Vehicle control device, vehicle control method, and storage medium

Abstract

A vehicle control device, a vehicle control method, and a storage medium capable of changing a control level of automatic driving under appropriate conditions are provided. The vehicle control device includes a first measurement unit that measures a position of a vehicle based on a radio wave transmitted from an artificial satellite, a second measurement unit that measures the position of the vehicle based on an index indicating a behavior of the vehicle, a decision unit that decides a correction amount of the second position based on a difference between a first position of the vehicle measured by the first measurement unit and a second position of the vehicle measured by the second measurement unit, and a driving control unit that performs automatic driving of the vehicle based on the first position or the second position corrected based on the correction amount. The driving control unit reduces the control level of the automatic driving by a shorter travel distance or a shorter travel time in a condition where the first position is not measured by the first measurement unit, compared to a condition where the correction amount is decided by the decision unit.

Description

Technical Field

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

[0002] Previously, a navigation system was known, which was configured to calculate a first travel distance of the vehicle within a specified range based on the vehicle speed calculated using a vehicle speed pulse and a vehicle speed calculation coefficient, calculate a second travel distance of the vehicle within the specified range based on GPS (Global Positioning System) information provided by a positioning satellite, correct the vehicle speed calculation coefficient based on the comparison result of the first travel distance and the second travel distance, and predict the position of the vehicle based on the vehicle speed calculated using a vehicle speed pulse and the corrected vehicle speed calculation coefficient (for example, see Patent Document 1).

[0003] Prior art literature

[0004] Patent documents

[0005] Patent Document 1: Japanese Patent Application Publication No. 2011-117739 Summary of the Invention

[0006] The problem that the invention aims to solve

[0007] Previous technologies have not adequately studied the following situation: after modifying indicators representing the behavior of the vehicle, such as speed pulses, and using these modified indicators for autonomous driving, under what conditions the control level of autonomous driving is changed.

[0008] This invention was proposed 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 changing the control level of autonomous driving under appropriate conditions.

[0009] Solution for solving the problem

[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 comprising: a first measuring unit that measures the position of a vehicle based on radio waves transmitted from an artificial satellite; a second measuring unit that measures the position of the vehicle based on an index representing the behavior of the vehicle; a decision unit that calculates the difference between the position of the vehicle measured by the first measuring unit (i.e., a first position) and the position of the vehicle measured by the second measuring unit (i.e., a second position), and determines a correction amount for the second position based on the calculated difference; and a driving control unit that performs automatic driving of the vehicle based on the first position measured by the first measuring unit or based on the second position corrected based on the correction amount determined by the decision unit, wherein, when the correction amount is not determined by the decision unit, the driving control unit reduces the control level of the automatic driving based on a shorter driving distance or driving time compared to when the correction amount is determined by the decision unit, provided that the first position is not measured by the first measuring unit.

[0012] (2) The second aspect of the present invention is a vehicle control device. Based on the first aspect, the first measuring unit repeatedly measures the first position, and the second measuring unit repeatedly measures the second position. Each time the first position and the second position are repeatedly measured, the decision unit repeatedly performs the following two processes: first, it calculates the difference between the first position and the second position after being corrected based on the correction amount; second, it determines the correction amount based on the calculated difference. When the first position is not measured by the first measuring unit, the driving control unit performs the automatic driving based on the second position after being corrected based on the last correction amount determined among the multiple correction amounts repeatedly determined by the decision unit or the correction amount with the smallest difference.

[0013] (3) The third aspect of the present invention is a vehicle control device, which, based on the second aspect, increases the driving control unit by the number of times the determination of the correction amount is repeated until the driving distance or driving time is reduced due to the control level of the automatic driving.

[0014] (4) The fourth aspect of the present invention is a vehicle control device, which, based on any of the first to third aspects, further reduces the control level of the automatic driving system when the angle at which the vehicle turns in the same direction exceeds a specified angle.

[0015] (5) The fifth aspect of the present invention is a vehicle control method, wherein a computer mounted on a vehicle performs the following processing: measuring the position of the vehicle based on radio waves transmitted from an artificial satellite; measuring the position of the vehicle based on an index representing the behavior of the vehicle; calculating the difference between the position of the vehicle measured based on the radio waves, i.e., a first position, and the position of the vehicle measured based on the index, i.e., a second position, and determining a correction amount for the second position based on the calculated difference; performing automatic driving of the vehicle based on the first position or based on the second position corrected based on the correction amount; and, in the absence of determining the correction amount, reducing the control level of the automatic driving by a shorter driving distance or driving time compared to the case where the correction amount is determined, without measuring the first position.

[0016] (6) The sixth 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: measuring the position of the vehicle based on radio waves transmitted from an artificial satellite; measuring the position of the vehicle based on an index representing the behavior of the vehicle; calculating the difference between the position of the vehicle measured based on the radio waves, i.e., a first position, and the position of the vehicle measured based on the index, i.e., a second position, and determining a correction amount for the second position based on the calculated difference; performing autonomous driving of the vehicle based on the first position or based on the second position corrected based on the correction amount; and, in the absence of determining the correction amount, reducing the control level of the autonomous driving by a shorter driving distance or driving time compared to the case where the correction amount is determined, without measuring the first position.

[0017] [Invention Effects]

[0018] According to the above scheme, the control level of autonomous driving can be changed under appropriate conditions. Attached Figure Description

[0019] Figure 1 This is a structural diagram of a vehicle system utilizing a vehicle control device according to an implementation method.

[0020] Figure 2 This is a functional structure diagram of the first control unit and the second control unit.

[0021] Figure 3 This is a diagram illustrating an example of the correspondence between driving modes, the vehicle's control status, and tasks.

[0022] Figure 4 This is a flowchart illustrating an example of training processing performed by vehicle system 1.

[0023] Figure 5This is a diagram used to illustrate the convergence determination of the correction amount.

[0024] Figure 6 This is a flowchart illustrating an example of runtime handling when an exception occurs due to vehicle system 1.

[0025] Figure 7 This diagram illustrates an example of a scenario where the turning angle θ of vehicle M exceeds the specified angle. Detailed Implementation

[0026] 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.

[0027] [Overall Structure]

[0028] Figure 1 This is a structural diagram of vehicle system 1 utilizing the vehicle control device of the 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.

[0029] Vehicle system 1 includes, for example, a camera 10, a radar device 12, a LiDAR (Light Detection and Ranging) device 14, an object recognition device 16, a communication device 20, a Human Machine Interface (HMI) 30, vehicle sensors 40, an inertial navigation unit (INU) 45, a navigation device 50, a Map Positioning Unit (MPU) 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 CAN (Controller Area Network) communication lines, serial communication lines, wireless communication networks, etc. It should be noted that... Figure 1 The structure shown is merely an example; a part of the structure may be omitted, or other structures may be added. Vehicle system 1 is an example of a "vehicle control device".

[0030] Camera 10 is, for example, a digital camera utilizing a solid-state imaging element such as a 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 the vehicle M). When photographing the front, camera 10 is mounted on the upper part of the windshield, behind the rearview mirror inside the vehicle, etc. Camera 10, for example, periodically and repeatedly photographs the perimeter of the vehicle M. Camera 10 can also be a stereo camera.

[0031] Radar device 12 radiates millimeter-wave or other radio waves around the vehicle M and detects the radio waves reflected by objects (reflected waves) to detect at least 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) mode.

[0032] The LIDAR 14 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. The LIDAR 14 detects the distance to an object based on the time from the emission of light to the reception of light. The illuminating light can be, for example, a pulsed laser. The LIDAR 14 can be mounted at any location on the vehicle M.

[0033] The object recognition device 16 performs sensor fusion processing on the detection results from some or all of the cameras 10, radar device 12, and LIDAR 14 to identify the position, type, speed, etc. of objects. 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 cameras 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.

[0034] The communication device 20 communicates 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 communicates with various server devices via wireless base stations.

[0035] The HMI30 provides various information to the occupants of vehicle M and accepts input operations performed by the occupants. The HMI30 includes various display devices, speakers, buzzers, touch panels, switches, buttons, etc.

[0036] 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 orientation of the vehicle M. The gyroscope sensor may include, for example, a yaw rate sensor for detecting angular velocity about a plumb axis.

[0037] In addition to the various sensors mentioned above, the vehicle sensor 40 also includes a wheel speed sensor 42. The wheel speed sensor 42 detects the rotational speed (rotational speed) of the wheels of the vehicle M and generates a pulse signal corresponding to the detected rotational speed (rotational speed). The wheel speed sensor 42 outputs the generated pulse signal to the automatic driving control device 100. The wheel rotational speed (rotational speed) detected by the wheel speed sensor 42 is an example of an "indicator representing behavior".

[0038] The inertial navigation device 45 measures or calculates the position of the vehicle M based on the inertial force acting on it. For example, the inertial navigation device 45 can calculate the position of the vehicle M by integrating the velocity detected by the gyroscope sensor included in the vehicle sensor 40 over time, or it can calculate the velocity by integrating the acceleration detected by the accelerometer sensor over time, and then calculate the position of the vehicle M by integrating the velocity over time. The inertial navigation device 45 outputs a signal indicating the measured or calculated position of the vehicle M to the automatic driving control device 100. The angular velocity detected by the gyroscope sensor and the acceleration detected by the accelerometer sensor are another example of "indicators representing behavior". The inertial navigation device 45 is an example of a "second measurement unit".

[0039] The navigation device 50 includes, for example, a GNSS (Global Navigation Satellite System) receiver 51, a navigation HMI 52, and a path determination unit 53. The navigation device 50 stores the first map information 54 in a storage device such as an HDD (Hard Disk Drive) or flash memory.

[0040] The GNSS receiver 51 receives radio waves from multiple GNSS satellites (artificial satellites) and measures or determines the position of the vehicle M based on the signals of the received radio waves. The GNSS receiver 51 outputs the measured or determined position of the vehicle M to the path determination unit 53, or directly to the automatic driving control device 100, or indirectly to the automatic driving control device 100 via the MPU 60.

[0041] The GNSS receiver 51 will also output a flag signal indicating the reception status of the GNSS satellite's radio waves (the strength of the received signal, whether or not it is received) directly to the autopilot control device 100 or indirectly to the autopilot control device 100 via the MPU 60.

[0042] The marking signals include position marking signals and non-position marking signals. Position marking signals indicate that the GNSS receiver 51 can receive radio waves from the GNSS satellite, or that the signal strength of the radio waves received by the GNSS receiver 51 from the GNSS satellite is above a threshold. Non-position marking signals indicate that the GNSS receiver 51 cannot receive radio waves from the GNSS satellite, or that the signal strength of the radio waves received by the GNSS receiver 51 from the GNSS satellite is below a threshold. The GNSS receiver 51 is an example of a "first measurement unit".

[0043] The navigation HMI52 includes a display device, speakers, a touch panel, buttons, etc. The navigation HMI52 can also share some or all of the aforementioned HMI30.

[0044] The route determination unit 53, for example, refers to the first map information 54 to determine the route (hereinafter referred to as the map route) from the position of the vehicle M measured or determined by the GNSS receiver 51 (or any input position) to the destination input by the occupant using the navigation HMI 52.

[0045] Furthermore, the path determination unit 53 determines the path on the map not only based on the position of the vehicle M measured or determined by the GNSS receiver 51, but also based on the position of the vehicle M measured or calculated by the inertial navigation device 45 and the position of the vehicle M estimated by the position estimation unit 156 described later.

[0046] The first map information 54 represents road shape information, for example, by indicating road lines and nodes connected by those lines. The first map information 54 may also include road curvature, POI (Point of Interest) information, etc. Paths on the map are output to the MPU60.

[0047] 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 using the functions of a terminal device such as a smartphone or tablet held by the occupant. The navigation device 50 can also send its current location and destination to the navigation server via the communication device 20 and obtain the path equivalent to the path on the map from the navigation server.

[0048] The MPU60 includes, for example, a lane recommendation unit 61, and stores the 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 MPU60'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 in the MPU60's storage device by mounting the storage medium (a non-transitory storage medium) to a drive unit.

[0049] The lane recommendation unit 61 divides the path on the map provided by the navigation device 50 into multiple segments (for example, divided into segments of 100 [m] in the direction of vehicle travel) and determines the recommended lane by segment with reference to the second map information 62. 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 so that the vehicle M can travel on a reasonable path for traveling to the branch destination.

[0050] The second map information 62 is map information with higher precision 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 also include road information, traffic restriction 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.

[0051] 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, capable of capturing the head of the occupant (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.

[0052] 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 that receives steering operations 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 capacitive sensor or the like, outputs a signal that detects whether the driver is gripping the steering wheel 82 (meaning contacting it under applied force) to the automatic driving control unit 100.

[0053] 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 the circuitry) such as LSI, ASIC, FPGA, and GPU, or through the coordinated operation of software and hardware. The program can 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 it can be stored in a removable storage medium such as a DVD or CD-ROM, and installed in the HDD or flash memory of the automatic driving control device 100 by assembling the storage medium (non-transitory storage medium) into the drive unit.

[0054] 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 pattern determination unit 150. The action plan generation unit 140 and the second control unit 160 together, or the action plan generation unit 140, the pattern determination unit 150, and the second control unit 160 together, constitute an example of a "driving control unit".

[0055] The first control unit 120, for example, implements functions based on AI (Artificial Intelligence) and functions based on pre-provided models in parallel. For example, the function of "identifying intersections" is achieved by executing intersection identification based on deep learning and other methods in parallel, and identification based on pre-provided conditions (the existence of signals, road signs, etc. that can be pattern matched), and then comprehensively evaluating both by adding scores to both. This ensures the reliability of autonomous driving.

[0056] 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 (center of gravity, drive shaft center, etc.) 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 representative points such as the object's center of gravity or corners, or by the area it represents. The "state" of the object can include the object's acceleration, jerk, or "action state" (e.g., whether it is changing lanes or about to change lanes).

[0057] Furthermore, the identification unit 130 identifies, for example, the lane in which the vehicle M is currently 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 and 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 from the image of the camera 10, thereby identifying the space between the dividing lines as the lane and adjacent lanes.

[0058] The recognition unit 130 is not limited to recognizing road markings; it can also identify lanes such as the current lane and adjacent lanes by recognizing driving road boundaries (road boundaries) including road markings, shoulders, curbs, median strips, and guardrails. In this recognition, the position of the vehicle M obtained from the navigation device 50 and the processing results from the inertial navigation device 45 can also be incorporated. Furthermore, the recognition unit 130 can also recognize temporary stop lines, obstacles, red lights, toll booths, and other road features.

[0059] 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 reference point of the vehicle M from the center of the lane, and the angle formed by the direction of travel of the vehicle M relative to the line connecting the coordinate points 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 identify the position of the reference point of the vehicle M relative to any end of the lane (road dividing line or road boundary), etc., as the relative position of the vehicle M relative to the lane.

[0060] The action plan generation unit 140 generates a target trajectory for the future travel of vehicle M automatically (without driver intervention) so that it can, in principle, travel on the recommended lane determined by the recommended lane determination unit 61 and cope with the surrounding conditions of vehicle M. The target trajectory includes, for example, speed elements. For instance, the target trajectory is represented by a track of locations (track points) that vehicle M should reach sequentially. Track points are locations that vehicle M should reach at predetermined travel distances (e.g., a few meters), whereas target speeds and target accelerations are generated as part of the target trajectory at predetermined sampling times (e.g., a few tenths of a second). Alternatively, track points can be positions that vehicle M should reach at predetermined sampling times. In this case, the target speed and target acceleration information are represented by the intervals between track points.

[0061] The action plan generation unit 140 can set events for automatic driving when generating a target track. These events include constant speed driving events, low-speed following events, lane change events, branching events, merging events, and takeover events. The action plan generation unit 140 generates a target track corresponding to the initiation events.

[0062] The mode determination unit 150 determines the driving mode of the vehicle M as any one of several driving modes that differ from the driving modes assigned to the driver. The mode determination unit 150 includes, for example, a driver state determination unit 152, a mode change processing unit 154, a position estimation unit 156, and a correction amount determination unit 158. Their respective functions will be described later.

[0063] Figure 3 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: lightest in Mode A, increasing in the order of Mode B, Mode C, and Mode D, with Mode E being the heaviest. It should be noted that Modes D and E are not automatic driving control states; therefore, the responsibility of the automatic driving control device 100 is to terminate control related to automatic driving until switching to driving assistance or manual driving. The contents of each driving mode are illustrated below.

[0064] In Mode A, the vehicle is in an automated driving state, and neither forward monitoring nor steering wheel control (as shown 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 according to the requirements of the system centered on the automated driving control unit 100. It should be noted that automated driving here refers to a driving mode in which steering, acceleration, and deceleration are controlled without relying on the driver's operation. Forward refers to the space in which the vehicle M's direction of travel is visually confirmed through the windshield. 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 dedicated motor vehicle road such as a highway and there is a vehicle ahead that is following it; it is sometimes called TJP (Traffic JamPilot). If this condition is no longer met, the mode determination unit 150 changes the driving mode of the vehicle M to Mode B.

[0065] 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 controlling the steering wheel 82. In Mode C, the system enters a driving support state, assigning the driver both the task of forward monitoring and the task of controlling the steering wheel 82. Mode D is a driving mode where the driver needs to perform some degree of driving operation for at least one of the steering or acceleration / deceleration of the vehicle M. For example, in Mode D, driving support functions such as ACC (Adaptive Cruise Control) and LKAS (Lane Keeping Assist System) are used. In Mode E, the system enters a manual driving state where both steering and acceleration / deceleration require driver operation. In both Modes D and E, the driver is also assigned the task of monitoring the area ahead of the vehicle M.

[0066] The automatic driving control unit 100 (and driving support unit (not shown)) performs automatic lane changes corresponding to the driving mode. Automatic lane changes include automatic lane changes based on system requirements (1) and automatic lane changes based on driver requirements (2). Automatic lane changes (1) include automatic lane changes for overtaking when the speed of the preceding vehicle is more than a certain threshold lower than the vehicle's speed, and automatic lane changes for heading towards the destination (automatic lane changes achieved by changing the recommended lane). Automatic lane changes (2) occur when the driver operates the direction indicator, provided that conditions related to speed and positional relationship with surrounding vehicles are met, causing the vehicle M to change lanes in the direction of operation.

[0067] In mode A, the automatic driving control device 100 does not perform automatic lane change (1) and automatic lane change (2). In modes B and C, the automatic driving control device 100 performs both automatic lane change (1) and automatic lane change (2). In mode D, the driving support device (not shown) performs automatic lane change (2) instead of automatic lane change (1). In mode E, neither automatic lane change (1) nor automatic lane change (2) is performed.

[0068] 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.

[0069] For example, in Mode A, if the driver is unable to switch to manual driving as requested by the system (e.g., continuously looking out of the permitted area, or detecting signs of driving difficulty), the mode determination unit 150 performs the following control: using the HMI 30 to urge 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, thus discontinuing automatic driving. After discontinuing automatic driving, the vehicle enters Mode D or Mode E, and the vehicle M 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 the HMI 30 to urge 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, thus 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 to urge 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 slowly stopped to discontinue automatic driving.

[0070] 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 to perform posture estimation processing and determine whether the driver is in a posture that prevents them from switching to manual driving as required by the system. In addition, the driver state determination unit 152 analyzes the images captured by the driver monitoring camera 70 to perform gaze estimation processing and determine whether the driver is monitoring the road ahead.

[0071] 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 work instructions to the driving support device (not shown), or controls the HMI 30 to urge the driver to take action.

[0072] The position estimation unit 156 estimates the position of the vehicle M based on the pulse signals output by the wheel speed sensor 42. For example, the position estimation unit 156 counts the pulse signals output from the wheel speed sensor 42 and converts the number of pulse signals, i.e., the rotational speed (rotational speed) of the wheel, into the distance traveled by the vehicle M. Then, the position estimation unit 156 estimates the current position of the vehicle M as the position that has advanced an amount corresponding to the travel distance from the point where the pulse signal counting began. The wheel speed sensor 42 and the position estimation unit 156 together constitute another example of a "second measurement unit".

[0073] The correction determination unit 158 ​​determines the correction amount for the vehicle speed observation position P2 based on the position of the vehicle M measured by the GNSS receiver 51 (hereinafter referred to as "satellite observation position P1") and the position of the vehicle M estimated by the position estimation unit 156 (hereinafter referred to as "vehicle speed observation position P2"). The satellite observation position P1 is an example of a "first position", and the vehicle speed observation position P2 is an example of a "second position".

[0074] Correction quantities are values ​​obtained by performing addition, subtraction, multiplication, or division operations on the vehicle speed observation position P2 to make it closer to the satellite observation position P1, which is also an observation. For example, when the vehicle speed observation position P2 is set as the explanatory variable, the correction quantity is the weighting coefficient α used to multiply this explanatory variable. The weighting coefficient α is also the ratio of the satellite observation position P1 to the vehicle speed observation position P2, i.e., the gain. In addition to the weighting coefficient α, correction quantities can also include bias components such as β used to add to the explanatory variable. Furthermore, correction quantities can also include the exponent of an exponential function, the base of a logarithmic function, and various parameters of machine learning (such as the weighting coefficients and bias components of a neural network).

[0075] For example, the correction determination unit 158 ​​calculates the difference Δ between the satellite observation position P1 and the vehicle speed observation position P2 in order to make the vehicle speed observation position P2 closer to the satellite observation position P1, and determines the correction amount of the vehicle speed observation position P2 in a way that makes the difference Δ smaller.

[0076] It should be noted that the vehicle speed observation position P2 is not limited to the position of the vehicle M estimated by the position estimation unit 156, but can also be the position of the vehicle M measured or calculated by the inertial navigation device 45, or the average of the two positions mentioned above.

[0077] 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.

[0078] return Figure 2 The second control unit 160 includes, for example, an acquisition unit 162, a speed control unit 164, and a steering control unit 166. The acquisition unit 162 acquires information 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 associated with 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.

[0079] The driving force output device 200 outputs driving force (torque) to the drive wheels to propel the vehicle. The driving force output device 200 may include, 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 aforementioned structure according to information input from the second control unit 160 or from the driving operation unit 80.

[0080] The braking device 210 includes, for example, a brake caliper, a hydraulic cylinder that transmits hydraulic pressure to the brake caliper, an electric motor that generates hydraulic pressure in the hydraulic cylinder, and a braking ECU. The braking ECU controls the electric motor according to information input from the second control unit 160 or from the driving operation unit 80, and outputs braking torque corresponding to the braking operation to each wheel. The braking device 210 may also include a mechanism for transmitting hydraulic pressure generated by the operation of the brake pedal included in the driving operation unit 80 to the hydraulic cylinder via the master hydraulic cylinder as a backup. 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 hydraulic cylinder.

[0081] 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 according to information input from the second control unit 160 or from the driving control unit 80, thereby changing the direction of the steering wheels.

[0082] [Training Processing]

[0083] The following flowchart illustrates the training process performed by vehicle system 1. The training process refers to the process of learning correction values ​​in advance; more specifically, it refers to the process of repeatedly determining correction values ​​and uniquely identifying their values. Figure 4 This is a flowchart illustrating an example of the training process performed by vehicle system 1. The processing in this flowchart is repeated at a predetermined period during the period when GNSS receiver 51 receives radio waves from GNSS satellites.

[0084] First, the GNSS receiver 51 receives radio waves from the GNSS satellite and measures the position of the vehicle M based on the signal of the received radio waves (step S100).

[0085] Next, the position estimation unit 156 estimates the position of the vehicle M based on the pulse signal output by the wheel speed sensor 42 (step S102).

[0086] Next, the correction amount determination unit 158 ​​determines whether the correction amount for the position of the vehicle M, i.e., the vehicle speed observation position P2, estimated by the position estimation unit 156, has been determined (step S104). For example, if the correction amount is stored in the storage device (HDD, flash memory, etc.) of the automatic driving control device 100, the correction amount determination unit 158 ​​determines that the correction amount for the vehicle speed observation position P2 has been determined.

[0087] The correction amount determination unit 158, having already determined the correction amount for the vehicle speed observation position P2, corrects the current vehicle speed observation position P2 estimated in step S102 based on the nearest correction amount among the correction amounts stored in the storage device of the automatic driving control device 100 (step S106).

[0088] For example, when the correction amount includes a weighting coefficient α and a deviation component β, the correction amount determination unit 158 ​​multiplies the vehicle speed observation position P2 by the weighting coefficient α and adds the deviation component β, thereby correcting the current vehicle speed observation position P2.

[0089] If the correction amount determination unit 158 ​​has not yet determined the correction amount for the vehicle speed observation position P2, it will not correct the current vehicle speed observation position P2 (that is, it will omit the processing in step S106) and will proceed to the next step S108.

[0090] Next, the correction determination unit 158 ​​calculates the difference Δ between the position of the vehicle M measured by the GNSS receiver 51, i.e., the satellite observation position P1, and the corrected / uncorrected vehicle speed observation position P2 (step S108).

[0091] For example, if the correction amount determination unit 158 ​​omits the processing of step S106, it calculates the difference Δ between the satellite observation position P1 and the uncorrected vehicle speed observation position P2. If the correction amount determination unit 158 ​​performs the processing of step S106, it calculates the difference Δ between the satellite observation position P1 and the corrected vehicle speed observation position P2.

[0092] Next, the correction determination unit 158 ​​determines the correction amount of the vehicle speed observation position P2 in a way that reduces the calculated difference Δ (step S110).

[0093] Next, the correction amount determination unit 158 ​​stores the determined correction amount of the vehicle speed observation position P2 in the storage device of the automatic driving control device 100 (step S112).

[0094] Next, the correction determination unit 158 ​​determines whether the correction amount of the determined vehicle speed observation position P2 has converged (step S114).

[0095] For example, the correction amount determination unit 158 ​​compares the correction amount determined and stored in the storage device up to the previous time with the correction amount determined in the current processing. If the error of these correction amounts is within the allowable range, it is determined that the correction amounts have converged. The allowable range is the numerical range within which two correction amounts being compared are allowed to be considered to have the same level of error.

[0096] On the other hand, if the error of the correction amount is outside the allowable range, the correction amount determination unit 158 ​​determines that the correction amount has not converged.

[0097] If the correction amount determination unit 158 ​​determines that the correction amount has converged, the processing of this flowchart will end.

[0098] On the other hand, if the correction amount determination unit 158 ​​determines that the correction amount has not converged, the process returns to step S100. Thus, the satellite observation position P1 and the vehicle speed observation position P2 are repeatedly calculated, and the correction amount is repeatedly determined until the correction amount of the vehicle speed observation position P2 converges to a fixed value.

[0099] For example, let the current processing be n (n is any natural number), and the previous processing be n-1. In this case, the correction amount determination unit 158 ​​corrects the nth vehicle speed observation position P2 based on the correction amount determined in the (n-1)th time. The correction amount determination unit 158 ​​calculates the difference Δ between the corrected nth vehicle speed observation position P2 and the nth satellite observation position P1, and determines the correction amount for the nth vehicle speed observation position P2 in a way that reduces this difference Δ, that is, in a way that makes the corrected nth vehicle speed observation position P2 closer to the nth satellite observation position P1. In this way, the correction amount determination unit 158 ​​repeatedly determines the current correction amount while reflecting the previous correction amount.

[0100] Figure 5 This is a diagram used to illustrate the convergence determination of the correction amount. Here, the correction amount is set to be only the weighting coefficient α. For example, the number of iterations (number of repetitions) that determines the correction amount is set to increase 1, 2, 3, ..., n-1, n times. The correction amount determination unit 158 ​​can determine the weighting coefficient α of the correction amount in the nth iteration when the number of iterations is n. n The weighting coefficient α, which is determined as the correction amount in the (n-1)th iteration. n-1 The error is determined to be within or outside the allowable range, and the correction amount is determined to converge based on the result of the determination.

[0101] Furthermore, the correction amount determination unit 158 ​​can also determine that the correction amount has converged when the moving average, which considers not only the correction amount determined recently but also the correction amounts of the past predetermined number of times, remains unchanged. For example, when considering the correction amounts of the past three times, the correction amount determination unit 158 ​​can determine the convergence based on the weighting coefficient α. n-3 α n-2 α n-1 and α n The moving average is used to determine whether the correction amount has converged.

[0102] [Runtime handling of exceptions]

[0103] The following flowchart illustrates the runtime processing of vehicle system 1 in the event of an anomaly. Runtime processing in the event of an anomaly refers to the use of pre-determined corrections during training to handle the autonomous driving of vehicle M when a specific anomaly occurs (including when its probability is high). A specific anomaly, for example, is when GNSS receiver 51 is unable to measure the position of vehicle M. Figure 6 This is a flowchart illustrating an example of runtime handling when an exception occurs due to vehicle system 1.

[0104] First, the action plan generation unit 140 goes into standby mode until the execution conditions are met (step S200). Execution conditions refer to the conditions used to execute the runtime processing of this flowchart, including various conditions such as those listed below.

[0105] Condition (i): A specific anomaly is occurring (GNSS receiver 51 is unable to measure the position of vehicle M).

[0106] Condition (ii): The automatic driving control unit 100 is able to obtain the second map information 62 from the MPU 60.

[0107] Condition (iii): This vehicle M is not driving in the prohibited area of ​​Mode A or Mode B.

[0108] Condition (iv): No anomalies were generated in the second map information 62.

[0109] For example, if the action plan generation unit 140 does not return to a position marker signal within a specified period (e.g., a few seconds) after the marker signal output from the GNSS receiver 51 changes from a self-positioning marker signal to a non-positioning marker signal, it determines that condition (i) is met.

[0110] Alternatively, or on this basis, if the action plan generation unit 140 does not return to a position marker signal during the period from when the marker signal output from the GNSS receiver 51 changes from a self-positioning marker signal to a non-positioning marker signal until the vehicle M has traveled a specified distance (e.g., several hundred meters), it determines that condition (i) is met.

[0111] Thus, if the action plan generation unit 140 determines that condition (i) is met when the period during which the GNSS receiver 51 is unable to receive radio waves from the GNSS satellite (or the period during which the signal strength of the radio waves is less than a threshold) continues throughout the entire specified time (or specified distance). That is, the action plan generation unit 140 determines that a specific anomaly has occurred (the GNSS receiver 51 is unable to measure the position of the vehicle M).

[0112] For example, specific anomalies may occur when the vehicle M is traveling in locations where GNSS satellite radio waves are easily blocked or reflected, such as tunnels, under overpasses, or among high-rise buildings. Furthermore, specific anomalies may also occur when the GNSS receiver 51 experiences a hardware or software malfunction, when the vehicle M is traveling in a location where other radio waves in the same frequency band as GNSS satellites are being transmitted, or when a GNSS satellite (e.g., a quasi-zenith satellite) is malfunctioning.

[0113] Therefore, under the various situations described above, the action plan generation unit 140 can easily determine that condition (i) is met, that is, it can easily determine that a specific anomaly has occurred.

[0114] If at least one of the execution conditions (i) to (iv) is satisfied (preferably all of (i) to (iv) are satisfied), the action plan generation unit 140 determines whether the training of the correction amount of the vehicle speed observation position P2 is completed (step S202).

[0115] For example, if the correction value of the vehicle speed observation position P2 converges at the training processing time point, the action plan generation unit 140 determines that the training of the correction value is complete; if the correction value of the vehicle speed observation position P2 does not converge at the training processing time point, the action plan generation unit 140 determines that the training of the correction value is incomplete. The action plan generation unit 140 can also determine that the training of the correction value is incomplete if no training processing has started and no correction value has been saved in the storage device of the automatic driving control device 100.

[0116] When the action plan generation unit 140 determines that the training of the correction amount is completed, it sets the limit of the distance that can be continuously driven by autonomous driving as a first upper limit value (step S204).

[0117] On the other hand, if the action plan generation unit 140 determines that the training of the correction amount is not completed, it sets the limit of the distance that can be continuously driven by autonomous driving to a second upper limit value that is smaller than the first upper limit value (step S206).

[0118] The first upper limit is set to around ten kilometers, and the second upper limit is set to around a few kilometers.

[0119] It should be noted that the action plan generation unit 140 sets the limit of the "distance" for sustainable driving through autonomous driving based on whether the training of the correction amount is completed. Alternatively, it can set the limit of the "time" for sustainable driving through autonomous driving instead of this or on this basis.

[0120] For example, if the action plan generation unit 140 determines that the training of the correction quantity is completed, it sets the limit of the "time" for continuous autonomous driving to a third upper limit value; if the training of the correction quantity is not completed, it sets the limit of the "time" for continuous autonomous driving to a fourth upper limit value that is smaller than the third upper limit value.

[0121] Next, the position estimation unit 156 estimates the vehicle speed observation position P2 based on the pulse signal output by the wheel speed sensor 42 (step S208).

[0122] For example, the position estimation unit 156 counts the pulse signals output from the wheel speed sensor 42 starting from the last satellite observation position P1 measured by the GNSS receiver 51 (or the location where the GNSS receiver 51 no longer measures the satellite observation position P1), and converts the number of pulse signals obtained from this count, i.e., the wheel rotation speed (rotation speed), into the distance traveled by the vehicle M. Then, the position estimation unit 156 estimates the vehicle speed observation position P2 as the position where the pulse signal counting started, having advanced an amount corresponding to the travel distance from the starting point.

[0123] Next, the correction amount determination unit 158 ​​corrects the current vehicle speed observation position P2 estimated in the process of step S208 based on the correction amount (step S210).

[0124] During the training process, the repeatedly determined correction values ​​are stored in the storage device until the training of the correction values ​​is completed (until the correction values ​​converge). That is, there are multiple candidate correction values ​​used in the process of step S210 in the storage device. Therefore, the correction value determination unit 158 ​​reads any one of the multiple correction values ​​stored in the storage device during the period until the training of the correction values ​​is completed, and uses the read correction value to correct the current vehicle speed observation position P2 estimated in the process of step S208.

[0125] For example, the correction amount determination unit 158 ​​can select the correction amount that was finally determined during the training process from multiple correction amounts, and use the selected correction amount to correct the vehicle speed observation position P2. Alternatively, the correction amount determination unit 158 ​​can also select the correction amount that has the smallest difference Δ with the satellite observation position P1 during the training process from multiple correction amounts, and use the selected correction amount to correct the vehicle speed observation position P2.

[0126] It should be noted that if the training process has not started even once and no correction value has been saved in the storage device of the autonomous driving control device 100, the processing of step S210 can also be omitted.

[0127] Next, instead of using the satellite observation position P1 measured by the GNSS receiver 51, the MPU60 uses the vehicle speed observation position P2 corrected by the correction determination unit 158 ​​to determine the location of the vehicle M on the second map information 62 (high-precision map) (step S212).

[0128] Specifically, the MPU60 uses the vehicle speed observation position P2, corrected by the correction determination unit 158, as the position of the vehicle M on the second map information 62. At this time, the MPU60 can re-determine the recommended lane.

[0129] Next, the action plan generation unit 140 generates the target trajectory based on the position of the vehicle M determined by the MPU 60 on the second map information 62, namely the corrected vehicle speed observation position P2 (step S214).

[0130] For example, the action plan generation unit 140 takes the corrected vehicle speed observation position P2 as the starting point, determines the position that the vehicle M should reach in the future from this starting point as the track point, and then determines the target speed and target acceleration from the starting point. Then, the action plan generation unit 140 generates a track that establishes a correspondence between the target speed and target acceleration and the track formed by connecting multiple track points that the vehicle M should reach in the future in a time sequence, and uses this track as the target track of the vehicle M.

[0131] Next, the second control unit 160 controls the driving drive output device 200, the braking device 210 and the steering device 220 based on the target track generated by the action plan generation unit 140 (the target track using the vehicle speed observation position P2), thereby performing automatic driving (step S216).

[0132] Next, the mode change processing unit 154 determines whether the distance traveled by the vehicle M in autonomous driving exceeds the upper limit value (first upper limit value or second upper limit value) of the distance set in the processing of step S204 or step S206 (step S218).

[0133] If an upper limit for time is set in step S204 or step S206, the mode change processing unit 154 can also determine whether the time the vehicle M has traveled in autonomous driving exceeds the upper limit for time (third upper limit or fourth upper limit) set in step S204 or step S206.

[0134] If the distance (or time) traveled by the vehicle M during autonomous driving is below the upper limit set in step S204 or step S206, the mode change processing unit 154 further determines whether the angle (hereinafter referred to as the turning angle) θ of the wheel or body when the vehicle M turns in the same direction exceeds the specified angle (step S220).

[0135] The specified angle is the angle considered as one full turn when the vehicle M makes a turn, for example, an angle within the range of 270 to 360 degrees.

[0136] Figure 7This diagram illustrates an example of a scenario where the turning angle θ of vehicle M exceeds a predetermined angle. As shown, highways, roundabouts, and other similar roads may have a circular or arc-shaped shape when viewed from above. When vehicle M travels on such roads, its turning angle θ increases with the passage of time t1, t2, t3, ..., t6, reaching an angle close to 360 degrees. Therefore, the mode change processing unit 154 determines that when vehicle M travels on a circular or arc-shaped road, the turning angle θ of vehicle M exceeds a predetermined angle.

[0137] Return to Figure 6 The flowchart is explained below. If, during autonomous driving, the mode change processing unit 154 returns to step S208 when the distance (or time) traveled by the vehicle M is below the upper limit and the turning angle θ of the vehicle M is below the specified angle, then autonomous driving based on the vehicle speed observation position P2 continues.

[0138] On the other hand, if the distance (or time) traveled by the vehicle M in autonomous driving exceeds the upper limit, or if the turning angle θ of the vehicle M exceeds the specified angle, the mode change processing unit 154 changes the autonomous driving mode to a lower control level (step S222). That is, the mode change processing unit 154 lowers the control level of autonomous driving. Thus, the processing of this flowchart ends.

[0139] For example, when the driving mode of the vehicle M is mode A or mode B, the mode change processing unit 154 changes the vehicle to mode C or mode D, which has a lower control level compared to mode B. In other words, when the driving mode of the vehicle M is mode A or mode B, the mode change processing unit 154 changes the vehicle to mode C or mode D, which assigns a greater responsibility (task) to the occupants compared to mode B.

[0140] As described above, Modes A and B do not assign the responsibility of holding the steering wheel 82 to the occupants. In contrast, Modes C and D assign the responsibility of holding the steering wheel 82 to the occupants. Therefore, when the distance (or time) traveled by the vehicle M in automatic driving exceeds the upper limit, or when the turning angle θ of the vehicle M exceeds the prescribed angle, the mode change processing unit 154 changes the driving mode of the vehicle M to a mode that assigns the responsibility of holding the steering wheel 82 to the occupants.

[0141] Furthermore, as a manual driving mode, Mode E naturally assigns the responsibility of holding the steering wheel 82 to the occupants. Therefore, the mode change processing unit 154 can also change from any autonomous driving mode to Mode E if the distance (or time) traveled by the vehicle M in autonomous driving exceeds the upper limit, or if the turning angle θ of the vehicle M exceeds the prescribed angle.

[0142] According to the implementation described above, when the GNSS receiver 51 cannot detect the position of the vehicle M, and the correction amount determination unit 158 ​​has not fully learned the correction amount during the training process, the mode change processing unit 154 reduces the control level of the automatic driving system by a shorter travel distance or time compared to when the correction amount determination unit 158 ​​has fully learned the correction amount during the training process. In other words, when the mode change processing unit 154 has not executed the training process even once or has not executed it enough times and the correction amount has not converged, the mode change processing unit 154 reduces the control level of the automatic driving system by a shorter travel distance or time compared to when the training process has been executed multiple times and the correction amount has converged, even when the GNSS receiver 51 cannot detect the position of the vehicle M. In this way, the control level of the automatic driving system can be changed under appropriate conditions that match the execution status of the correction amount training.

[0143] [Other implementation methods (variations)]

[0144] The following describes other implementation methods (variations). In the above implementation method, a scheme was described to set the limit of the distance or time for sustainable driving by autonomous driving based on two determination results: whether the training of the correction amount is completed. However, it is not limited to this.

[0145] For example, the more times the correction amount is trained (the more times the correction amount is determined), the more the action plan generation unit 140 increases the distance or time limit. In other words, the more times the correction amount is trained (the more times the correction amount is determined), the more the action plan generation unit 140 increases the driving distance (first upper limit or second upper limit) until the control level of autonomous driving is reduced, or the more it increases the driving time (third upper limit or fourth upper limit) until the control level of autonomous driving is reduced.

[0146] [Note]

[0147] The implementation methods described above can be performed as follows.

[0148] (Performance example 1)

[0149] A vehicle control device, comprising:

[0150] Memory containing programs; and

[0151] Hardware processor,

[0152] The hardware processor executes the program to perform the following processing:

[0153] The vehicle's position is determined based on radio waves transmitted from artificial satellites;

[0154] The vehicle's position is determined based on indicators representing the vehicle's behavior.

[0155] The difference between the vehicle's position measured by the radio wave meter (i.e., the first position) and the vehicle's position measured by the index meter (i.e., the second position) is calculated, and the correction amount for the second position is determined based on the calculated difference.

[0156] The vehicle performs autonomous driving based on the first position or the second position corrected based on the correction amount; and

[0157] Without determining the correction amount, compared to determining the correction amount, without measuring the first position, the control level of the autonomous driving is reduced by a shorter driving distance or driving time.

[0158] (Performance example 2)

[0159] A vehicle control device, comprising:

[0160] Memory containing programs; and

[0161] Hardware processor,

[0162] The hardware processor executes the program to perform the following processing:

[0163] The vehicle's position is determined based on radio waves transmitted from artificial satellites;

[0164] The vehicle's position is determined based on indicators representing the vehicle's behavior.

[0165] The difference between the vehicle's position measured by the radio wave meter (i.e., the first position) and the vehicle's position measured by the index meter (i.e., the second position) is calculated, and the correction amount for the second position is determined based on the calculated difference.

[0166] Based on the first position or the second position after being corrected based on the correction amount, the driving mode of the vehicle 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), wherein the second driving mode is a driving mode that assigns a lighter task to the driver compared to the first driving mode.

[0167] Control at least one of the vehicle's steering and acceleration / deceleration according to the determined driving mode;

[0168] If the task assigned to the determined driving mode is not performed by the driver, the vehicle's driving mode is changed to a more demanding driving mode; and

[0169] Without determining the correction amount, compared to determining the correction amount, without measuring the first position, the vehicle's driving mode is changed to a more demanding driving mode based on a shorter driving distance or time.

[0170] 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.

[0171] Symbol Explanation

[0172] 10 cameras

[0173] 12 Radar Devices

[0174] 14. LIDAR (Light Detection and Ranging)

[0175] 16 Object recognition devices

[0176] 20 Communication devices

[0177] 30 HMI

[0178] 40 Vehicle Sensors

[0179] 42 Wheel speed sensors

[0180] 45 Inertial Navigation Device

[0181] 50 navigation devices

[0182] 51 GNSS Receiver

[0183] 52 Navigation HMI

[0184] 53 Path Determination Department

[0185] 54 First Map Information

[0186] 60 MPU

[0187] 61 Recommended Lane Decision Department

[0188] 62 Second Map Information

[0189] 70 Driver monitoring cameras

[0190] 82 Steering Wheel

[0191] 84 Steering and Handling Sensors

[0192] 100 Automatic driving control device

[0193] 120 First Control Department

[0194] 130 Identification Department

[0195] 140 Action Plan Generation Department

[0196] 150 Model Decision Department

[0197] 160 Second Control Unit

[0198] 162 Acquisition Department

[0199] 164 Speed ​​Control Unit

[0200] 166 Steering Control Unit

[0201] 200 Driving drive force output device

[0202] 210 Braking device

[0203] 220 Steering System

Claims

1. A vehicle control device, wherein, The vehicle control device includes: The first measurement unit measures the vehicle's position based on radio waves transmitted from artificial satellites; The second measurement unit measures the position of the vehicle based on indicators representing the vehicle's behavior; The decision unit calculates the difference between the vehicle's position (first position) measured by the first measurement unit and the vehicle's position (second position) measured by the second measurement unit, and determines a correction amount for the second position based on the calculated difference. as well as The driving control unit performs autonomous driving of the vehicle based on either the first position measured by the first measurement unit or a second position corrected based on the correction amount determined by the decision unit. When the correction amount is not determined by the decision unit, the driving control unit reduces the control level of the automated driving system based on a shorter driving distance or driving time, compared to when the correction amount is determined by the decision unit, without the first measurement unit measuring the first position. The first measuring unit repeatedly measures the first position. The second measuring unit repeatedly measures the second position. Each time the first position and the second position are repeatedly measured, the decision unit repeatedly performs the following two processes: calculating the difference between the first position and the second position after correction based on the correction amount, and determining the correction amount based on the calculated difference. The driving control unit performs autonomous driving based on a second position corrected by the last correction value determined from a plurality of correction values ​​repeatedly determined by the decision unit, when the first position is not measured by the first measurement unit. The more times the determination of the correction amount is repeated, the more the driving control unit increases the driving distance or driving time until the control level of the automatic driving is reduced.

2. The vehicle control device according to claim 1, wherein, The driving control unit also reduces the control level of the automatic driving system when the vehicle turns in the same direction at an angle exceeding a specified angle.

3. A vehicle control method, wherein, The computer installed in the vehicle performs the following processing: The vehicle's position is determined based on radio waves transmitted from artificial satellites; The vehicle's position is determined based on indicators representing the vehicle's behavior. The difference between the vehicle's position measured by the radio wave meter (i.e., the first position) and the vehicle's position measured by the index meter (i.e., the second position) is calculated, and the correction amount for the second position is determined based on the calculated difference. The vehicle performs autonomous driving based on the first position or based on the second position after being corrected based on the correction amount; Without determining the correction amount, compared to determining the correction amount, without measuring the first position, the control level of the autonomous driving is reduced by a shorter driving distance or driving time; Repeatedly measure the first position; Repeatedly measure the second position; Each time the first position and the second position are repeatedly measured, the following two processes are repeatedly performed: calculating the difference between the first position and the second position after being corrected based on the correction amount, and determining the correction amount based on the calculated difference; Without measuring the first position, the autonomous driving is performed based on the second position, which is corrected according to the last corrected value among a plurality of repeatedly determined corrected values; and The more times the determination of the correction amount is repeated, the greater the distance or time of travel until the control level of the autonomous driving is reduced.

4. A storage medium storing a program, wherein, The program is used to cause the computer mounted in the vehicle to perform the following processing: The vehicle's position is determined based on radio waves transmitted from artificial satellites; The vehicle's position is determined based on indicators representing the vehicle's behavior. The difference between the vehicle's position measured by the radio wave meter (i.e., the first position) and the vehicle's position measured by the index meter (i.e., the second position) is calculated, and the correction amount for the second position is determined based on the calculated difference. The vehicle performs autonomous driving based on the first position or based on the second position after being corrected based on the correction amount; Without determining the correction amount, compared to determining the correction amount, without measuring the first position, the control level of the autonomous driving is reduced by a shorter driving distance or driving time; Repeatedly measure the first position; Repeatedly measure the second position; Each time the first position and the second position are repeatedly measured, the following two processes are repeatedly performed: calculating the difference between the first position and the second position after being corrected based on the correction amount, and determining the correction amount based on the calculated difference; Without measuring the first position, the autonomous driving is performed based on the second position, which is corrected according to the last corrected value among a plurality of repeatedly determined corrected values; and The more times the determination of the correction amount is repeated, the greater the distance or time of travel until the control level of the autonomous driving is reduced.

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