Vehicle control device, vehicle control method and storage medium

Abstract

This invention provides a vehicle control device, a vehicle control method, and a storage medium. It enables more appropriate autonomous driving. The vehicle control device of one embodiment includes: an identification unit that identifies the conditions surrounding the vehicle; a determination unit that determines an event determining the vehicle's driving state based on the conditions identified by the identification unit, the path to the vehicle's destination, and the vehicle's position; and a driving control unit that performs autonomous driving, controlling at least one of the vehicle's acceleration, deceleration, and steering, based on the event determined by the determination unit. The driving control unit changes the control level of the autonomous driving based on the number of events within a predetermined range of the vehicle's direction of travel.

Description

Technical Field

[0001] This invention relates to vehicle control devices, vehicle control methods, and storage media. Background Technology

[0002] Previously known technologies include repeatedly determining whether the road the vehicle has traveled exists on a high-precision map and notifying the high-precision map of the determination result (for example, see Patent Document 1).

[0003] Prior art literature

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2018-189594 Summary of the Invention

[0006] The problem that the invention aims to solve

[0007] In existing technologies, information stored in maps is used to determine whether autonomous driving can proceed mechanically. However, in existing technologies, the processing load increases in locations with a large amount of map information, sometimes making proper autonomous driving impossible.

[0008] This invention was made in consideration of such circumstances, and one of its objectives is to provide a vehicle control device, vehicle control method, and storage medium capable of more appropriate autonomous driving.

[0009] Methods for solving problems

[0010] The vehicle control device, vehicle control method, and storage medium of the present invention adopt the following structure.

[0011] (1) A first aspect of the present invention is a vehicle control device, wherein the vehicle control device comprises: an identification unit that identifies the surrounding conditions of the vehicle; a determination unit that determines an event that determines the state of the vehicle while it is driving based on the conditions identified by the identification unit, the path to the destination of the vehicle, and the position of the vehicle; and a driving control unit that performs automatic driving control of at least one of acceleration, deceleration, and steering of the vehicle based on the event determined by the determination unit, wherein the driving control unit changes the control level of the automatic driving control according to the number of events within a predetermined range of the direction of travel of the vehicle.

[0012] (2) The second aspect of the present invention, based on the first aspect, wherein when the number of events exceeds the upper limit, the driving control unit reduces the control level of the automatic driving compared to when the number of events is below the upper limit.

[0013] (3) The third aspect of the present invention is based on the second aspect, wherein the event includes a first event that is essential for the vehicle to reach the destination and a second event that is not essential for the vehicle to reach the destination, and the driving control unit reduces the control level of the automatic driving when the number of the remaining first events after removing the second event exceeds the upper limit number.

[0014] (4) The fourth aspect of the present invention is a vehicle control method, wherein a computer mounted on the vehicle performs the following processing: identifying the surrounding conditions of the vehicle, determining an event that determines the state of the vehicle while it is driving based on the identified conditions, the path to the destination of the vehicle, and the position of the vehicle, performing automatic driving control of at least one of the acceleration, deceleration and steering of the vehicle based on the determined event, and changing the control level of the automatic driving control according to the number of events within a specified range of the direction of travel of the vehicle.

[0015] (5) The fifth aspect of the present invention is a storage medium storing a program, wherein the program is configured to cause a computer mounted on a vehicle to perform the following processing: identifying the conditions around the vehicle, determining an event that determines the state of the vehicle while it is driving based on the identified conditions, the path to the destination of the vehicle, and the position of the vehicle, performing automatic driving control of at least one of the acceleration, deceleration, and steering of the vehicle based on the determined event, and changing the control level of the automatic driving control according to the number of events within a specified range of the direction of travel of the vehicle.

[0016] Invention Effects

[0017] Based on the above scheme, more appropriate autonomous driving can be achieved. Attached Figure Description

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

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

[0020] Figure 3 It is a diagram that schematically represents branching events.

[0021] Figure 4 It is a schematic diagram representing the branch points through events.

[0022] Figure 5 It is a diagram that schematically represents a convergence event.

[0023] Figure 6It is a diagram that schematically represents the events that occur at the meeting point.

[0024] Figure 7 This is a diagram schematically representing a lane reduction event.

[0025] Figure 8 This is a diagram schematically representing a takeover event.

[0026] Figure 9 It is a diagram that schematically represents the passing of a signal.

[0027] Figure 10 This is a diagram illustrating an example of the correspondence between driving modes, the control state of the vehicle M, and tasks.

[0028] Figure 11 This is a flowchart illustrating an example of a series of processes performed by the automatic driving control device 100 of the first embodiment.

[0029] Figure 12 This indicates that the number of events N is the upper limit. MAX The following are images of the scene.

[0030] Figure 13 This indicates that the number of events N exceeds the upper limit N. MAX A picture of the scene.

[0031] Figure 14 This is a flowchart illustrating an example of a series of processes performed by the automatic driving control device 100 in the second embodiment.

[0032] Explanation of reference numerals in the attached figures

[0033] 10 cameras;

[0034] 12. Radar equipment;

[0035] 14 LIDAR;

[0036] 16. Object recognition device;

[0037] 20 communication devices;

[0038] 30 HMI;

[0039] 40 Vehicle sensors;

[0040] 50. Navigation devices;

[0041] 51 GNSS receiver;

[0042] 52. Navigation HMI;

[0043] 53. Path Determination Department;

[0044] 54. First map information;

[0045] 60 MPU;

[0046] 61. Recommended lane decision-making department;

[0047] 62. Second map information;

[0048] 70 Driver monitoring cameras;

[0049] 82 Steering wheel;

[0050] 84 Steering and holding sensors;

[0051] 100 Automatic driving control device;

[0052] 120 First Control Unit;

[0053] 130 Identification Department;

[0054] 140 Action Plan Generation Department;

[0055] 150 Model Decision Department;

[0056] 160 Second Control Unit;

[0057] 162 Acquisition Department;

[0058] 164 Speed ​​Control Unit;

[0059] 166 Steering control unit;

[0060] 200 Driving force output device;

[0061] 210 Braking device;

[0062] 220 Steering mechanism. Detailed Implementation

[0063] Hereinafter, embodiments of the vehicle control device, vehicle control method, and storage medium of the present invention will be described with reference to the accompanying drawings.

[0064] <First Implementation>

[0065] [Overall Structure]

[0066] Figure 1 This is a structural diagram of a vehicle system 1 utilizing the vehicle control device of the first embodiment. The vehicle equipped with vehicle system 1 is, for example, a two-wheeled, three-wheeled, or four-wheeled vehicle, and its drive source is an internal combustion engine such as a diesel engine or a gasoline engine, an electric motor, or a combination thereof. The electric motor operates using electricity generated by a generator connected to the internal combustion engine, or electricity discharged from a secondary battery or fuel cell.

[0067] Vehicle system 1 includes, for example, a camera 10, a radar device 12, a LiDAR (Light Detection and Ranging) system 14, an object recognition device 16, a communication device 20, an HMI (Human Machine Interface) 30, vehicle sensors 40, a navigation device 50, an MPU (Map Positioning Unit) 60, a driver monitoring camera 70, driving controls 80, an automatic driving control device 100, a driving force output device 200, a braking device 210, and a steering device 220. These devices and equipment are interconnected via multiple communication lines such as CAN (Controller Area Network) communication lines, serial communication lines, and wireless communication networks. It should be noted that... Figure 1 The structure shown is merely an example; a part of the structure may be omitted, and other structures may be added. The automatic driving control device 100 is an example of a "vehicle control device".

[0068] Camera 10 is, for example, a digital camera utilizing a solid-state imaging element such as CCD (Charge Coupled Device) or CMOS (Complementary Metal-Oxide Semiconductor). Camera 10 is mounted anywhere on the vehicle equipped with vehicle system 1 (hereinafter referred to as vehicle M). When photographing the front, camera 10 is mounted on the upper part of the windshield or behind the rearview mirror inside the vehicle. Camera 10, for example, periodically and repeatedly photographs the perimeter of vehicle M. Camera 10 can also be a stereo camera.

[0069] Radar device 12 radiates millimeter-wave and other radio waves to the periphery of the vehicle M, and detects the radio waves reflected by objects (reflected waves) to at least detect the position (distance and orientation) of the objects. Radar device 12 can be installed at any location on the vehicle M. Radar device 12 can also detect the position and speed of objects using FM-CW (Frequency Modulated Continuous Wave) method.

[0070] The LIDAR14 illuminates the periphery of the vehicle M with light (or electromagnetic waves of a wavelength close to that of light) and measures the scattered light. Based on the time from the emission of light to the reception of light, the LIDAR14 detects the distance to the object. The illuminated light can be, for example, a pulsed laser. The LIDAR14 can be mounted at any location on the vehicle M.

[0071] The object recognition device 16 performs sensor fusion processing on some or all of the detection results from the camera 10, radar device 12, and LIDAR 14 to identify the object's position, type, speed, etc. The object recognition device 16 outputs the recognition results to the autonomous driving control device 100. Alternatively, the object recognition device 16 can directly output the detection results from the camera 10, radar device 12, and LIDAR 14 to the autonomous driving control device 100. The object recognition device 16 can also be omitted from the vehicle system 1.

[0072] The communication device 20 can communicate with other vehicles in the vicinity of the vehicle M, for example, using cellular networks, Wi-Fi networks, Bluetooth (registered trademark), DSRC (Dedicated Short Range Communication), etc., or communicate with various server devices via wireless base stations.

[0073] The HMI30 provides various information to passengers in vehicle M and accepts their input operations. The HMI30 includes various display devices, speakers, buzzers, touch panels, switches, buttons, etc.

[0074] The vehicle sensor 40 includes a vehicle speed sensor for detecting the speed of the vehicle M, an acceleration sensor for detecting acceleration, a gyroscope sensor for detecting angular velocity, and an orientation sensor for detecting the direction of the vehicle M. The gyroscope sensor may, for example, include a yaw rate sensor for detecting angular velocity about a vertical axis.

[0075] The navigation device 50 includes, for example, a GNSS (Global Navigation Satellite System) receiver 51, a navigation HMI 52, and a route determination unit 53. The navigation device 50 stores first map information 54 in a storage device such as an HDD (Hard Disk Drive) or flash memory.

[0076] The GNSS receiver 51 receives radio waves from multiple GNSS satellites (artificial satellites) and determines the position of the vehicle M based on the received radio wave signals. The GNSS receiver 51 outputs the determined position of the vehicle M to the path determination unit 53, or directly or indirectly to the automatic driving control unit 100 via the MPU 60. The position of the vehicle M can also be determined or supplemented by the INS (Inertial Navigation System) using the output of the vehicle sensors 40.

[0077] The navigation HMI52 includes a display device, speakers, a touch panel, buttons, etc. Part or all of the navigation HMI52 can also be integrated with the aforementioned HMI30.

[0078] The route determination unit 53, for example, refers to the first map information 54 to determine the route from the position of the vehicle M determined by the GNSS receiver 51 (or any input position) to the destination input by the passenger using the navigation HMI 52 (hereinafter referred to as the route on the map).

[0079] The first map information 54, for example, is information representing the shape of a road by indicating its route and the nodes connected by the route. The first map information 54 may also include road curvature, POI (Point of Interest) information, etc. The paths on the map are output to the MPU 60.

[0080] The navigation device 50 can also provide route guidance using the navigation HMI 52 based on the path on the map. The navigation device 50 can also be implemented, for example, through the functions of a terminal device such as a smartphone or tablet held by the passenger. The navigation device 50 can also send its current location and destination to the navigation server via the communication device 20, and obtain a path from the navigation server that corresponds to the path on the map.

[0081] The MPU 60 includes, for example, a lane recommendation unit 61, which stores second map information 62 in a storage device such as an HDD or flash memory. The lane recommendation unit 61 is implemented by executing a program (software) using a hardware processor such as a CPU (Central Processing Unit). Alternatively, the lane recommendation unit 61 can be implemented using hardware (including a circuitry) such as an LSI (Large Scale Integration), ASIC (Application Specific Integrated Circuit), FPGA (Field-Programmable Gate Array), or GPU (Graphics Processing Unit), or through a combination of software and hardware. The program can be pre-stored in the MPU 60's storage device (a storage device with a non-transitory storage medium), or stored in a removable storage medium such as a DVD or CD-ROM, and installed into the MPU 60's storage device by mounting the storage medium (non-transitory storage medium) to a drive device.

[0082] The lane recommendation unit 61 divides the path on the map provided by the navigation device 50 into multiple segments (for example, every 100 [m] in the direction of vehicle travel), refers to the second map information 62, and determines the recommended lane according to each segment. The lane recommendation unit 61 makes a decision on which lane to drive in from the left. When there are branching points in the path on the map, the lane recommendation unit 61 determines the recommended lane as one that allows the vehicle M to travel on a reasonable path to the branch destination.

[0083] The second map information 62 is map information with higher accuracy than the first map information 54. The second map information 62 may include, for example, information about the center of a lane or the boundaries of a lane. Additionally, the second map information 62 may include road information, traffic control information, residential information (address / postal code), facility information, telephone number information, and information about prohibited areas in Mode A or Mode B (described later). The second map information 62 can be updated in real time by communicating with other devices via the communication device 20.

[0084] The driver monitoring camera 70 is, for example, a digital camera utilizing a solid-state imaging element such as a CCD or CMOS. The driver monitoring camera 70 is mounted anywhere in the vehicle M, in a position and direction that allows it to capture the head of the passenger (hereinafter referred to as the driver) seated in the driver's seat from the front (facing the face). For example, the driver monitoring camera 70 is mounted above a display device located in the center of the dashboard of the vehicle M.

[0085] The driving control unit 80 includes, for example, a steering wheel 82, an accelerator pedal, a brake pedal, a gear lever, and other control components. A sensor is installed on the driving control unit 80 to detect the amount of operation or whether operation has occurred. The detection result of this sensor is output to the automatic driving control unit 100, or to some or all of the driving force output device 200, braking device 210, and steering device 220. The steering wheel 82 is an example of a "control component subject to steering operation performed by the driver." The steering wheel 82 does not necessarily have to be ring-shaped; it can also be an irregularly shaped steering wheel, a lever, a button, etc. A steering grip sensor 84 is installed on the steering wheel 82. The steering grip sensor 84, implemented by an electrostatic capacitance sensor or the like, outputs a signal to the automatic driving control unit 100 that detects whether the driver is holding the steering wheel 82 (meaning it is in contact with the steering wheel under applied force).

[0086] The automatic driving control device 100 includes, for example, a first control unit 120 and a second control unit 160. The first control unit 120 and the second control unit 160 are implemented, for example, by executing programs (software) using a hardware processor such as a CPU. Furthermore, some or all of these components can also be implemented using hardware (including circuitry) such as LSI, ASIC, FPGA, and GPU, or through the coordinated operation of software and hardware. The program can also be pre-stored in a storage device such as an HDD or flash memory (a storage device with a non-transitory storage medium) of the automatic driving control device 100, or stored in a removable storage medium such as a DVD or CD-ROM, and installed into the HDD or flash memory of the automatic driving control device 100 by assembling the storage medium (non-transitory storage medium) into the drive unit.

[0087] Figure 2 This is a functional structure diagram of the first control unit 120 and the second control unit 160. The first control unit 120 includes, for example, an identification unit 130, an action plan generation unit 140, and a mode determination unit 150. A control unit that combines the action plan generation unit 140 and the second control unit 160, or a control unit that combines the action plan generation unit 140, the mode determination unit 150, and the second control unit 160, is an example of a "driving control unit".

[0088] The first control unit 120, for example, implements AI (Artificial Intelligence)-based functions and functions based on pre-defined models in parallel. For instance, the function of "identifying intersections" can be implemented by simultaneously performing intersection identification based on deep learning and other methods, as well as identification based on pre-defined conditions (signals with pattern matching, road signs, etc.), scoring both, and comprehensively evaluating them. This ensures the reliability of autonomous driving.

[0089] The recognition unit 130 identifies the surrounding conditions or environment of the vehicle M. For example, the recognition unit 130 identifies objects existing around the vehicle M based on information input from the camera 10, radar device 12, and LIDAR 14 via the object recognition device 16. Objects identified by the recognition unit 130 include, for example, bicycles, motorcycles, four-wheeled motor vehicles, pedestrians, road signs, road markings, dividing lines, utility poles, guardrails, and fallen objects. In addition, the recognition unit 130 identifies the position, speed, acceleration, and other states of the objects. The position of the object is identified, for example, as its position on a relative coordinate system with a representative point of the vehicle M (such as the center of gravity or the center of the drive shaft) as the origin (i.e., its relative position relative to the vehicle M), and is used for control. The position of the object can also be represented by a representative point such as the object's center of gravity or corner, or by the area it represents. The "state" of the object can also include the object's acceleration, jerk, or "action state" (e.g., whether a lane change is in progress or whether a lane change is about to occur).

[0090] Furthermore, the identification unit 130 identifies, for example, the lane in which the vehicle M is traveling (hereinafter referred to as the lane) and adjacent lanes adjacent to the lane. For example, the identification unit 130 obtains second map information 62 from the MPU 60, compares the pattern of road dividing lines (e.g., the arrangement of solid and dashed lines) contained in the obtained second map information 62 with the pattern of road dividing lines around the vehicle M identified based on the image of the camera 10, thereby identifying the space between the dividing lines as the lane or an adjacent lane.

[0091] Not limited to road markings, the recognition unit 130 can also identify lanes such as the current lane or adjacent lanes by recognizing driving road boundaries (road boundaries) including road markings, shoulders, curbs, median strips, guardrails, etc. This recognition can also incorporate the position of the vehicle M obtained from the navigation device 50 and INS-based processing results. Furthermore, the recognition unit 130 can recognize temporary stop lines, obstacles, red lights, toll booths, and other road features.

[0092] Furthermore, when identifying the lane, the identification unit 130 identifies the relative position and attitude of the vehicle M relative to the lane. For example, the identification unit 130 may identify the deviation of the vehicle M's reference point from the center of the lane, and the angle formed by the vehicle M's direction of travel relative to the line connecting the coordinates of the center of the lane, as the relative position and attitude of the vehicle M relative to the lane. Alternatively, the identification unit 130 may also identify the position of the vehicle M's reference point relative to any side end (road dividing line or road boundary) of the lane as the relative position of the vehicle M relative to the lane.

[0093] The action plan generation unit 140 generates a future target trajectory for the vehicle M to travel automatically (independent of driver operation) under the driving conditions specified by the events described later, so that it travels in the recommended lane determined by the recommended lane determination unit 61 in principle, and can cope with the surrounding conditions of the vehicle M. The target trajectory includes, for example, a speed element. For example, the target trajectory is represented by a track consisting of locations (track points) that the vehicle M should reach arranged side by side. Track points are locations that the vehicle M should reach at predetermined travel distances (e.g., a few meters) along the route. In addition, the target speed and target acceleration are generated as part of the target trajectory at predetermined sampling times (e.g., a few tenths of a second). Alternatively, track points can also be positions that the vehicle M should reach at the sampling time at predetermined sampling times. In this case, the information of target speed and target acceleration is represented by the interval of track points.

[0094] In order to address the surrounding conditions of the vehicle M, the action plan generation unit 140 may also exceptionally generate a target track that causes the vehicle M to travel in a lane other than the recommended lane (e.g., a lane adjacent to the recommended lane). That is, the priority of lanes other than the recommended lane is relatively low compared to the priority of the recommended lane. For example, the recommended lane has the highest priority (priority 1), the other lanes adjacent to the recommended lane (hereinafter referred to as adjacent lanes) have the second highest priority (priority 2), and the other lanes adjacent to the adjacent lanes have the third highest priority (priority 3). In this way, the action plan generation unit 140 generates a target track in principle that causes the vehicle M to travel in the recommended lane with the highest priority, and exceptionally generates a target track that causes the vehicle M to travel in a lane with a lower priority than the recommended lane, depending on the surrounding conditions of the vehicle M.

[0095] When generating the target track, the action plan generation unit 140 determines the event of automatic driving (including partial driving assistance) on the path where the recommended lane has been determined. The automatic driving event refers to information that specifies the behavior that the vehicle M should take under automatic driving (partial driving assistance), that is, the state (or mode of driving) during driving.

[0096] Automated driving events include, for example, constant speed driving events, low-speed following events, lane changing events, and overtaking events. A constant speed driving event is an event that causes vehicle M to travel at a fixed speed within the same lane. A low-speed following event is an event that causes vehicle M to follow another vehicle (hereinafter referred to as the preceding vehicle) that is within a specified distance (e.g., within 100 m) ahead of vehicle M. "Following" can be, for example, maintaining a fixed relative distance (inter-vehicle distance) between vehicle M and the preceding vehicle, or maintaining a fixed relative distance while driving in the center of the lane. A lane changing event is an event that causes vehicle M to change lanes from its current lane to an adjacent lane. An overtaking event is an event that causes vehicle M to temporarily change lanes to an adjacent lane, overtake the preceding vehicle in the adjacent lane, and then change lanes back to its original lane.

[0097] In addition, events for autonomous driving include branching events, branch point passing events, merging events, merging point passing events, lane reduction events, takeover events, and signal passing events.

[0098] Figure 3 This is a schematic diagram representing a branching event. A branching event refers to an event at the branching point in which vehicle M changes lanes from the main road to the branching lane, provided that vehicle M is traveling on the main road and its destination exists on the extension of a branch road (hereinafter referred to as a branch lane).

[0099] Figure 4 This is a diagram schematically representing a branch point passing event. A branch point passing event refers to the event where, when vehicle M is traveling on the main road and its destination exists on an extension of the main road, vehicle M is guided at the branch point to continue traveling on the main road instead of branching off from it.

[0100] Figure 5 This is a schematic diagram illustrating a merging event. A merging event refers to an event in which vehicle M, while traveling on a side road merging with the main road (hereinafter referred to as the merging lane), and its destination is located on an extension of the main road, guides vehicle M to change lanes from the merging lane to the main road at the merging point.

[0101] Figure 6 This is a diagram schematically representing a merging point passing event. A merging point passing event refers to the event where, when vehicle M is traveling on the main road and its destination exists on an extension of the main road, vehicle M is guided to continue traveling on the main road at the merging point.

[0102] Figure 7This is a diagram schematically representing a lane reduction event. A lane reduction event refers to an event that, while traveling along a path where the number of lanes decreases midway, guides the vehicle M to either change lanes to another lane or continue traveling in the current lane.

[0103] Figure 8 This is a schematic diagram illustrating a takeover event. A takeover event refers to the event of ending the automatic driving mode (Mode A, described below) and switching to a driving assistance mode (Modes B, C, and D, described below) or a manual driving mode (Mode E, described below). For example, sometimes near a tollbooth on a highway, the lane markings are interrupted, making it impossible to identify the relative position of vehicle M. In such cases, the (planned) takeover event is determined for the section near the tollbooth.

[0104] Figure 9 This is a diagram schematically representing a traffic signal passage event. A traffic signal passage event refers to the event in which the vehicle M stops or starts according to the signal of the traffic signal.

[0105] The action plan generation unit 140 sequentially determines these multiple events along the path to the destination, while taking into account the surrounding conditions of the vehicle M, and generates a target track for the vehicle M to travel in the state specified by each event.

[0106] Return to Figure 2 The mode determination unit 150 determines the driving mode of the vehicle M as any one of multiple driving modes. In each of these multiple driving modes, the tasks assigned to the driver differ. The mode determination unit 150 includes, for example, a driver state determination unit 152 and a mode change processing unit 154. These individual functions will be described below.

[0107] Figure 10 This diagram illustrates an example of the correspondence between driving modes, the control state of the vehicle M, and tasks. The vehicle M has, for example, five driving modes: Mode A through Mode E. The control state, i.e., the degree of automation (control level) of the driving control of the vehicle M, is highest in Mode A, decreasing in the order of Mode B, Mode C, and Mode D, with Mode E being the lowest. Conversely, the tasks assigned to the driver are as follows: Mode A is the lightest, decreasing in the order of Mode B, Mode C, and Mode D, with Mode E being the most demanding. It should be noted that in Modes D and E, the control state is not automatic driving; therefore, the automatic driving control device 100 is responsible for ending the control related to automatic driving and transferring to driving assistance or manual driving. The following provides examples of the content of each driving mode.

[0108] In Mode A, the vehicle is in an automated driving state, and neither forward monitoring nor steering wheel 82 control (steering control in the diagram) is assigned to the driver. However, even in Mode A, the driver is required to be able to quickly switch to manual driving posture upon request from the system centered on the automated driving control unit 100. It should be noted that automated driving here means that steering, acceleration, and deceleration are controlled without relying on the driver's operation. Forward refers to the space in which the vehicle M is visually confirmed through the windshield in the direction of travel. Mode A is, for example, a driving mode that can be executed when the vehicle M is traveling at a specified speed (e.g., around 50 km / h) or less on a motor vehicle-only road such as a highway and there are following vehicles, and is sometimes called TJP (Traffic Jam Pilot). If the conditions are no longer met, the mode determination unit 150 changes the driving mode of the vehicle M to Mode B.

[0109] In Mode B, the system enters a driving support state, assigning the driver the task of monitoring the area ahead of the vehicle M (hereinafter referred to as forward monitoring), but not the task of holding the steering wheel 82. In Mode C, the system also enters a driving support state, assigning the driver both the task of forward monitoring and the task of holding the steering wheel 82. Mode D is a driving mode where at least one of the steering or acceleration / deceleration of the vehicle M requires some degree of driver intervention. For example, in Mode D, driving support functions such as ACC (Adaptive Cruise Control) or LKAS (Lane Keeping Assist System) are activated. In Mode E, the system enters a manual driving state where both steering and acceleration / deceleration require driver intervention. In both Modes D and E, the driver is also assigned the task of monitoring the area ahead of the vehicle M.

[0110] The automatic driving control unit 100 (and driving support unit (not shown)) performs lane changes corresponding to the driving mode. Lane changes include system-requested lane changes (1) and driver-requested lane changes (2). Lane change (1) includes lane changes for overtaking when the speed of the preceding vehicle is more than a certain threshold lower than the vehicle's speed, and lane changes for traveling towards the destination (lane changes achieved by changing the recommended lane). Lane change (2) occurs when the driver operates the direction indicator if conditions regarding speed or positional relationship with surrounding vehicles are met, causing the vehicle M to change lanes in the direction of operation.

[0111] In mode A, the automatic driving control device 100 neither performs lane change (1) nor lane change (2). In modes B and C, the automatic driving control device 100 performs both lane change (1) and lane change (2). In mode D, the driving support device (not shown) performs lane change (2) but not lane change (1). In mode E, neither lane change (1) nor lane change (2) is performed.

[0112] If the driver does not perform the task related to the determined driving mode, the mode determination unit 150 changes the driving mode of the vehicle M to a driving mode with a heavier task.

[0113] For example, in Mode A, if the driver is unable to switch to manual driving upon request from the system (e.g., continuously looking outside the permitted area, or detecting signs of driving difficulty), the mode determination unit 150 performs the following control: using HMI 30, prompting the driver to switch to manual driving; if the driver does not respond, the vehicle M is brought closer to the curb and gradually brought to a stop, discontinuing automatic driving. After discontinuing automatic driving, the vehicle enters Mode D or E, and can be started manually by the driver. The following "discontinuing automatic driving" is the same. In Mode B, if the driver is not monitoring the road ahead, the mode determination unit 150 performs the following control: using HMI 30, prompting the driver to monitor the road ahead; if the driver does not respond, the vehicle M is brought closer to the curb and gradually brought to a stop, discontinuing automatic driving. In Mode C, if the driver is not monitoring the road ahead or is not holding the steering wheel 82, the mode determination unit 150 performs the following control: using HMI 30, prompting the driver to monitor the road ahead and / or hold the steering wheel 82; if the driver does not respond, the vehicle M is brought closer to the curb and gradually brought to a stop, discontinuing automatic driving.

[0114] The driver state determination unit 152 monitors the driver's state to determine whether the driver's state is appropriate for the task in order to perform the aforementioned mode change. For example, the driver state determination unit 152 analyzes the images captured by the driver monitoring camera 70, performs posture estimation processing, and determines whether the driver is in a posture that prevents them from switching to manual driving upon request from the system. Additionally, the driver state determination unit 152 analyzes the images captured by the driver monitoring camera 70, performs gaze estimation processing, and determines whether the driver is monitoring the road ahead.

[0115] The mode change processing unit 154 performs various processes for mode changes. For example, the mode change processing unit 154 instructs the action plan generation unit 140 to generate a target track for shoulder stopping, or gives working instructions to the driver support device (not shown), or controls the HMI 30 to prompt the driver to take action.

[0116] The second control unit 160 controls the driving force output device 200, the braking device 210 and the steering device 220, so that the vehicle M passes through the target track generated by the action plan generation unit 140 at a predetermined time.

[0117] The second control unit 160 includes, for example, an acquisition unit 162, a speed control unit 164, and a steering control unit 166. The acquisition unit 162 acquires information about the target track (track point) generated by the action plan generation unit 140 and stores it in a memory (not shown). The speed control unit 164 controls the driving force output device 200 or the braking device 210 based on the speed elements of the target track stored in the memory. The steering control unit 166 controls the steering device 220 according to the curvature of the target track stored in the memory. The processing of the speed control unit 164 and the steering control unit 166 is achieved, for example, through a combination of feedforward control and feedback control. As an example, the steering control unit 166 combines feedforward control corresponding to the curvature of the road ahead of the vehicle M with feedback control based on deviation from the target track.

[0118] The driving force output device 200 outputs driving force (torque) to the drive wheels for vehicle propulsion. The driving force output device 200 includes, for example, a combination of an internal combustion engine, an electric motor, and a transmission, as well as an ECU (Electronic Control Unit) that controls them. The ECU controls the above structure according to information input from the second control unit 160 or from the driving operation device 80.

[0119] The braking device 210 includes, for example, a brake caliper, a cylinder that transmits hydraulic pressure to the brake caliper, an electric motor that generates hydraulic pressure in the cylinder, and a braking ECU. The braking ECU controls the electric motor according to information input from the second control unit 160 or from the driving control unit 80, outputting braking torque corresponding to the braking operation to each wheel. The braking device 210 may include, as a backup, a mechanism for transmitting hydraulic pressure generated by the operation of the brake pedal included in the driving control unit 80 via a master hydraulic cylinder to the cylinder. It should be noted that the braking device 210 is not limited to the structure described above, and may also be an electronically controlled hydraulic braking device that controls the actuator according to information input from the second control unit 160 and transmits hydraulic pressure from the master hydraulic cylinder to the cylinder.

[0120] The steering system 220 includes, for example, a steering ECU and an electric motor. The electric motor applies force to a rack and pinion mechanism to change the direction of the steering wheels. The steering ECU drives the electric motor to change the direction of the steering wheels according to information input from the second control unit 160 or from the driving control unit 80.

[0121] [Processing Flow]

[0122] The following uses flowcharts to illustrate the process of a series of processes performed by the automatic driving control device 100 of the first embodiment. Figure 11 This is a flowchart illustrating an example of a series of processes performed by the automatic driving control device 100 of the first embodiment. For example, the processes in this flowchart can be repeatedly executed at a predetermined cycle when certain execution conditions are met.

[0123] Condition (i): The automatic driving control unit 100 is able to obtain the second map information 62 from the MPU 60.

[0124] Condition (ii): This vehicle M is not driving in the prohibited area of ​​Mode A or Mode B.

[0125] Condition (iii): No anomalies were generated in the second map information 62.

[0126] First, the identification unit 130 identifies the surrounding conditions (or environment) of the vehicle M (step S100).

[0127] Next, the action plan generation unit 140 determines the events for autonomous driving along the path from the recommended lane determined by the MPU 60 to the destination (step S102). For example, the action plan generation unit 140 sequentially determines the events to be executed under autonomous driving along the path to the destination (in the direction of path extension). For example, on the path to the destination, there is a section A, a section B adjacent to section A, and a section C adjacent to section B. The sections do not need to be equally spaced; they can be unequally spaced. In such a case, the action plan generation unit 140 determines an event I to be executed in section A. A Determine an event I that should be executed in interval B. B Determine an event I that should be executed in interval C. C Therefore, in accordance with the movement of vehicle M along the path, according to I... A I B I C The events are executed sequentially. In addition, in order to respond flexibly to the surrounding situation of the vehicle M, the action plan generation unit 140 can dynamically change an event determined in each interval into another event of a different type, or dynamically divide it into multiple events.

[0128] Next, the action plan generation unit 140 generates the target trajectory according to each event (step S104).

[0129] Next, the mode change processing unit 154 monitors the number of events (hereinafter referred to as the event number N) determined (planned) in one or more events determined by the action plan generation unit 140 for the interval within a predetermined distance Dth from the current position of the vehicle M (the direction of travel of the vehicle M), and determines whether the event number N exceeds the upper limit N. MAX (Step S106). Maximum quantity N MAX For example, there could be around several dozen.

[0130] Figure 12 This indicates that the number of events N is the upper limit. MAX The following scene diagrams, Figure 13 This indicates that the number of events N exceeds the upper limit N. MAX A scene diagram. For example... Figure 12 Therefore, when vehicle M travels on a path with a simple road structure or traffic conditions, the number of events N tends to decrease (in the diagram, these are events I1 to I5, which are the 5 events). As a result, it is easy for the number N to reach the upper limit. MAX The following describes a simple road structure or traffic condition, such as a highway or other dedicated motor vehicle road. On the other hand, such as... Figure 13 Therefore, when vehicle M travels on a path with complex road structure or traffic conditions, the number of events N is likely to increase (represented by events I1 to I in the diagram). 15 These 15 (in this case) result in a high likelihood of exceeding the upper limit N. MAX Such road structures or routes with complex traffic conditions include, for example, intersections or urban roads with many narrow lanes.

[0131] return Figure 11 The flowchart is explained. Next, the mode change processing unit 154 handles events where the number N exceeds the upper limit N. MAX In this case, the driving mode of vehicle M is changed to a driving mode with a lower control level (step S108).

[0132] For example, if the driving mode of the vehicle M is mode A or mode B, the mode change processing unit 154 changes it to mode C or mode D, which has a lower control level than mode B. In other words, the mode change processing unit 154 changes it to mode C or mode D, which assigns a greater responsibility (task) to the passenger compared to mode B.

[0133] As described above, Modes A and B do not assign the responsibility of steering wheel 82 to the passenger. In contrast, Modes C and D assign the responsibility of steering wheel 82 to the passenger. Therefore, in autonomous driving or driver assistance, the number of events N exceeds the upper limit N. MAX In this case, the mode change processing unit 154 changes the driving mode of the vehicle M to a mode in which the responsibility of holding the steering wheel 82 is assigned to the passenger.

[0134] Furthermore, Mode E, being a manual driving mode, naturally assigns the responsibility of steering wheel 82 to the passenger. Therefore, in automatic driving or driver assistance mode, the number of events N exceeds the upper limit N. MAX In this case, the mode change processing unit 154 can also change from any autonomous driving or driving support mode to mode E.

[0135] On the other hand, when the number of events N is the upper limit N MAX In the following cases, the mode change processing unit 154 will not change the driving mode of the vehicle M and will maintain the current driving mode (step S110).

[0136] The action plan generation unit 140 switches between outputting a target trajectory to the second control unit 160 according to the driving mode changed or maintained by the mode change processing unit 154 (step S110). For example, if the current driving mode is mode A, B, or C and the number of events N is the upper limit N. MAX In the following situations, the current driving mode is maintained. In this situation, the action plan generation unit 140 outputs a target trajectory to the second control unit 160. In response, the second control unit 160 controls the acceleration, deceleration, and steering of the vehicle M based on the target trajectory. As a result, automatic driving or driving support is performed.

[0137] On the other hand, the current driving mode is mode A or B and the number of events N exceeds the upper limit N. MAX In such cases, the current driving mode will be changed to a lower control level mode, such as C, D, or E.

[0138] For example, when switching to Mode C, the action plan generation unit 140 outputs a target track to the second control unit 160, just as when maintaining Mode C. In response, the second control unit 160 controls the acceleration, deceleration, and steering of the vehicle M based on the target track. As a result, in Mode C, driving support based on the target track is performed.

[0139] When switching to Mode D, the action plan generation unit 140 outputs a target track to the second control unit 160. In this case, the second control unit 160 controls the driving force output device 200 and braking device 210, or the steering device 220, which are the controlled objects, based on the target track. That is, the second control unit 160 controls one of the acceleration, deceleration, or steering of the vehicle M.

[0140] When switching to Mode E, the action plan generation unit 140 does not output the target trajectory to the second control unit 160. In this case, the ECUs of the driving force output device 200, braking device 210, and steering device 220, which are controlled by the second control unit 160, control their respective devices according to the driver's operation of the driving operation unit 80. That is, the acceleration, deceleration, and steering of the vehicle M are controlled by manual driving. Thus, the processing of this flowchart ends.

[0141] According to the first embodiment described above, the navigation device 50 determines the path from the location of the vehicle M determined by the GNSS receiver 51 to the destination input by the passenger. The MPU 60 determines a recommended lane for the vehicle M to travel on the path determined by the navigation device 50 to the destination. The automatic driving control device 100 identifies the surrounding conditions of the vehicle M and, based on these conditions and the recommended lane determined by the MPU 60, determines events that determine the driving state of the vehicle M. The automatic driving control device 100 generates a target track corresponding to the determined events and controls the acceleration, deceleration, and steering of the vehicle M based on this target track. That is, the automatic driving control device 100 performs automatic driving (including driving assistance) based on the target track. At this time, the automatic driving control device 100 changes the control level of automatic driving according to the number of events. When the number of events N exceeds the upper limit N... MAX In such cases, it means that the control of autonomous driving becomes more complex or difficult. Therefore, the number of events N is used as an indicator of the complexity or difficulty of control in autonomous driving. The control level of autonomous driving is changed according to the number of events N, thereby enabling more appropriate autonomous driving.

[0142] <Second Implementation>

[0143] The second embodiment will now be described. The difference between the second embodiment and the first embodiment described above is that when the number of events N exceeds the upper limit N... MAX In this case, specific events are reduced from these N events. The following description focuses on the differences from the first embodiment, omitting descriptions of the commonalities. It should be noted that in the description of the second embodiment, the same reference numerals are used to describe the parts that are the same as in the first embodiment.

[0144] Figure 14 This is a flowchart illustrating an example of a series of processes performed by the automatic driving control device 100 in the second embodiment. For example, if several execution conditions described in the first embodiment are met, the processes in this flowchart can be repeatedly executed according to a predetermined cycle.

[0145] First, the identification unit 130 identifies the surrounding conditions (or environment) of the vehicle M (step S200).

[0146] Next, the action plan generation unit 140 determines the event of autonomous driving on the path leading to the destination, from the recommended lane determined by the MPU 60 (step S202).

[0147] Next, the action plan generation unit 140 generates the target trajectory according to each event (step S204).

[0148] Next, the mode change processing unit 154 monitors the number of the most recent events (N) determined by the action plan generation unit 140 for the interval within a predetermined distance Dth ahead of the current position of the vehicle M, and determines whether the number of events N exceeds the upper limit N. MAX (Step S206).

[0149] Next, the mode change processing unit 154 detects that the number of events N exceeds the upper limit N. MAX In the case of N events, events that are not essential until the vehicle M reaches its destination are reduced (step S208).

[0150] As described above, the N events can include various events such as constant speed driving events, low-speed following events, lane change events, overtaking events, branching events, branch point passing events, merging events, merging point passing events, lane reduction events, takeover events, and signal passing events. Among these events, there are events that are indispensable for vehicle M to reach its destination (hereinafter referred to as necessary events) and events that are not indispensable for vehicle M to reach its destination (hereinafter referred to as arbitrary events). Necessary events are an example of "first events," and arbitrary events are an example of "second events."

[0151] For example, events such as branching, passing through a branch point, merging, passing through a merging point, lane reduction, takeover, and passing through a traffic signal are essential events for vehicle M to reach its destination, and are therefore mandatory events. On the other hand, events such as constant speed driving, low-speed following, lane changing, and overtaking are not essential events for vehicle M to reach its destination, and are therefore optional events.

[0152] Next, the pattern change processing unit 154 determines whether the number of remaining necessary events after removing any event from the N events (hereinafter referred to as the number of events after deletion N′) exceeds the upper limit N. MAX (Step S210).

[0153] For example, in order to reach the destination as quickly as possible, more lane-changing events or overtaking events are decided, resulting in the number of events N exceeding the upper limit N. MAX In this case, by removing arbitrary events such as lane change events or overtaking events from the N events, we can expect the number of events N′ after deletion to become the upper bound N. MAX The following means that it can be expected to make control in autonomous driving simple or easy.

[0154] The pattern change processing unit 154 found that the number of events N′ after deletion exceeded the upper limit N. MAX In this case, the driving mode of vehicle M is changed to a driving mode with a lower control level (step S212).

[0155] On the other hand, the pattern change processing unit 154 sets the event number N or the event number N′ after deletion to the upper limit N. MAX In the following cases, the driving mode of vehicle M will not be changed, and the current driving mode will be maintained (step S214).

[0156] The action plan generation unit 140 switches whether or not to output the target track to the second control unit 160 according to the driving mode changed or maintained by the mode change processing unit 154 (step S216). Thus, the processing of this flowchart ends.

[0157] According to the second embodiment described above, the automatic driving control device 100 starts from a quantity exceeding the upper limit N. MAX From N events, any event is removed. Therefore, the number of remaining necessary events after removing the arbitrary event (i.e., the number of events N′ after deletion) easily becomes the upper limit N. MAX The result is that, even without lowering the control level, it is possible to simplify or facilitate control in autonomous driving. That is, it is possible to more appropriately change the control level of autonomous driving.

[0158] [Postscript]

[0159] The implementation methods described above can be performed as follows.

[0160] (Performance example 1)

[0161] A vehicle control device, configured as follows:

[0162] It has a memory for storing programs and a hardware processor.

[0163] The hardware processor executes the program to perform the following processing:

[0164] Identify the surroundings of the vehicle.

[0165] Based on the identified conditions, the path to the vehicle's destination, and the vehicle's location, an event is used to determine the vehicle's driving state.

[0166] Based on the events determined, autonomous driving is performed, controlling at least one of the vehicle's acceleration, deceleration, and steering.

[0167] The control level of the automated driving system is changed based on the number of events within a specified range of the vehicle's direction of travel.

[0168] (Performance example 2)

[0169] A vehicle control device, configured as follows:

[0170] It has a memory for storing programs and a hardware processor.

[0171] The hardware processor executes the program to perform the following processing:

[0172] Identify the surroundings of the vehicle.

[0173] Based on the identified conditions, the path to the vehicle's destination, and the vehicle's location, an event is used to determine the vehicle's driving state.

[0174] The vehicle's driving mode is determined to be any one of a plurality of driving modes, including a first driving mode (e.g., mode C, mode D, or mode E) and a second driving mode (e.g., mode A or mode B) that assigns a lighter task to the driver than the first driving mode.

[0175] Based on the events determined, autonomous driving is performed, controlling at least one of the vehicle's acceleration, deceleration, and steering.

[0176] If the task in the determined driving mode is not performed by the driver, the vehicle's driving mode will be changed to a more demanding driving mode.

[0177] If the number of events within a specified range of the vehicle's direction of travel exceeds the upper limit, the vehicle's driving mode is changed to a more demanding driving mode compared to when the number of events is below the upper limit.

[0178] The above describes specific embodiments of the present invention, but the present invention is not limited to such embodiments in any way, and various modifications and substitutions can be made without departing from the spirit of the present invention.

Claims

1. A vehicle control device, wherein the vehicle control device includes: an identifying section that identifies a situation of a periphery of a vehicle; a deciding section that decides an event that determines a state at a time of travel of the vehicle, based on the situation identified by the identifying section, a route to a destination of the vehicle, and a position of the vehicle; and a driving control section that performs automatic driving that controls at least one of acceleration and deceleration and steering of the vehicle, based on the event decided by the deciding section, the driving control section changes a control level of the automatic driving according to a number of the event within a prescribed range of a traveling direction of the vehicle, the driving control section lowers the control level of the automatic driving if the number of the event exceeds an upper limit number, compared to a case where the number of the event is equal to or less than the upper limit number, the event includes a first event that is indispensable until the vehicle reaches the destination.

2. The vehicle control device according to claim 1, wherein the event further includes a second event that is not indispensable until the vehicle reaches the destination, the driving control section lowers the control level of the automatic driving if a number of the first event remaining after the second event is removed exceeds the upper limit number.

3. A vehicle control method, wherein a computer mounted on a vehicle performs the following processes: identifying a situation of a periphery of the vehicle, deciding an event that determines a state at a time of travel of the vehicle, based on the situation identified, a route to a destination of the vehicle, and a position of the vehicle, performing automatic driving that controls at least one of acceleration and deceleration and steering of the vehicle, based on the event decided, changing a control level of the automatic driving according to a number of the event within a prescribed range of a traveling direction of the vehicle, lowering the control level of the automatic driving if the number of the event exceeds an upper limit number, compared to a case where the number of the event is equal to or less than the upper limit number, the event includes a first event that is indispensable until the vehicle reaches the destination.

4. A storage medium that stores a program, wherein the program is used to cause a computer mounted on a vehicle to perform the following processes: identifying a situation of a periphery of the vehicle, deciding an event that determines a state at a time of travel of the vehicle, based on the situation identified, a route to a destination of the vehicle, and a position of the vehicle, performing automatic driving that controls at least one of acceleration and deceleration and steering of the vehicle, based on the event decided, changing a control level of the automatic driving according to a number of the event within a prescribed range of a traveling direction of the vehicle, lowering the control level of the automatic driving if the number of the event exceeds an upper limit number, compared to a case where the number of the event is equal to or less than the upper limit number, the event includes a first event that is indispensable until the vehicle reaches the destination. ​

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