0047]

[0048] The calculation of Equation 1 is performed under the assumption that each object moves at a constant velocity in a straight line, maintaining its current speed in the future. This makes it possible to predict many objects in a short period of time. Alternatively, as another calculation method, position and speed information of other vehicles obtained by sensors or image information obtained by a camera may be input into a trained neural network model to obtain prediction results of the future position and speed information of other vehicles. This calculation also makes it possible to calculate the reliability of the prediction results.

[0049] The stagnation risk map generation unit 309A generates a stagnation risk map indicating the stagnation risk of surrounding vehicles, such as oncoming vehicles, based on the processing results of the sensor information processing unit 305, the map information processing unit 306, the self-position estimation processing unit 310, and the three-dimensional object behavior prediction unit 307. Collisions and deadlocks can occur when the host vehicle does not behave cooperatively with surrounding objects, such as vehicles, pedestrians, and bicycles. For such scenes, the stagnation risk map generation unit 309A generates a stagnation risk map using a predicted trajectory calculated based on the target behavior of the object to be coordinated, and outputs the generated stagnation risk map.

[0050] The fixed obstacle risk map generation unit 309B generates a fixed obstacle risk map that indicates the fixed obstacle risk around the vehicle, based on the processing results of the sensor information processing unit 305, the map information processing unit 306, the self-position estimation processing unit 310, and the three-dimensional object behavior prediction unit 307. Arrows between these processing units and the fixed obstacle risk map generation unit 309B are omitted.

[0051] Similar to the fixed obstacle risk map generation unit 309B, the moving obstacle risk map generation unit 309C generates a moving obstacle risk map indicating the moving obstacle risk around the vehicle based on the processing results of each processing unit.

[0052] [Stagnation Risk Map Generation Processing] Next, the stagnation risk map generation processing by the risk map generation unit 205 (stagnation risk map generation unit 309A) will be described with reference to FIG.

[0053] 4 is a flowchart showing an example of the retention risk map generation process. First, the risk map generation unit 205 acquires various information including surrounding information (step S401). Next, the three-dimensional object behavior prediction unit 307 of the risk map generation unit 205 predicts the target behavior of the target vehicle based on the acquired surrounding information, such as the current position and speed information of oncoming vehicles that need to pass each other in cooperation with the host vehicle, and road environment information on the road environment on which the host vehicle and the target vehicle are traveling (step S402). Note that the road environment information may include road shape, information on objects present in the vicinity, surrounding map information, congestion information, etc.

[0054] Then, the stagnation risk map generation unit 309A of the risk map generation unit 205 determines whether the target vehicle is a moving object that requires cooperative action with the host vehicle (step S403). If it is determined in step S403 that cooperative action is required (YES in step S403), the stagnation risk map generation unit 309A generates and outputs a cooperative action plan and a stagnation risk map based on the target action of the target vehicle that requires cooperative action (step S404). On the other hand, if it is determined in step S403 that cooperative action is not required (NO in step S403), the stagnation risk map generation unit 309A ends the processing without generating a stagnation risk map.

[0055] The generated results of the cooperative action plan and the congestion risk map will be explained using FIGS. 5A and 5B. FIGS. 5A and 5B are diagrams showing examples of the generated results of the cooperative action plan and the congestion risk map. Both FIGS. 5A and 5B show an example of a situation in which the host vehicle M1 and an oncoming vehicle M2, which is a target for cooperative action, are about to pass each other on a narrow road. The example of FIG. 5A shows a case in which the road boundary 741 on the left side of the host vehicle M1 is not linear and there is a turnout road 2200, while the example of FIG. 5B shows a case in which the road boundary 741 on the left side of the host vehicle M1 is linear and the turnout road 2200 is on the opposite side (the right side as viewed from the host vehicle M1).

[0056] 5A and 5B , the risk map generation unit 205 predicts the destination point 2201 of the oncoming vehicle M2 at a future time (a predetermined time until the completion of the cooperative behavior) based on various information. Then, the risk map generation unit 205 predicts the route that the oncoming vehicle M2 will take to reach the predicted destination point 2201, and calculates the area that the oncoming vehicle M2 will pass along that route. The result of the calculation by the risk map generation unit 205 becomes a retention risk map 701. The retention risk map 701 in FIG. 5A is an example of a case where the host vehicle M1 moves to the turnout path 2200, and the retention risk map 701 in FIG. 5B is an example of a case where the oncoming vehicle M2 moves to the turnout path 2200.

[0057] Furthermore, the risk map generation unit 205 calculates a host vehicle waiting position 2202 where the host vehicle M1 should wait, based on the arrival target point 2201 of the oncoming vehicle M2 and the retention risk map 701. Then, the risk map generation unit 205 considers a sequence for executing each waiting action in series or in parallel so that the actions of the host vehicle M1 and the oncoming vehicle M2 are both completed, and sets the sequence that can realize this as a cooperative action plan.

[0058] As a cooperative action plan, the risk map generation unit 205 first formulates a plan for the host vehicle M1 to enter the host vehicle waiting position 2202 (step 1), then formulates a plan for the oncoming vehicle M2 to pass alongside the host vehicle M1 (step 2), and finally formulates a plan for the host vehicle M1 to exit the waiting path (step 3).If a sequence that completes such cooperative action cannot be found, the target point, the delay risk map, and the host vehicle waiting position are reviewed again, and a sequence is recursively searched for.

[0059] [Configuration of Autonomous Driving Planning Unit] Next, the configuration of the autonomous driving planning unit 201 will be described with reference to Fig. 6. Fig. 6 is a block diagram showing an example configuration of the autonomous driving planning unit 201. The autonomous driving planning unit 201 includes a driving planning unit 501 that calculates a target trajectory, a driving mode management unit 509, a trajectory planning unit 507, and a peripheral object approach determination unit 508.

[0060] The driving plan unit 501 is supplied with a delay risk map, a cooperative action plan, lane information, map information, environmental information, route information, UI (User Interface) information, etc. The driving plan unit 501 calculates weights of candidate target actions that the vehicle can take (hereinafter referred to as "candidate target action weights") based on the route information, environmental information, detection information from the vehicle sensors 303, occupant status information, etc.

[0061] The target action candidate weights are weights for actions that the vehicle can take, such as an LK candidate weight for maintaining the lane in which the vehicle is currently located, an LC candidate weight for changing lanes from the current lane to an adjacent lane, an OA candidate weight for avoiding an obstacle ahead, and a CO candidate weight for cooperative behavior with other vehicles. For example, when traveling on a straight road, in a situation where there are no vehicles or objects ahead that need to be avoided and the route information suggests that there is no need to change lanes to an adjacent lane, the weights for LK and LC are 100, 0, 0, and 0, respectively.

[0062] The trajectory planning unit 507 includes a lane keeping trajectory generation unit (LK) 502, a lane change trajectory generation unit (LC) 503, an obstacle avoidance trajectory generation unit (OA) 504, a cooperative behavior trajectory generation unit (CO) 505, and a trajectory arbitration unit 506. The trajectory planning unit 507 is an example of a trajectory planning unit that performs a driving trajectory generation process that generates a driving trajectory from the current position of the vehicle to a target position. The lane keeping trajectory generation unit 502 generates a trajectory for keeping the host vehicle in the center of the lane in which it is currently traveling. The lane change trajectory generation unit 503 generates a trajectory for changing lanes to an adjacent lane from the lane in which the host vehicle is currently traveling.

[0063] The obstacle avoidance trajectory generation unit 504 generates a trajectory that avoids objects that exist in the lane in which the host vehicle is currently traveling and that become obstacles to traveling. The cooperative behavior trajectory generation unit 505 generates a cooperative behavior trajectory for cooperative behavior with a peripheral object such as an oncoming vehicle. For example, as the cooperative behavior trajectory, a trajectory for moving the host vehicle to a waiting space at a waiting position where the host vehicle and a moving body (e.g., an oncoming vehicle) pass each other is generated. The generation of the cooperative behavior trajectory takes into consideration the determination result by the peripheral object approach determination unit 508 as to whether or not an object around the host vehicle is approaching the host vehicle.

[0064] The peripheral object approach determination unit 508 determines the approach of a peripheral object by comparing distance information to the peripheral object obtained from detection information of the external sensor with a threshold (distance determination). The threshold is stored in advance in the memory 212 ( FIG. 2 ). The external sensor is, for example, a camera, a radar 301, a stereo camera 302, a lidar 304, etc.

[0065] The trajectory arbitration unit 506 evaluates each of the lane keeping trajectory, lane change trajectory, obstacle avoidance trajectory, and cooperative behavior trajectory based on the degree of safety relative to surrounding objects and the weight of the candidate target behavior, selects the trajectory with the best evaluation, and generates the target trajectory.

[0066] The driving mode management unit 509 calculates previously selected information for calculating the target behavior candidate weights at the next sampling time, based on the target driving mode selected by the trajectory arbitration unit 506, the trajectory evaluation values ​​based on each target behavior candidate, and the manual driving request from the cooperative behavior trajectory generation unit 505. For example, if LK (maintain the current lane: lane keep) is selected based on the evaluation values ​​LK = 60, LC = 40, OA = 0, and CO = 0, the driving mode management unit 509 generates previously selected current driving information (current driving mode) so that LK is more likely to be selected at the next sampling time for the sake of behavior continuity.

[0067] [Configuration of the cooperative behavior trajectory generation unit] Next, the configuration of the cooperative behavior trajectory generation unit 505 will be described with reference to Fig. 7. Fig. 7 is a block diagram showing an example configuration of the cooperative behavior trajectory generation unit 505. The cooperative behavior trajectory generation unit 505 is supplied with various types of information, such as a stay risk map, cooperative behavior plan information, environmental information, lane information, map information, and peripheral object approach determination information.

[0068] The cooperative behavior trajectory generation unit 505 includes a cooperative behavior state management unit 600, a turnaround position and attitude generation unit 601, a cooperative behavior basic trajectory generation unit 602, a peripheral object approach steering limit setting unit 603, a peripheral object approach turnaround position generation unit 604, a peripheral object approach trajectory generation unit 605, and a cooperative behavior trajectory selection unit 606. As can be seen from their names, the peripheral object approach steering limit setting unit 603, the peripheral object approach turnaround position generation unit 604, and the peripheral object approach trajectory generation unit 605 are used when a peripheral object (e.g., a target vehicle of cooperative behavior) approaches the host vehicle.

[0069] The cooperative behavior state management unit 600 has a function of determining (first determination) whether the vehicle is capable of tracking the cooperative behavior trajectory. Furthermore, the cooperative behavior state management unit 600 has a function of determining (second determination) whether the vehicle is in a state where a peripheral object is approaching when the vehicle is unable to track the cooperative behavior trajectory. The cooperative behavior state management unit 600 outputs each determination result to the cooperative behavior trajectory selection unit 606.

[0070] The turnaround position and attitude generating unit 601 generates candidate target turnaround positions and attitudes in the cooperative action plan. The cooperative action basic trajectory generating unit 602 calculates a traveling trajectory (cooperative action basic trajectory) in the cooperative action mode based on the candidate target turnaround positions and attitudes generated by the turnaround position and attitude generating unit 601.

[0071] The steering restriction setting unit 603 for when a peripheral object is approaching performs a setting (steering restriction for when a peripheral object is approaching) to restrict the steering of the vehicle when a peripheral object is approaching, based on the judgment of the proximity of a peripheral object and the cooperative behavior basic trajectory generated by the cooperative behavior basic trajectory generating unit 602.

[0072] The peripheral object approaching retreat position generation unit 604 generates a retreat position when a peripheral object is approaching (a retreat position when a peripheral object is approaching) based on the peripheral object approach determination and the peripheral object approaching steering limit set by the peripheral object approaching steering limit setting unit 603.

[0073] The peripheral object approach time trajectory generation unit 605 generates a cooperative behavior trajectory when a peripheral object is approaching based on the peripheral object approach determination, the peripheral object approach time steering limit set by the peripheral object approach time steering limit setting unit 603, and the peripheral object approach time evacuation position generated by the peripheral object approach time evacuation position generation unit 604.

[0074] The cooperative behavior trajectory selection unit 606 selects an appropriate trajectory from the cooperative behavior basic trajectory calculated by the cooperative behavior basic trajectory generation unit 602 and the cooperative behavior trajectory when approaching a peripheral object generated by the peripheral object approach trajectory generation unit 605, and controls the traveling of the vehicle 100. This selection reflects the result of the first determination or the second determination made by the cooperative behavior state management unit 600.

[0075] For example, if the first determination determines that the vehicle can follow the cooperative behavior trajectory, the peripheral object approaching trajectory generating unit 605 selects the cooperative behavior basic trajectory. Also, if the second determination determines that the vehicle cannot follow the cooperative behavior trajectory and is in a peripheral object approaching state, the peripheral object approaching trajectory generating unit 605 selects the cooperative behavior trajectory when a peripheral object is approaching.

[0076] [Escape Position and Orientation Generation Processing] Next, the escape position and orientation generation processing performed by the escape position and orientation generation unit 601 will be described with reference to Fig. 8. Fig. 8 is a flowchart showing an example of the escape position and orientation generation processing.

[0077] First, the turnaround position and attitude generation unit 601 acquires various information such as a delay risk map, cooperative action plan information, environmental information, lane information, map information, etc. (Step S801) Next, the turnaround position and attitude generation unit 601 generates candidate target turnaround positions and attitudes for the current cooperative action step in the cooperative action plan (Step S802).

[0078] (Generation Process of Target Turn-Away Position and Orientation Candidates) Next, the generation process of target turn-away position and orientation candidates in step S802 will be described with reference to FIG. 9. FIG. 9 is a diagram showing an example of the generation process of target turn-away position and orientation candidates. In the example of FIG. 9, an oncoming vehicle M2 with which the host vehicle M1 should take coordinated action is present ahead of the host vehicle M1. Also, a turn-away space is present ahead of the host vehicle M1 on the left side. A road boundary 741 is shown in FIG. 9.

[0079] The stay risk map 701 generated by the stay risk map generation unit 309A ( FIG. 3 ) exists in front of and on the host vehicle M1. Therefore, in order to realize the cooperative behavior of passing other vehicles, the host vehicle M1 must move to a location where the stay risk map 701 does not exist and wait there. Therefore, the stay risk map generation unit 309A generates turnout position and orientation candidates (N1, θ1) to (N5, θ5) in a turnout space that is a surrounding drivable area (inside the road boundary 741) where the stay risk map 701 does not exist. N represents the planar position coordinate, and θ represents the vehicle's headway angle. For example, the headway angle is the angle between the direction of the road and the vehicle. The number of turnout position and orientation candidates generated at this time is arbitrary, but the locations for generation are selected to be close to the stay risk map 701 within a range that takes into account the size of the host vehicle M1.

[0080] [Cooperative Behavior Trajectory Generation Processing] Next, cooperative behavior basic trajectory generation processing by the cooperative behavior basic trajectory generation unit 602 will be described with reference to the flowchart of Fig. 10. Fig. 10 is a flowchart showing an example of cooperative behavior basic trajectory generation processing.

[0081] First, the cooperative behavior basic trajectory generation unit 602 acquires various information such as a delay risk map, cooperative behavior plan information, environmental information, lane information, map information, and peripheral object approach determination (step S1001). Next, the cooperative behavior basic trajectory generation unit 602 generates a candidate arrival position, which is a destination position where the driving mode is changed from the cooperative behavior mode to another mode after passing the turn-off position (step S1002). Thereafter, the cooperative behavior basic trajectory generation unit 602 generates a candidate cooperative behavior trajectory that passes through the candidate turn-off position and the candidate arrival position (step S1003).

[0082] Furthermore, the cooperative behavior basic trajectory generating unit 602 evaluates the generated cooperative behavior trajectory candidates and selects the optimal cooperative behavior trajectory (step S1004). In the present invention, the selected cooperative behavior trajectory is called a "cooperative behavior basic trajectory" and is used as the basis for the cooperative behavior trajectory when approaching a peripheral object, which is generated by the peripheral object approach trajectory generating unit 605.

[0083] Finally, the cooperative behavior basic trajectory generating unit 602 stores information on the cooperative behavior trajectory (cooperative behavior basic trajectory) selected in step S1004 in the storage unit 308 (memory 212 in FIG. 2) (step S1005).

[0084] Here, the process of evaluating the generated cooperative behavior trajectory candidates in step S1004 and selecting the optimal cooperative behavior trajectory will be described below. To select the optimal cooperative behavior trajectory from the generated cooperative behavior trajectory candidates, it is conceivable to use, for example, the equation of [Equation 2] as a method of evaluating each cooperative behavior trajectory candidate. As described above, cooperative behavior trajectory candidates are generated for each turnaround position candidate and arrival position candidate, and it is necessary to select the optimal one from among these candidates. Therefore, for each cooperative behavior trajectory candidate, the following evaluation function was used to determine an overall evaluation value for each cooperative behavior trajectory candidate:

[0085]

[0086] In equation [2], a weighting coefficient (i) is set for each of the five individual evaluation values, and the sum of the products of the weighting coefficient (i) and the individual evaluation value (i) is calculated. The weighting coefficient is a value determined in advance depending on the scene. The smaller the overall evaluation value, the more preferable the trajectory candidate is. The five individual evaluation values ​​are an evaluation of safety, an evaluation of convenience, an evaluation of ride comfort, an evaluation of discomfort, and an evaluation of the feeling of pressure from oncoming vehicles.

[0087] Safety evaluation involves assessing whether a sufficient distance is maintained to avoid contact with or approaching nearby objects while traveling along a passing route. To this end, a safety evaluation value is calculated using the distance to nearby objects and a manifest risk value (probability) (not shown). For example, a method can be considered in which the evaluation value is the reciprocal of the minimum distance between a road boundary and a route candidate or a value (probability) of a collision risk map (not shown). In this embodiment, the route is intended to represent planar coordinate information, and the trajectory is intended to include time information in addition to route information.

[0088] Regarding the evaluation of convenience, when an oncoming vehicle is present, if cooperative behavior, in this case, the act of passing, takes time, the oncoming vehicle will have to wait longer, which may lead to distrust among other vehicles and the occupants of the vehicle itself. Therefore, assuming that the speed of the vehicle itself is constant and does not depend on the length of the route, the evaluation value of convenience is calculated using the length of the evacuation route of the vehicle itself. For example, a method can be considered in which the length of the route on the entrance side (the route entering the evacuation area) is used as the evaluation value.

[0089] The evaluation of ride comfort is based on whether the acceleration and jerk (time derivative of acceleration) occurring in the vehicle when the vehicle follows the evacuation route increase. For example, a route with a large lateral acceleration and a long duration of lateral acceleration is considered to have a poor ride comfort. Also, when the turning radius of the route is small, the larger the total turning angle, the less comfortable the route is. Therefore, it is conceivable to use the value of (reciprocal of turning radius) x (total turning angle) as an evaluation index.

[0090] Regarding the evaluation of discomfort, it is considered that the occupants of the vehicle will feel uncomfortable when passing other vehicles, which requires them to turn around, and an evaluation value is calculated using the number of times they turn around.

[0091] In evaluating the sense of pressure from oncoming vehicles, a route in which the own vehicle gets too close to an oncoming vehicle may cause distrust or fear among the occupants of the own vehicle and the other vehicle. Therefore, an evaluation value is calculated based on the distance between the candidate cooperative behavior trajectory of the own vehicle and the oncoming vehicle. For example, a method can be considered in which the reciprocal of the minimum distance between the candidate cooperative behavior trajectory and the center position coordinates of the other vehicle is used as the evaluation index.

[0092] (Processing for Generating Arrival Position Candidates) Next, a method for generating arrival position candidates in step S1002 will be described with reference to Fig. 11. Fig. 11 is a diagram showing examples of arrival position candidates.

[0093] Here, the candidate arrival position is selected as a point where a transition from the cooperative behavior mode to another mode (e.g., lane keeping mode) is expected. In the example of Fig. 11 , in a situation where a location where passing is possible (e.g., a turning-off space) is set by a road boundary 741, the candidate arrival position is a position on the center line 1101 of the driving lane of the host vehicle M1 stored in the map information, which is line-symmetrical with the start point of the collaborative behavior mode and each candidate turning-off position / posture point (a position where the length of L is equal in Fig. 11 ).

[0094] (Process for generating cooperative behavior trajectory candidates) Next, a method for generating cooperative behavior trajectory candidates in step S1003 will be described with reference to FIG. 12. FIG. 12 is a diagram showing an example of a cooperative behavior trajectory (passing trajectory). To generate a cooperative behavior trajectory, a cooperative behavior target route is generated, and then a cooperative behavior target speed for traveling on the generated route is generated. As a process for generating the cooperative behavior target route, for example, a method can be considered in which, using the aforementioned retention risk map and evacuation position and posture candidates, arcs, straight lines, and clothoid curves are generated for the target evacuation position and posture candidates and arrival position candidates.

[0095] 12 shows the result of generating a target route for the host vehicle M1 from its current position to the waiting position using straight lines and arc curves. In the example of FIG. 12, the route for the host vehicle M1 from its current position to the waiting position is a straight line with a road boundary 741 that is approximately parallel to the center line 1101 until the waiting position begins. After that, at the point where the road boundary 741 deviates to the outside of the road, the route toward the waiting position (N3, θ3) becomes a curved route using arc curves. Then, after the host vehicle M1 passes the oncoming vehicle M2, the route returns to the center line 1101.

[0096] Next, a description will be given of a retention risk map and a cooperative behavior trajectory when an oncoming vehicle is present ahead of the host vehicle, using a schematic diagram of a typical scene shown in FIG. 13 . FIG. 13 shows an example of a typical scene of a retention risk map and a cooperative behavior trajectory when an oncoming vehicle M2 is present ahead of the host vehicle M1. Here, as shown in the upper part of FIG. 13 , the host vehicle M1 and the oncoming vehicle M2, which is the target of cooperative behavior, are traveling with a passing point between them. A waiting area is located on the left side of the host vehicle M1. In addition, a retention risk map 701 calculated based on the surrounding environment and the location of the oncoming vehicle M2 is acquired. In this situation, as shown in the lower part of FIG. 13 , a waiting position 702 for the host vehicle M1, a trajectory 704 leading from the current position to the waiting position 702, and a trajectory 705 leading from the waiting position 702 to a candidate arrival position 703 are calculated based on the retention risk map 701.

[0097] [When Occupants of the Host Vehicle Feel Uneasy or Fearful] Next, referring to FIG. 14 , a case where an occupant of the host vehicle feels uneasy or frightened due to an oncoming vehicle or the surrounding environment while traveling along the cooperative behavior trajectory (following the planned cooperative behavior trajectory) described above will be described. FIG. 14 is a diagram showing an example where an occupant of the host vehicle feels uneasy or frightened while traveling along the cooperative behavior trajectory. Here, as shown in the upper part of FIG. 14 , the host vehicle M1 is attempting to achieve cooperative behavior by following trajectories 704 and 705 on the cooperative behavior trajectory. During this behavior process, an oncoming vehicle M2, which was located far away when the cooperative behavior trajectory was planned, approaches the host vehicle M1. The oncoming vehicle M2 will not collide with the host vehicle M1, and even if the host vehicle M1 withdraws to the withdrawal position 702, it will not prevent the oncoming vehicle M2 from passing each other.

[0098] The vehicle control device 1 of this embodiment plans a waiting position and a trajectory to the waiting position in order to reduce the fear felt by the occupants of the host vehicle M1 when the host vehicle M1 is waiting to wait, even though the host vehicle M1 can pass the oncoming vehicle M2 without interfering with the oncoming vehicle M2. As a result, it is desirable to wait in a safer and more secure state in the host vehicle M1 to a location where the host vehicle M1 can pass the oncoming vehicle M2 without causing anxiety to the occupants.

[0099] For this purpose, in this embodiment, when a peripheral object is approached as shown in Figure 7, a steering limit setting unit 603 for when a peripheral object is approached, a turnaround position generating unit 604 for when a peripheral object is approached, and a trajectory generating unit 605 for when a peripheral object is approached generate a trajectory for when a peripheral object is approached.

[0100] 14, a waiting position 1401 for the host vehicle M1, a trajectory 1402 from the current position to the waiting position 1401, and a trajectory 1403 from the waiting position 1401 to the arrival position candidate 703 are calculated. The waiting position 1401 is farther away from the oncoming vehicle M2 during passing than the initial waiting position 702. Also, at the waiting position 702 in the upper part of FIG. 14, the host vehicle M1 was parallel to the road (headway angle = 0 degrees), but the posture of the host vehicle M1 upon reaching the waiting position 1401 is not parallel but oblique to the road and the oncoming vehicle M2 (headway angle ≠ 0 degrees).

[0101] [Steering Limit Setting Process When Approaching a Peripheral Object] Next, the steering limit setting process when approaching a peripheral object performed by the steering limit setting unit 603 will be described with reference to Fig. 15. Fig. 15 is a flowchart showing an example of the steering limit setting process when approaching a peripheral object.

[0102] First, the peripheral object approach steering restriction setting unit 603 acquires various information such as a congestion risk map, cooperative action plan information, environmental information, lane information, map information, and peripheral object approach determination (step S1501).

[0103] Next, when approaching a peripheral object, the peripheral object approach steering limit setting unit 603 calculates a target yaw angle (yaw angle profile) of the vehicle 100 when following the target cooperative behavior trajectory (cooperative behavior basic trajectory) calculated by the cooperative behavior basic trajectory generation unit 602 (step S1502). Specifically, the target yaw angle of the vehicle 100 can be calculated from the tangential direction of the cooperative behavior basic trajectory.

[0104] Next, the peripheral object approach steering limit setting unit 603 calculates the relative yaw angle between the host vehicle M1 and the oncoming vehicle M2 based on the predicted trajectory of the oncoming vehicle and the target yaw angle calculated in step S1502. The relative yaw angle is the angle between the traveling direction of the host vehicle M1 and the traveling direction of the oncoming vehicle M2. The peripheral object approach steering limit setting unit 603 also calculates the relative yaw angle change rate using the time series prediction result of the relative yaw angle (step S1503).

[0105] Next, the peripheral object approach steering limit setting unit 603 calculates the steering limit angle based on the traveling speed of the host vehicle and the cooperative behavior trajectory (step S1504). The peripheral object approach steering limit setting unit 603 calculates the position on the cooperative behavior trajectory where the relative yaw angle change rate is equal to or greater than a predetermined value. For example, it is desirable to limit the upper limit of the yaw angle change rate to approximately 1.0 [rad / sec] or less, taking into account ride comfort and fear.

[0106] In addition, by setting the allowable range for the relative yaw angle to be constant or increasing, the steering limit can prevent the approach of the oncoming vehicle M2 due to rotation in the yaw angle direction, thereby reducing the sense of fear. If the relative yaw angle is set to be greater than zero, the host vehicle will move in a direction away from the oncoming vehicle.

[0107] Furthermore, the peripheral object approach steering limit setting unit 603 (an example of a steering limit setting unit) may set the magnitude of the relative yaw angle or the allowable range of the change in the relative yaw angle instead of the relative yaw angle change rate. Even with such settings, the same effect as described above can be obtained. When the peripheral object approach determination unit 508 determines that a moving object is approaching the host vehicle, the peripheral object approach trajectory generation unit 605 (an example of a trajectory planning unit) generates (plans) a trajectory for the movement of the host vehicle so that the relative yaw angle after the host vehicle moves or the amount of change in the relative yaw angle accompanying the movement of the host vehicle falls within the allowable range.

[0108] [Retreat position generation process when a peripheral object is approaching] Next, the process of generating a retreat position when a peripheral object is approaching, performed by the retreat position generation unit 604, will be described with reference to Fig. 16. Fig. 16 is a flowchart showing an example of the process of generating a retreat position when a peripheral object is approaching.

[0109] First, the peripheral object approach time evacuation position generation unit 604 acquires various information such as a stay risk map, cooperative action plan information, environmental information, lane information, map information, and peripheral object approach determination (step S1601).

[0110] Next, when the vehicle M1 approaches a peripheral object, the peripheral object approach time turn-off position generating unit 604 generates a turn-off position for the vehicle M1 when the vehicle M1 approaches a peripheral object (step S1602). In this embodiment, the turn-off position when the vehicle M1 approaches a peripheral object is generated at a position that satisfies the steering limit calculated by the peripheral object approach time steering limit setting unit 603 and is not on the retention risk map 701.

[0111] Finally, the peripheral object approach time turn-off position generating unit 604 stores the generated peripheral object approach time turn-off position information in the storage unit 308 (step S1603). In this way, when the host vehicle M1 is to turn off in a state where it is close to the oncoming vehicle M2, it is considered that the sense of anxiety of the occupants can be reduced by turning off the host vehicle M1 at a position that does not interfere with the oncoming vehicle M2 passing by the host vehicle M1 and at a position where the rate of change of the relative yaw angle between the host vehicle M1 and the oncoming vehicle M2 is equal to or less than a predetermined value.

[0112] [Processing for generating a trajectory of cooperative behavior when a peripheral object is approaching] Next, the processing for generating a turnaround trajectory when a peripheral object is approaching, performed by the trajectory generating unit 605 when a peripheral object is approaching, will be described with reference to Fig. 17. Fig. 17 is a flowchart showing an example of the processing for generating a turnaround trajectory when a peripheral object is approaching.

[0113] First, the peripheral object approach trajectory generation unit 605 acquires various information such as a stay risk map, cooperative action plan information, environmental information, lane information, map information, and peripheral object approach determination (step S1701).

[0114] Next, the peripheral object approach time trajectory generation unit 605 generates a candidate avoidance trajectory (cooperative behavior trajectory) for when approaching a peripheral object, which leads to the avoidance position when approaching the peripheral object (step S1702). This avoidance position when approaching a peripheral object is the avoidance position generated by the peripheral object approach time trajectory generation unit 604. The avoidance trajectory leading to the avoidance position when approaching a peripheral object can be generated using a straight line, an arc curve, or the like, as shown in Fig. 12. It is possible to generate a plurality of such trajectories by combining forward and backward movements, etc.

[0115] Therefore, the peripheral object approach trajectory generation unit 605 evaluates multiple avoidance trajectory (cooperative behavior trajectory) candidates and selects the optimal cooperative behavior trajectory candidate when the peripheral object is approaching from the evaluation results (step S1703). As a method for this, the method described in step S1004 of FIG. 10 can be considered.

[0116] Finally, the peripheral object approaching trajectory generating unit 605 stores the avoidance trajectory (cooperative behavior trajectory) when the peripheral object is approaching, selected in step S1703, in the storage unit 308 (step S1704).

[0117] [Configuration of the Running Mode Management Unit] Next, the running mode management unit 509 (FIG. 6) will be described with reference to FIG. 18. FIG. 18 is a block diagram showing an example of the configuration of the running mode management unit 509.

[0118] Various types of information, such as a target driving mode, a manual driving request, and system information, are supplied to the driving mode management unit 509. The system information includes, for example, information on the power switch of the vehicle 100 and information on the automatic driving switch of the automatic driving system.

[0119] 18, the driving mode management unit 509 manages three states: a manual driving mode 1801, a system-off mode 1802 of the automatic driving system, and an automatic driving mode 1803. The driving mode management unit 509 outputs the current mode state as the current driving mode.

[0120] When the power switch or the like of the vehicle 100 is turned on (an example of UI information in FIG. 6 ), the driving mode management unit 509 transitions from the system-off mode 1802 to the manual driving mode 1801 or the like. In addition, the driving mode management unit 509 transitions from the manual driving mode 1801 or the like to the automatic driving mode 1803 when the driver or the like turns on the automatic driving switch (an example of UI information in FIG. 6 ).

[0121] As already explained, the autonomous driving mode 1803 includes lane keeping (LK) and lane change (LC). Intermediate transition states exist between these states. In addition, a deadlock or an event requiring a change of driving direction to the driver may occur in the cooperative behavior mode (CO), and the autonomous driving system may determine that it is necessary to take over driving (give up). In such cases, the cooperative behavior trajectory selection unit 606 ( FIG. 7 ) issues a manual driving request, resulting in a transition to the manual driving mode 1801.

[0122] [Steering angle control when approaching a peripheral object] Next, control of the steering angle when approaching a peripheral object will be described with reference to Fig. 19. Fig. 19 is a diagram showing an example of control of the steering angle when approaching a peripheral object. In Fig. 19, the horizontal axis represents time and the vertical axis represents the steering angle. Furthermore, conventional steering angle control is shown by a dashed line, and the steering angle control of the present invention is shown by a solid line.

[0123] As a result of controlling the steering angle according to the above embodiment, when a peripheral object approach determination is made, the actual steering angle is controlled to be within the steering limit value by the time the host vehicle M1 moves to the waiting position, as shown in Figure 19. In the lower part of Figure 14, because the amount of steering angle operation in the negative direction is small, the posture of the host vehicle M1 that has reached the waiting position 1401 is oblique to the road and the oncoming vehicle M2. By controlling in this manner, the relative yaw angle between the host vehicle M1 and the oncoming vehicle M2 is limited, making it possible to prevent the occupants from feeling scared. In the past, the magnitude of the actual steering angle greatly exceeded the steering limit value, making the occupants more likely to feel scared.

[0124] According to this embodiment, when cooperatively traveling with a nearby moving object, a trajectory for the movement of the vehicle can be planned so as to alleviate the discomfort and fear felt by the occupants due to approaching the moving object. For example, in this embodiment, if it is predicted that the occupants will feel fear due to approaching a nearby object (oncoming vehicle) during automatic passing traveling, it is possible to plan a trajectory when approaching the nearby object, taking into account detection information from various sensors and the behavior of the vehicle and the nearby object. Furthermore, for example, it is expected to have the effect of reducing the distrust and anxiety felt by the drivers of the vehicle and the oncoming vehicle, and suppressing a reduction in the occupants' riding time.

[0125] Second Embodiment Next, a vehicle control device according to a second embodiment of the present invention will be described with reference to FIG.

[0126] [Peripheral Object Approach Determination Processing] FIG. 20 is a diagram showing an example of peripheral object approach determination processing performed by the peripheral object approach determination unit 508 of the vehicle control device according to the second embodiment.

[0127] The vehicle control device of this embodiment differs from the vehicle control device 1 of the first embodiment in the method of determining the approach of a peripheral object. As shown in Fig. 20 , the peripheral object approach determination unit 508 uses not only the distance information (distance determination) to the oncoming vehicle described in the first embodiment but also the time to collision (TTC) and the rate of change of the time to collision (ΔTTC) when determining the approach of a peripheral object such as another vehicle.

[0128] The peripheral object approach determination unit 508 uses a logical OR circuit to calculate the logical OR of the distance determination, TTC determination, or ΔTTC determination, and if it determines that any one of them indicates that a peripheral object is approaching, it outputs a peripheral object approach determination (approaching).

[0129] The peripheral object approach determination unit 508 may also perform the approach determination using at least one of the time to collision (TTC) between the moving object and the host vehicle or the rate of change of the time to collision (ΔTTC).

[0130] In this embodiment, a method for determining approach using TTC will be described with reference to the explanatory diagram of Fig. 21. Fig. 21 is a diagram showing examples of vehicle movement, time to collision (TTC), and threshold values ​​in the vehicle control device according to this embodiment.

[0131] The upper part of Figure 21 shows the distance from the host vehicle M1 to the oncoming vehicle M2 from time T = T0 to T = T2 when the host vehicle M1 and the oncoming vehicle M2 pass each other, expressed as TTC. The lower part of Figure 21 shows the results of calculating the TTC between the host vehicle M1 and the oncoming vehicle M2 at each time. In this passing scene, the TTC decreases as time passes as the host vehicle M1 takes shelter in the turnout road. Then, at time T2, the calculated TTC becomes smaller than a preset threshold. It is determined that the host vehicle is approaching another vehicle based on this inequality.

[0132] In FIG. 21, ΔTTC is constant (ΔTTC=0), but if ΔTTC is not linear, the threshold may be adjusted based on the determination of ΔTTC.

[0133] Furthermore, by using ΔTTC, it is possible to determine whether the TTC with an oncoming vehicle is increasing or decreasing over time.

[0134] 22 is a diagram showing an example of the relationship between the collision time and the threshold value when the derivative value of the collision time (ΔTTC) is positive and negative, respectively. The upper part of Fig. 22 shows a graph when ΔTTC is positive, and the lower part of Fig. 22 shows a graph when ΔTTC is negative.

[0135] The upper part of Figure 22 shows an example in which ΔTTC increases (positive rate of change) and TTC also increases halfway through (a downward convex function). When TTC is close to the threshold, there is a possibility that TTC will fall below the threshold due to noise, etc. However, if ΔTTC has a positive trend (increase), it is expected that TTC will increase over time (after time T1 in the figure). In this case, even if TTC exceeds the threshold, it is not necessary to immediately determine that an approach is occurring, and it is expected that discrepancies with the driver's sense of fear can be avoided more effectively than when determining based on TTC alone.

[0136] Furthermore, if ΔTTC has a positive trend (increase), it is possible to lower the TTC threshold. When ΔTTC is positive, the risk of collision is lower than when determined based on TTC alone, and since TTC increases over time, it is considered that the fear felt by the driver is not so great. The upper part of Figure 12 shows an example in which the TTC threshold is lowered from "Th1" to "Th2." As a result, TTC < Th1 at time T1 changes to TTC > Th2 at time T1.

[0137] The lower part of Figure 22 shows an example in which ΔTTC decreases (negative rate of change). When ΔTTC has a negative trend (decreasing), the proximity between the host vehicle and an oncoming vehicle is increasing, which may increase the driver's sense of fear compared to when determining based on the TTC threshold alone. As a countermeasure to this, when ΔTTC has a negative trend, it may be possible to increase the TTC threshold. This is expected to have the effect of detecting a sudden approach to an oncoming vehicle in advance and easing the driver's sense of fear.

[0138] 12 shows an example in which the TTC threshold is increased from "Th1" to "Th3." As a result, TTC > Th1 at time T2 becomes TTC < Th3 at time T1, making it possible to detect a sudden approach to an oncoming vehicle earlier (time T2 is brought forward to T1).

[0139] <Third embodiment> Next, a vehicle control device according to a third embodiment of the present invention will be described with reference to Fig. 23A and Fig. 23B. Fig. 23A and Fig. 23B are diagrams showing an example of vehicle movement and an occupant's visual field range in the vehicle control device according to the third embodiment. Fig. 23A shows an example of the evacuation behavior of the host vehicle when passing another vehicle, and Fig. 23B shows an example of a change in the occupant's visual field range as the vehicle moves.

[0140] The basic configuration of the vehicle control device according to this embodiment is the same as that of the vehicle control device according to embodiment 1. The vehicle control device according to this embodiment predicts the visual field state of the occupants at their future positions and the behavior of other vehicles while performing cooperative behavior, and generates an avoidance trajectory (cooperative behavior trajectory) when other vehicles approach.

[0141] 23A shows an example in which the host vehicle M1 moves to a waiting position 702 in a waiting space by following a trajectory 704 based on a cooperative behavior trajectory for an oncoming vehicle M2. The host vehicle M1 reaches a position and attitude 2300 at a certain point while traveling along the trajectory 704. When there is an occupant in the rear seat of the host vehicle M1, the field of view, particularly in front of the vehicle, is limited, and the occupant must understand the state outside the vehicle through windows on the left and right of the rear seat. It is difficult for this occupant to understand the state outside the vehicle by any means other than the windows on the left and right of the rear seat. Field of view 1601 shows the field of view from the right window of the rear seat occupant.

[0142] 23B , when the host vehicle M1 and the oncoming vehicle M2 start passing each other, the oncoming vehicle M2 approaching from the right front of the host vehicle M1 enters the field of view 1601 from the right window of the passenger in the back seat (left side of FIG. 23B ). Then, when the host vehicle M1 transitions to a position and attitude 2300 during the passing behavior and the oncoming vehicle M2 approaches the right side of the host vehicle M1, it is considered that the oncoming vehicle M2 occupies most of the field of view 1601 from the right window of the passenger in the back seat (right side of FIG. 23B ).

[0143] In this state, consider the case where control is performed to return the yaw angle of the host vehicle M1 to the waiting position 702. Because the information obtained from the occupant's field of view 1601 is limited, the occupant obtains various information such as ride comfort from the acceleration and jerk caused by the change in the yaw angle of the host vehicle M1. As a result of the change in the yaw angle of the host vehicle, acceleration (centrifugal force) occurs in the vehicle, including the occupant. Furthermore, as a result of this change in acceleration, jerk occurs, and it is thought that the occupant predicts the direction and movement of the (automatically driven) host vehicle from the acceleration and jerk information.

[0144] When an occupant tries to understand the state of another vehicle (here, oncoming vehicle M2) using information obtained from the acceleration and jerk caused by changes in the yaw angle of vehicle M1, the occupant may become more aware that vehicle M1 is approaching the other vehicle and may feel the risk of a collision.

[0145] Therefore, in a situation where there is an occupant with a restricted field of view and an oncoming vehicle M2 is approaching, it is conceivable that the occupant will feel more fear. Therefore, when another vehicle is approaching, by using the field of view restriction determination to limit the yaw angle change before the occupant begins to feel a stronger sense of fear, it is possible to alleviate the occupant's sense of fear.

[0146] [Peripheral object approach determination processing] Figure 24 is a diagram showing an example of peripheral object approach determination processing by the peripheral object approach determination unit 508 of the vehicle control device according to the third embodiment. The peripheral object approach determination unit 508 of this embodiment uses not only the information shown in the second embodiment (Figure 20) but also a view restriction determination when determining the approach of a peripheral object. Note that while an example in which the peripheral object approach determination unit 508 executes the view restriction determination processing will be described, a view restriction determination unit (an example of an occupant state estimation unit) not shown in the figure may be provided in the autonomous driving planning unit 201, and the determination result may be input to the peripheral object approach determination unit 508.

[0147] (View restriction determination processing) Next, the view restriction determination processing by the peripheral object approach determination unit 508 will be described with reference to Fig. 25. Fig. 25 is a flowchart showing an example of the view restriction determination processing.

[0148] First, the peripheral object approach determination unit 508 acquires detection information from external sensors such as the camera, radar 301, stereo camera 302, and lidar 304 (step S2501). The peripheral object approach determination unit 508 may acquire various information such as a stay risk map, cooperative action plan information, environmental information, lane information, map information, and peripheral object approach determination.

[0149] Next, the peripheral object approach determination unit 508 performs numerical modeling of the surrounding environment of the vehicle to generate a numerical model (step S2502). In order to numerically model the surrounding environment, it is desirable to model the three-dimensional shapes of the vehicle, oncoming vehicles, the surrounding road environment, etc. in a state where they are maintained. In addition, it is desirable to use a motion model suitable for moving objects (for example, if the vehicle is a motorcycle, a two-wheel model, etc.).

[0150] Next, the peripheral object approach determination unit 508 uses the above-mentioned numerical model to predict the future visual field state of the occupants on the cooperative behavior trajectory (step S2503). The peripheral object approach determination unit 508 estimates the position and attitude of each vehicle based on the target trajectories of the oncoming vehicle and the own vehicle, and predicts the visual field state of the occupants when passing an oncoming vehicle.

[0151] Finally, the peripheral object approach determination unit 508 determines whether the host vehicle and other vehicles will move while the occupant's field of view is restricted (step S2504). The result of the field of view restriction determination is input to a logical OR circuit of the peripheral object approach determination unit 508 shown in Fig. 24. Information on the field of view restriction determination may be input to the peripheral object approach determination unit 508 only when the field of view restriction determination is "true."

[0152] In this way, in this embodiment, the autonomous driving system may further include an occupant state estimation unit (the view restriction determination unit, not shown, in the aforementioned autonomous driving planning unit 201) that estimates the occupancy rate of a moving object (e.g., an oncoming vehicle) relative to the field of view of an occupant of the vehicle. When the occupancy rate of the moving object exceeds a threshold, the cooperative behavior trajectory generation unit 505 (an example of a trajectory planning unit) generates (plans) a trajectory for the movement of the vehicle so that the relative yaw angle or the amount of change in the relative yaw angle is within an allowable range (see the description of FIG. 15 ).

[0153] [Example of not limiting the yaw angle change rate] On the other hand, if the occupant's field of view is not restricted, if the occupant is not awake, or if the occupant is looking ahead of the host vehicle, a sense of fear may be less likely to be generated if the relative yaw angle with the oncoming vehicle is close to zero (180 degrees). In such cases, the yaw angle change rate limit described in step S1504 of Fig. 15 is not implemented. When passing an oncoming vehicle, keeping the relative yaw angle with the oncoming vehicle close to zero (180 degrees) can be rephrased as meaning that the host vehicle and the oncoming vehicle are traveling parallel to each other while passing each other.

[0154] Furthermore, when it is difficult to change the turn-off position, i.e., when it is difficult to generate a path that satisfies the steering restrictions, it is desirable for the peripheral object approach trajectory generation unit 605 to reduce the target speed of the host vehicle. By reducing the target speed of the vehicle, even when the yaw angle of the host vehicle is moving in a direction that brings it closer to an oncoming vehicle, it is expected that the rate of change in the yaw angle due to the reduction in speed will decrease, which is thought to have the effect of reducing the sense of fear felt by the occupants.

[0155] Furthermore, when it is determined that an oncoming vehicle is approaching, the peripheral object approach determination unit 508 (the autonomous driving planning unit 201) preferably notifies a user interface (an example of an output device) such as a display for the navigation system of the vehicle that an oncoming vehicle is approaching. For example, the display device 24 ( FIG. 1 ) can be used as the user interface.

[0156] By providing such a notification, the occupant's attention and awareness of the surroundings can be increased. This may lead to the occupant being aware of the restricted field of view and shifting their line of sight and posture in a direction that alleviates the restriction. As a result, the occupant's sense of fear may be alleviated.

[0157] In addition to visual information such as a display, it is also desirable to have an in-vehicle audio notification system (an example of an output device) that can be heard inside the vehicle. When the field of view is limited, audio notification of an approaching vehicle is considered to be effective in mitigating the fear felt by occupants.

[0158] In this embodiment, when the occupants are cooperatively behaving (passing each other) in a state where their field of view is restricted, a passing trajectory is generated that takes into account the field of view restriction at the future position of the vehicle. As a result, this embodiment prevents the occupants from feeling uneasy or scared due to the approach of an oncoming vehicle during passing.

[0159] <Fourth embodiment> Next, a vehicle control device according to a fourth embodiment of the present invention will be described with reference to Fig. 26. Fig. 26 is a diagram showing an example of approach determination processing by a peripheral object approach determination unit 508 of the vehicle control device according to the fourth embodiment.

[0160] The peripheral object approach determination unit 508 of this embodiment uses not only the information shown in the third embodiment ( FIG. 24 ) but also a fear determination when determining the approach of a peripheral object. The vehicle control device generates an avoidance trajectory (cooperative behavior trajectory) when another vehicle approaches by determining the fear level based on the result of estimating the fear level of the occupants at the future position of the vehicle while performing cooperative behavior.

[0161] Methods for estimating the occupant's sense of fear include using an occupant state detection system such as an occupant state estimation camera (eye gaze camera) installed inside the vehicle cabin or a wearable sensor worn by the occupant.

[0162] For example, when the surrounding object approach determination unit 508 determines that an oncoming vehicle is approaching, it estimates the sense of fear of the occupants of the vehicle while passing the oncoming vehicle based on the detection results of a camera that detects the line of sight information of the occupants of the vehicle.

[0163] In addition, the surrounding object approach determination unit 508 acquires biometric information such as heart rate and sweat rate detected by the occupant state detection system, and based on that biometric information, estimates the occupant's sense of fear while passing an oncoming vehicle.

[0164] When the value of the index representing the occupant's sense of fear based on the gaze information or biological information becomes equal to or greater than a predetermined value, the peripheral object approach determination unit 508 determines that the occupant is feeling fear and sets the fear determination as "true." In this way, by estimating the sense of fear using a sensor that detects the occupant's state, the cooperative behavior trajectory can be changed when an oncoming vehicle approaches, thereby reducing the occupant's sense of anxiety.

[0165] <Modifications> The present invention is not limited to the above-described embodiments, and various other applications and modifications are possible as long as they do not deviate from the gist of the present invention as set forth in the claims. For example, the above-described embodiments have been described in detail and specifically to clearly explain the present invention, and are not necessarily limited to those that include all of the components described. Furthermore, it is also possible to add, replace, or delete other components from part of the configuration of each embodiment.

[0166] For example, the passing operation described in Figures 5A and 5B is an example of passing by pulling the vehicle to the left side of the road in areas where vehicles drive on the left side of the road, and in areas where vehicles drive on the right side of the road, the left and right directions when passing are reversed.

[0167] In addition, in each of the above-described embodiments, a driving trajectory (cooperative behavior trajectory) is generated (calculated), and the vehicle is autonomously driven along the driving trajectory. Generally, a driving trajectory includes information on a coordinate position on a road and the time at which the vehicle passes through the coordinate position. In each of the embodiments, driving is controlled using the coordinate position and time information. In contrast, the driving trajectory may be information on a so-called driving route that includes only information on the coordinate position on the road, without information on the time at which the vehicle passes each coordinate position. When passing other vehicles, it is preferable to predict the position of an oncoming vehicle at each time and determine where the vehicle will move at each time. However, in situations such as when the oncoming vehicle is stopped, a driving route that includes only coordinate position information may be generated instead of a driving trajectory. By generating a driving route that includes only coordinate position information in this way, it is not necessary to handle time information, and the driving trajectory (driving route) can be generated more easily.

[0168] 2, the vehicle control device 1 configured as a computer is configured as a device that performs the processing of each of the above-described embodiments, but a program implemented in an existing vehicle control device 1 may be modified to perform similar processing. In this case, the program may be prepared in the memory within the computer shown in FIG. 2, or may be stored on an external memory, an IC card, an SD card, an optical disk, or other recording medium and transferred to the computer. Furthermore, part or all of the vehicle control device 1 may be realized by dedicated hardware such as an FPGA (Field Programmable Gate Array) or an ASIC (Application Specific Integrated Circuit).

[0169] In the above-described embodiment, the control lines and information lines of the vehicle control device and each processing function unit are shown as necessary for explanation, and not all control lines and information lines are necessarily shown in the actual product. In reality, it may be assumed that almost all components are interconnected. Furthermore, with regard to the flowcharts shown in Figures 4, 8, 10, 15 to 17, and 25, the processing order may be changed or multiple processes may be executed simultaneously as long as the processing results are the same.

[0170] Furthermore, although the term "parallel" is used in this specification, this term does not mean only "parallel" in the strict sense, but also includes the meaning of "parallel" in the strict sense and also includes the meaning of "approximately parallel" within a range in which the function can be exerted.

[0171] 1...vehicle control device, 100...vehicle, 201...automatic driving planning unit, 202...automatic parking planning unit, 203...vehicle motion control unit, 204...actuator control unit, 205...risk map generation unit, 301...radar, 302...stereo camera, 303...vehicle sensor, 304...lidar, 305...sensor information processing unit, 306...map information processing unit, 307...solid object behavior prediction unit, 308...memory unit, 309A...stay risk map generation unit, 309B...fixed obstacle risk map generation unit, 309C...moving obstacle risk map generation unit, 310...self-position estimation processing unit, 501...driving planning unit, 502...lane keeping trajectory generation unit, 503...lane change trajectory generation unit, 504...obstacle avoidance trajectory generation unit, 505...cooperative behavior trajectory generation unit, 506...trajectory arbitration unit, 507...Trajectory planning unit, 508...Peripheral object approach determination unit, 509...Driving mode management unit, 600...Cooperative behavior state management unit, 601...Retreat position and attitude generation unit, 602...Cooperative behavior basic trajectory generation unit, 603...Peripheral object approach steering limit setting unit, 604...Peripheral object approach retreat position generation unit, 605...Peripheral object approach trajectory generation unit, 606...Cooperative behavior trajectory selection unit

Claims

1. A vehicle control device comprising: an approach determination unit that determines the approach of a moving object to a host vehicle; a steering limit setting unit that sets an allowable range for a relative yaw angle, which is the angle between the traveling direction of the host vehicle and the traveling direction of the moving object, or a relative yaw angle change amount; and a trajectory planning unit that, when the approach determination unit determines that the moving object is approaching the host vehicle, plans a trajectory for the movement of the host vehicle so that the relative yaw angle after the movement of the host vehicle or the relative yaw angle change amount accompanying the movement of the host vehicle is within the allowable range.

2. The vehicle control device according to claim 1, wherein the steering limit setting unit sets the permissible range in a direction in which the relative yaw angle is constant or increases.

3. The vehicle control device according to claim 1, wherein the trajectory planning unit plans a trajectory for the movement of the host vehicle to a waiting space at a waiting position where the host vehicle and the moving body pass each other.

4. The vehicle control device according to claim 1, wherein the approach determination unit performs the approach determination using at least one of a collision time between the moving object and the host vehicle or a rate of change of the collision time.

5. A vehicle control device as described in claim 1, further comprising an occupant state estimation unit that estimates the occupancy rate of the moving body relative to the field of view of an occupant of the vehicle, wherein the trajectory planning unit plans a trajectory for the movement of the vehicle so that the relative yaw angle or the amount of change in the relative yaw angle is within the allowable range when the occupancy rate of the moving body exceeds a threshold value.

6. The vehicle control device according to claim 1, wherein the approach determination unit notifies an output device of the host vehicle of the approach of the moving object when it determines that the moving object is approaching.

7. The vehicle control device according to claim 1, wherein, when determining that the moving body is approaching, the approach determination unit estimates the sense of fear of the occupants of the vehicle while passing the moving body based on the detection results of a camera that detects the line of sight information of the occupants, and determines that the moving body is approaching based on the estimated sense of fear.

8. The vehicle control device according to claim 1, wherein, when determining that the moving body is approaching, the approach determination unit estimates the sense of fear of the occupant of the vehicle while passing the moving body based on the detection results of a sensor that detects biometric information of the occupant, and determines that the moving body is approaching based on the estimated sense of fear.

9. The vehicle control device according to claim 3, wherein the trajectory planning unit reduces the target speed of the host vehicle when it is determined that it will be difficult to change the evacuation position.

10. A vehicle control method by a vehicle control device that controls vehicle driving, comprising: a determination process for determining whether a moving object is approaching the host vehicle; a setting process for setting an allowable range for a relative yaw angle or a change in a relative yaw angle, which is the angle between the traveling direction of the host vehicle and the traveling direction of the moving object; and, when it is determined by the determination process that the moving object is approaching the host vehicle, a planning process for planning a trajectory for the movement of the host vehicle so that the relative yaw angle after the movement of the host vehicle or the change in the relative yaw angle accompanying the movement of the host vehicle is within the allowable range.

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