Determination device, determination method, and storage medium

Abstract

Provided are a determination device, a determination method, and a storage medium that make it possible to more appropriately determine whether or not a dividing line is correct on the basis of the dividing line around a vehicle and the traveling condition of another vehicle. The determination device is provided with: a first recognition unit that recognizes, on the basis of the output of the detection device, the surroundings including the first division line and the other vehicle; a second recognition unit that recognizes, from the map information, a second division line that divides lanes around the host vehicle on the basis of the location information of the host vehicle; and a determination unit that determines whether or not at least one of the first and second division lines is correct on the basis of the traveling trajectory of the other vehicle and at least one of the first and second division lines. When the first and second other vehicles recognized by the first recognition unit move laterally in front of the own vehicle and the positions thereof after the lateral movement differ by a predetermined distance or more, the determination unit determines whether or not to be correct on the basis of the first and second other vehicles.

Description

Technical Field

[0001] The present invention relates to a determination device, a determination method and a storage medium. Background Art

[0002] In recent years, efforts to provide sustainable transportation system access that is also considerate of vulnerable traffic participants, particularly those in vulnerable situations, have intensified. To achieve this, research and development efforts are underway to further improve traffic safety and convenience through autonomous driving technology. In this regard, there is a known technique for controlling a vehicle's driving pattern based on the degree of parallelism between the driving trajectories of other vehicles in the vicinity and the camera dividing line when a deviation is determined between the road dividing line shown in a camera image (camera dividing line) and the road dividing line shown in map information (map dividing line). (For example, see Japanese Patent Application Laid-Open No. 2023-148405). Summary of the Invention

[0003] However, existing autonomous driving technology, when determining the accuracy of camera and map dividing lines based on the driving trajectories of other vehicles, may not be able to properly determine whether the other vehicle is traveling along the dividing line or changing lanes. This poses the problem of not being able to properly determine the accuracy of the dividing lines based on the driving trajectories of other vehicles.

[0004] To address the aforementioned issues, one of the objectives of this application is to provide a determination device, determination method, and storage medium that can more appropriately determine the correctness of a demarcation line based on the demarcation lines surrounding the vehicle and the driving conditions of other vehicles. Furthermore, this can contribute to the development of a sustainable transportation system.

[0005] The determination device, determination method, and storage medium according to the present invention have the following configurations.

[0006] (1): A determination device according to one embodiment of the present invention comprises: a first recognition unit for recognizing a surrounding condition including a first dividing line dividing a lane of the vehicle and other vehicles existing around the vehicle based on an output of a detection device that detects the surrounding condition of the vehicle; a second recognition unit for recognizing a second dividing line dividing a lane around the vehicle from map information based on position information of the vehicle; and a determination unit for determining whether at least one of the first dividing line and the second dividing line is correct based on at least one of the first dividing line and the second dividing line and the driving trajectory of the other vehicle, wherein the determination unit does not perform the determination based on the first other vehicle and the second other vehicle if the first other vehicle and the second other vehicle identified by the first recognition unit have moved laterally in front of the vehicle and their positions after the lateral movement differ by more than a specified distance.

[0007] (2): Based on the solution of (1) above, the prescribed distance is set based on the width of the lane divided by the first dividing line or the width of the lane divided by the second dividing line.

[0008] (3): Based on the solution of (1) above, the predetermined distance is set based on the distance between the lateral positions of the first other vehicle and the second other vehicle before the lateral movement.

[0009] (4): Based on the solution of (1) above, the determination unit performs the correctness determination based on the third other vehicle when the first recognition unit recognizes a third other vehicle that is different from the first other vehicle and the second other vehicle, and the third other vehicle does not move laterally in the same direction as the first other vehicle and the second other vehicle that have moved laterally at positions that are more than a specified distance apart.

[0010] (5): Based on the scheme of (1) above, the determination unit performs the correctness determination based on the first other vehicle and the second other vehicle when there is a location in front of the vehicle where the number of lanes in which the vehicle can travel is reduced, and the first other vehicle and the second other vehicle move laterally within a specified range in front of the location.

[0011] (6): In a determination method according to one embodiment of the present invention, the determination method causes a computer to perform the following processing: based on the output of a detection device that detects the surrounding conditions of the vehicle, identify the surrounding conditions including a first dividing line that divides the driving lane of the vehicle and other vehicles existing around the vehicle; based on the position information of the vehicle, identify a second dividing line that divides the lane around the vehicle from map information; based on at least one of the first dividing line and the second dividing line and the driving trajectory of the other vehicle, perform a correctness determination on whether at least one of the first dividing line and the second dividing line is correct; and when the identified first other vehicle and second other vehicle have moved laterally in front of the vehicle and the positions after the lateral movement differ by more than a specified distance, the correctness determination is not performed based on the first other vehicle and the second other vehicle.

[0012] (7): A storage medium involved in one embodiment of the present invention stores a program, which causes a computer to perform the following processing: based on the output of a detection device that detects the surrounding conditions of the vehicle, identifying the surrounding conditions including a first dividing line that divides the driving lane of the vehicle and other vehicles existing in the surroundings of the vehicle; based on the position information of the vehicle, identifying a second dividing line that divides the lane around the vehicle from map information; based on at least one of the first dividing line and the second dividing line and the driving trajectory of the other vehicle, making a correctness judgment on whether at least one of the first dividing line and the second dividing line is correct; and when the identified first other vehicle and second other vehicle have moved laterally in front of the vehicle and the positions after the lateral movement differ by more than a specified distance, not making the correctness judgment based on the first other vehicle and the second other vehicle.

[0013] According to the above-mentioned solutions (1) to (7), the correctness of the dividing lines can be more appropriately determined based on the dividing lines around the host vehicle and the driving conditions of other vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 It is a configuration diagram of a vehicle system including a vehicle control device according to an embodiment.

[0015] Figure 2 This is a functional configuration diagram of the first control unit and the second control unit.

[0016] Figure 3 This is a diagram for explaining the determination process in the first scenario.

[0017] Figure 4 This is a diagram for explaining the determination process in the second scenario.

[0018] Figure 5 This is a diagram for explaining the determination process in the third scenario.

[0019] Figure 6 This is a flowchart showing an example of processing executed by the automatic driving control device according to the embodiment. DETAILED DESCRIPTION

[0020] Embodiments of the determination device, determination method, and storage medium of the present invention are described below with reference to the accompanying drawings. As an example, an embodiment in which a vehicle control device including a determination device that determines whether a road dividing line (or lane) dividing a lane in which a vehicle is traveling is the correct one (or lane) will be described. Autonomous driving refers to, for example, automatically controlling one or both of a vehicle's steering and speed to perform driving control. Such driving control may also include ACC (Adaptive Cruise Control System), TJP (Traffic Jam Pilot), LKAS (Lane Keeping Assistance System), ALC (Automated Lane Change), CMBS (Collision Mitigation Brake System), and the like. Autonomous vehicles may also perform driving control manually by the vehicle user (e.g., passenger) (so-called manual driving). The following description applies to the case where left-hand traffic regulations apply, but for right-hand traffic regulations, the left and right sections can be reversed.

[0021] [Overall structure]

[0022] Figure 1 This is a structural diagram of a vehicle system 1 including a vehicle control device according to an embodiment. The vehicle equipped with the vehicle system 1 (hereinafter referred to as the host vehicle M) is, for example, a two-wheeled, three-wheeled, or four-wheeled vehicle. Its driving 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 power generated by a generator connected to the internal combustion engine or power discharged from a battery such as a secondary battery or a fuel cell.

[0023] 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, an HMI (Human Machine Interface) 30, vehicle sensors 40, a navigation device 50, an MPU (Map Positioning Unit) 60, a driving control element 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 multiplexed communication lines such as CAN (Controller Area Network) communication lines, serial communication lines, and wireless communication networks. Figure 1 The illustrated configuration is merely an example; portions of the configuration may be omitted, or additional configurations may be added. The combination of camera 10, radar device 12, LIDAR 14, and object recognition device 16 is an example of a "detection device DD." HMI 30 is an example of an "output device." Autonomous driving control device 100 is an example of a "vehicle control device."

[0024] The camera 10 is, for example, a digital camera that utilizes a solid-state imaging element such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor). The camera 10 is mounted at any location on the vehicle M equipped with the vehicle system 1. When photographing the front, the camera 10 is mounted on the upper portion of the front windshield, the back of the rearview mirror inside the vehicle, the front head of the vehicle body, etc. When photographing the rear, the camera 10 is mounted on the upper portion of the rear windshield, the tailgate, etc. When photographing the side, the camera 10 is mounted on the rearview mirror on the door, etc. The camera 10, for example, periodically and repeatedly photographs the surroundings of the vehicle M. The camera 10 may also be a stereo camera.

[0025] The radar device 12 radiates radio waves, such as millimeter waves, toward the periphery of the host vehicle M and detects the radio waves (reflected waves) reflected by surrounding objects to detect at least the object's position (range and direction). The radar device 12 is mounted anywhere on the host vehicle M. The radar device 12 can also detect the position and velocity of objects using the FM-CW (Frequency Modulated Continuous Wave) method.

[0026] The LIDAR 14 irradiates light around the vehicle M and measures the scattered light. The LIDAR 14 detects the distance to the object based on the time from light emission to light reception. The irradiated light is, for example, a pulsed laser. The LIDAR 14 is mounted at any location on the vehicle M.

[0027] The object recognition device 16 performs sensor fusion processing on the detection results from some or all of the camera 10, radar device 12, and LIDAR 14 to identify the position, type, speed, and other aspects of the object. The object recognition device 16 outputs the recognition results to the automatic driving control device 100. Alternatively, the object recognition device 16 may output the detection results from the camera 10, radar device 12, and LIDAR 14 to the automatic driving control device 100 as is. In this case, the object recognition device 16 may be omitted from the configuration of the vehicle system 1 (detection device DD).

[0028] The communication device 20 communicates with, for example, other vehicles around the vehicle M, terminal devices of users of the vehicle M, or various server devices using a network such as a cellular network, a Wi-Fi network, Bluetooth (registered trademark), DSRC (Dedicated Short Range Communication), a LAN (Local Area Network), a WAN (Wide Area Network), or the Internet.

[0029] The HMI 30 outputs various information to the occupants of the vehicle M and receives input operations from the occupants. The HMI 30 includes, for example, various display devices, speakers, buzzers, touch panels, switches, buttons, microphones, and the like.

[0030] The vehicle sensors 40 include a speed sensor for detecting the speed of the vehicle M, an acceleration sensor for detecting acceleration, a yaw rate sensor for detecting yaw rate (for example, the angular velocity of rotation about a vertical axis passing through the center of gravity of the vehicle M), and an azimuth sensor for detecting the orientation of the vehicle M. The vehicle sensors 40 may also include a position sensor for detecting the vehicle's position. A position sensor is an example of a "position measurement unit." For example, a position sensor acquires position information (longitude and latitude) from a GPS (Global Positioning System) device. Alternatively, the position sensor may acquire position information using a GNSS (Global Navigation Satellite System) receiver 51 of the navigation device 50. The vehicle sensors 40 may also derive the speed of the vehicle M from the difference (i.e., distance) in position information at a predetermined time interval in the position sensor. The detection results of the vehicle sensors 40 are output to the automatic driving control device 100.

[0031] The navigation device 50 includes, for example, a GNSS receiver 51, a navigation HMI 52, and a route determination unit 53. The navigation device 50 stores first map information 54 in a storage device such as a hard disk drive (HDD) or flash memory. The GNSS receiver 51 determines the position of the vehicle M based on signals received from GNSS satellites. The position of the vehicle M can also be determined or supplemented by an INS (Inertial Navigation System) utilizing the output of the vehicle sensors 40. The navigation HMI 52 includes a display, speakers, a touch panel, keys, etc. The GNSS receiver 51 can also be provided in the vehicle sensors 40. The navigation HMI 52 can also partially or entirely be shared with the HMI 30 described above. The route determination unit 53, for example, refers to the first map information 54 to determine a route (hereinafter referred to as a "map route") from the position of the vehicle M determined by the GNSS receiver 51 (or an input arbitrary position) to a destination input by the occupant using the navigation HMI 52. The first map information 54 is information representing the road shape, for example, by representing road links and nodes connected by the links. The first map information 54 may also include POI (Point of Interest) information and the like. The route on the map is output to the MPU 60. The navigation device 50 may also provide route guidance using the navigation HMI 52 based on the route on the map. The navigation device 50 may also transmit its current location and destination to a navigation server via the communication device 20 and obtain a route equivalent to the route on the map from the navigation server. The navigation device 50 outputs the determined route on the map to the MPU 60.

[0032] The MPU 60, for example, includes a recommended lane determination unit 61, which stores second map information 62 in a storage device such as a HDD or flash memory. The recommended lane determination unit 61 divides the route on the map provided by the navigation device 50 into multiple blocks (for example, every 100 meters in the vehicle's travel direction) and determines a recommended lane for each block by referring to the second map information 62. The recommended lane determination unit 61 determines the lane from the left to be driven. If the route on the map branches, the recommended lane determination unit 61 determines a recommended lane so that the vehicle M can travel on a reasonable route to the branch destination.

[0033] The second map information 62 is map information with higher accuracy than the first map information 54. The second map information 62 includes, for example, the number of lanes, the type and shape of road dividing lines (hereinafter referred to as dividing lines), information on lane centers, and road boundary information. The second map information 62 may also include information on whether a road boundary includes a structure that a vehicle cannot pass through (including crossing over or contacting). Examples of such structures include guardrails, curbs, medians, and fences. Impassable structures may also include low steps that are passable as long as vehicle vibrations that do not normally occur are tolerated. The second map information 62 may include road shape information, traffic restriction information, address information (address, postal code), facility information, parking information, and telephone number information. Examples of road shape information include road curvature (also referred to as curvature radius, hereinafter the same), width, and slope. The second map information 62 can be updated at any time by communicating with an external device via the communication device 20. The first map information 54 and the second map information 62 may also be provided as a single piece of map information. The map information may also be stored in the storage unit 190 .

[0034] The driving operating parts 80 include, for example, a steering wheel, an accelerator pedal, and a brake pedal. The driving operating parts 80 may also include a shift lever, a special-shaped steering gear, a joystick, and other operating parts. Each operating part of the driving operating parts 80 is equipped with an operation detection unit that detects the amount of operation of the operating part by the occupant or the presence or absence of operation. The operation detection unit detects, for example, the steering angle of the steering wheel, the steering torque, the amount of depression of the accelerator pedal and the brake pedal. In addition, the operation detection unit outputs the detection results to the automatic driving control device 100, or one or both of the driving force output device 200, the braking device 210, and the steering device 220.

[0035] The automatic driving control device 100 performs various driving controls related to automatic driving on the host vehicle M. The automatic driving control device 100 includes, for example, a first control unit 120, a second control unit 160, an HMI control unit 180, and a storage unit 190. The first control unit 120, the second control unit 160, and the HMI control unit 1800 are each implemented by a hardware processor, such as a CPU (Central Processing Unit), executing a program (software). Some or all of these components may be implemented using hardware (including circuitry) such as an LSI (Large Scale Integration), an ASIC (Application Specific Integrated Circuit), an FPGA (Field-Programmable Gate Array), a GPU (Graphics Processing Unit), or a System on Chip (SOC), or through a combination of software and hardware. The above-mentioned program can be pre-stored in a storage device such as an HDD, a flash memory, or the like of the automatic driving control device 100 (a storage device having a non-temporary storage medium), or can be stored in a removable storage medium such as a DVD, a CD-ROM, or a memory card, and installed in the storage device of the automatic driving control device 100 by assembling the storage medium (non-temporary storage medium) in a drive device, a card slot, or the like.

[0036] The storage unit 190 may be implemented by any of the aforementioned storage devices, or by an EEPROM (Electrically Erasable Programmable Read Only Memory), a ROM (Read Only Memory), or a RAM (Random Access Memory). For example, the storage unit 190 stores various information and programs according to the embodiments. The storage unit 190 may also store map information (e.g., the first map information 54 and the second map information 62).

[0037] Figure 2This 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 and an action plan generation unit 140. The first control unit 120 implements, for example, functions based on AI (Artificial Intelligence) and functions based on a pre-given model in parallel. For example, the function of "identifying intersections" can be achieved by performing intersection identification based on deep learning, etc. and identification based on pre-given conditions (the presence of signals and road signs that can be pattern-matched, etc.) in parallel, and comprehensively evaluating both by assigning scores to both. In this way, the reliability of autonomous driving is ensured. The first control unit 120 performs control related to autonomous driving of the vehicle M, for example, based on instructions from the MPU 60, the HMI control unit 180, etc.

[0038] The recognition unit 130 identifies the surrounding conditions of the host vehicle M based on the recognition results of the detection device DD (information input from the camera 10, radar device 12, and LIDAR 14 via the object recognition device 16). For example, the recognition unit 130 identifies the position, velocity, acceleration, and other conditions of objects surrounding the host vehicle M (within a predetermined distance). Examples of such objects include other vehicles (surrounding vehicles), road users (pedestrians, bicycles, etc.), road structures, and other surrounding obstacles. Examples of road structures include road signs, traffic signals, railway crossings, curbs, medians, guardrails, and fences. The position of an object is identified as an absolute coordinate system with a representative point (such as the center of gravity or the center of the drive shaft) of the host vehicle M as the origin, and is used for control. The position of an object can be represented by a representative point such as the center of gravity or a corner, or by a displayed area. The so-called "state" of an object, for example, when the object is a moving object such as another vehicle, may also include the acceleration, jerk, or "action state" of the moving object (for example, whether the other vehicle is changing lanes or is about to change lanes).

[0039] The recognition unit 130 includes, for example, a first recognition unit 132 and a second recognition unit 134. Details of these functions will be described later.

[0040] The action plan generation unit 140 generates an action plan for autonomous driving of the host vehicle M based on, for example, the recognition results from the recognition unit 130. For example, the action plan generation unit 140 generates a target trajectory for the host vehicle M to automatically travel (independent of driver control) in the future, while maintaining the recommended lane determined by the recommended lane determination unit 61 and adapting to the surrounding conditions of the host vehicle M based on the recognition results from the recognition unit 130 and the road shape around the host vehicle M's current position obtained from map information. The target trajectory includes, for example, a speed element. For example, the target trajectory is represented by a sequence of locations (track points) that the host vehicle M should reach. Track points are locations that the host vehicle M should reach at every predetermined distance (e.g., a few meters) along the route. Separately, target speeds and target accelerations are generated as part of the target trajectory for each predetermined sampling time (e.g., a few tenths of a second). Alternatively, track points may be locations that the host vehicle M should reach at the sampling time of each predetermined sampling time. In this case, information on the target speed and target acceleration is expressed by the intervals between track points.

[0041] The action plan generation unit 140 can set events for autonomous driving when generating the target trajectory. Examples of these events include: a constant speed driving event that causes the host vehicle M to travel in the same lane at a constant speed; a following driving event that causes the host vehicle M to follow another vehicle that is within a specified distance (e.g., within 100 meters) ahead of the host vehicle M and is closest to the host vehicle M; a lane change event that causes the host vehicle M to change lanes from its own lane to an adjacent lane; a diverging event that causes the host vehicle M to diverge from a lane on the destination side at a road divergence point; a merging event that causes the host vehicle M to merge onto the main road at a merging point; and a takeover event that terminates autonomous driving and switches to manual driving. Examples of these events include: an overtaking event that causes the host vehicle M to temporarily change lanes to an adjacent lane, overtake a preceding vehicle in the adjacent lane, and then change lanes back to its original lane; and an avoidance event that causes the host vehicle M to at least brake or swerve to avoid an obstacle ahead of the host vehicle M.

[0042] For example, the action plan generation unit 140 can change an event already set for the current section to another event, or set a new event for the current section, based on the surrounding conditions of the vehicle M detected during travel. The action plan generation unit 140 can also change an event already set for the current section to another event, or set a new event for the current section, based on an occupant's operation of the HMI 30. The action plan generation unit 140 generates a target trajectory corresponding to the set event.

[0043] The action plan generator 140 includes, for example, a determination unit 142 and an execution control unit 144. Details of these functions will be described later. For example, the recognition unit 130 and the determination unit 142 are examples of a "determination device." The execution control unit 144 and the second control unit 160 are examples of a "driving control unit."

[0044] The second control unit 160 controls the travel driving force output device 200 , the braking device 210 , and the steering device 220 so that the host vehicle M passes through the target trajectory generated by the action plan generation unit 140 at a predetermined timing.

[0045] The second control unit 160 includes, for example, a target trajectory acquisition unit 162, a speed control unit 164, and a steering control unit 166. The target trajectory acquisition unit 162 acquires information on the target trajectory (trajectory point) generated by the action plan generation unit 140 and stores the information in a memory (not shown). The speed control unit 164 controls the driving force output device 200 or the braking device 210 based on the speed factor associated with the target trajectory stored in the memory. The steering control unit 166 controls the steering device 220 based on the curvature of the target trajectory stored in the memory. The processing of the speed control unit 164 and the steering control unit 166 is implemented, for example, through a combination of feedforward control and feedback control. As an example, the steering control unit 166 performs a combination of feedforward control based on the curvature of the road ahead of the vehicle M and feedback control based on deviation from the target trajectory.

[0046] return Figure 1 The HMI control unit 180 uses the HMI 30 to notify the occupants of specified information. Examples of the specified information include information related to the status of the host vehicle M, information related to driving control, and other information related to the driving of the host vehicle M. Examples of information related to the status of the host vehicle M include the speed, engine speed, and gear position of the host vehicle M. Examples of information related to driving control include information regarding the execution of driving control based on autonomous driving, information inquiring whether to start autonomous driving, information regarding the status of driving control based on autonomous driving, information regarding the automation level, and information urging the occupants to drive when switching from autonomous driving to manual driving. The specified information may also include information unrelated to the driving of the host vehicle M, such as television programs and content stored on storage media such as DVDs (e.g., movies). Examples of the specified information include information related to the current location, destination, and fuel level of the host vehicle M during autonomous driving. The HMI control unit 180 may also output information received by the HMI 30 to the communication device 20, the navigation device 50, the first control unit 120, and other devices.

[0047] The HMI control unit 180 may also cause the HMI 30 to output inquiry information to the occupant, processing results performed by the first control unit 120 and the second control unit 160 , etc. The HMI control unit 180 may also transmit various information caused to be output by the HMI 30 to a terminal device used by a user of the vehicle M via the communication device 20 .

[0048] The driving force output device 200 outputs the driving force (torque) used to propel the vehicle to the drive wheels. The driving force output device 200 comprises, for example, a combination of an internal combustion engine, an electric motor, and a transmission, and an ECU (Electronic Control Unit) that controls them. The ECU controls the aforementioned components based on information input from the second control unit 160 or from the accelerator pedal of the driving control element 80.

[0049] The braking device 210 includes, for example, a brake caliper, a hydraulic cylinder that transmits hydraulic pressure to the caliper, an electric motor that generates hydraulic pressure in the hydraulic cylinder, and a brake ECU. The brake ECU controls the electric motor based on information input from the second control unit 160 or information input from the brake pedal of the driver's operating element 80, and outputs braking torque corresponding to the braking operation to each wheel. The braking device 210 may include a mechanism that transmits the hydraulic pressure generated by the operation of the brake pedal to the hydraulic cylinder via the master hydraulic cylinder as a backup. The braking device 210 is not limited to the structure described above and may also be an electronically controlled hydraulic brake device that controls an actuator based on information input from the second control unit 160 to transmit the hydraulic pressure from the master hydraulic cylinder to the hydraulic cylinder.

[0050] The steering system 220 includes, for example, a steering ECU and an electric motor. The electric motor, for example, applies force to a rack-and-pinion mechanism to change the direction of the steered wheels. The steering ECU drives the electric motor based on information input from the second control unit 160 or information input from the steering wheel of the driving control unit 80, thereby changing the direction of the steered wheels.

[0051] [Identification Department and Action Plan Generation Department]

[0052] Next, the functions of the recognition unit 130 (first recognition unit 132, second recognition unit 134) and the action plan generation unit 140 (determination unit 142, execution control unit 144) will be described in detail. The following primarily describes the determination process and driving control (travel control) based on the determination results in this embodiment. The determination process will be described in several scenarios.

[0053] [Scene 1]

[0054] Figure 3 This is a diagram for explaining the determination process in the first scene. Figure 3In the example shown, dividing lines CL1 and CL2 recognized by the detection device DD and dividing lines ML1 to ML3 obtained from map information (e.g., second map information 62) based on the position information of the host vehicle M are shown. In the map information, lane L1 is divided by dividing lines ML1 and ML2, and lane L2 is divided by dividing lines ML2 and ML3. Lanes L1 and L2 are lanes that can travel in the same direction (the X-axis direction in the figure). Figure 3 In the example of , the dividing lines CL1 to CL2 are examples of “first dividing lines”, and the dividing lines ML1 to ML3 are examples of “second dividing lines”. Figure 3 In the example, it is assumed that the host vehicle M is traveling at a speed VM in lane L1, another vehicle m1 is traveling at a speed Vm1 ahead of the host vehicle M, and another vehicle m2 is traveling at a speed Vm2 ahead of the other vehicle m1 (host vehicle M). The other vehicle m1 is an example of a "first other vehicle," and the other vehicle m2 is an example of a "second other vehicle."

[0055] The first recognition unit 132 recognizes the surrounding conditions of the vehicle M based on the output of the detection device DD that detects the surrounding conditions of the vehicle M. For example, the first recognition unit 132 recognizes the left and right dividing lines CL1 and CL2 that divide the driving lane (lane L1) of the vehicle M based on the image captured by the camera 10 (hereinafter, camera image). The first recognition unit 132 can also recognize the dividing line that divides the adjacent lane (lane L2) adjacent to the driving lane. Hereinafter, the dividing lines CL1 and CL2 are sometimes referred to as "camera dividing lines CL1 and CL2". For example, the first recognition unit 132 analyzes the camera image, extracts edge points with large brightness differences with adjacent pixels in the image, and connects the edge points to respectively identify the camera dividing lines CL1 and CL2 in the image plane. The first recognition unit 132 calculates the positions of the camera dividing lines CL1 and CL2 based on the position of the representative point of the vehicle M to the vehicle coordinate system (for example Figure 3 The first recognition unit 132 may also recognize the curvature or curvature change of each camera dividing line CL1, CL2. The so-called curvature change refers to, for example, the time rate of change of the curvature of the camera dividing lines CL1, CL2 at the front X [m] when viewed from the vehicle M by the camera 10. The first recognition unit 132 may also average the curvature or curvature change of each camera dividing line CL1, CL2 to recognize the curvature or curvature change of the lane divided by the camera dividing lines CL1, CL2. The camera dividing lines CL1, CL2 may also be recognized or corrected based on the output of a detection device other than the camera 10.

[0056] The first recognition unit 132 identifies other vehicles located around the vehicle M (within a predetermined distance). For example, the first recognition unit 132 identifies other vehicles m1 and m2 located in front of the vehicle M based on the output of a detection device DD that detects the surrounding conditions of the vehicle M. The first recognition unit 132 identifies the positions (relative positions relative to the vehicle M) and speeds (relative speeds relative to the vehicle M) of each of the other vehicles m1 and m2. The first recognition unit 132 may also identify driving position information of the other vehicles m1 and m2. Driving position information refers to, for example, driving trajectories K1 and K2 based on the positions of representative points of each of the other vehicles m1 and m2 at a predetermined time. The driving position information may include, for example, information regarding the predicted future driving trajectories of the other vehicles m1 and m2 based on the driving trajectories K1 and K2 and the orientations of the other vehicles m1 and m2.

[0057] The second recognition unit 134 identifies lane dividing lines around the vehicle M (within a predetermined distance) from map information, based on the position of the vehicle M detected by the vehicle sensor 40 and the GNSS receiver 51. For example, based on the position information of the vehicle M and referring to the map information, the second recognition unit 134 identifies lane dividing lines ML1-ML3 that are located in the direction in which the vehicle M is traveling or in which the vehicle M can travel. Hereinafter, the lane dividing lines ML1-ML3 may be referred to as "map lane dividing lines ML1-ML3."

[0058] The second recognition unit 134 may also recognize the map dividing lines ML1 and ML2 as the lines that, among the recognized map dividing lines ML1 to ML3, demarcate the lane L1 in which the vehicle M is traveling. The second recognition unit 134 may identify the curvature or curvature change of each of the map dividing lines ML1 to ML3 from the map information. The second recognition unit 134 may also average the curvature or curvature change of each of the map dividing lines ML1 to ML3 to identify the curvature or curvature change of each lane L1 or L2 demarcated by the map dividing lines.

[0059] The determination unit 142 determines whether at least one of the camera dividing lines CL (CL1, CL2) and the map dividing lines ML (ML1-ML3) is correct, based on, for example, at least one of the camera dividing lines CL and the map dividing lines ML, as well as the driving trajectories K1 and K2 of other vehicles m1 and m2. Based on the determination result of the determination unit 142, the execution control unit 144 generates a target trajectory for driving control such that the host vehicle M travels along the dividing line determined to be correct, or performs control to terminate driving control (or not initiate driving control) if both dividing lines are determined to be incorrect.

[0060] exist Figure 3In the example, as a correctness determination, the determination unit 142 first determines whether the camera dividing lines CL (CL1, CL2) recognized by the first recognition unit 132 and the map dividing lines ML (ML1, ML2) recognized by the second recognition unit 134 deviate from each other. For example, the determination unit 142 derives the degree of deviation between the nearest dividing lines CL1 and ML1 on the left side as viewed from the vehicle M, and the degree of deviation between the nearest dividing lines CL2 and ML2 on the right side as viewed from the vehicle M. If the derived degree of deviation is greater than a threshold, the determination unit 142 determines that the camera dividing line CL and the map dividing line ML have deviated from each other; if the derived degree of deviation is less than the threshold, the determination unit 142 determines that the camera dividing line CL and the map dividing line ML have not deviated from each other. This deviation determination is repeated at a predetermined timing or period.

[0061] For example, the determination unit 142 overlaps the camera dividing lines CL1 and CL2 based on the position of the representative point of the vehicle M in the plane of the vehicle coordinate system (XY plane), and overlaps the map dividing lines ML1 and ML2. Furthermore, when determining the deviation of the dividing lines of the comparison object (dividing lines CL1 and ML1, dividing lines CL2 and ML2), the determination unit 142 determines that the dividing lines are deviated if the degree of deviation of each dividing line is greater than a threshold value, and determines that there is no deviation if the degree of deviation is less than the threshold value. The so-called degree of deviation refers to, for example, the degree of offset of the lateral position (for example, the Y-axis direction in the figure). Figure 3 In the example, the lateral position offset D1 between the dividing lines CL1 and ML1 and the lateral position offset D2 between the dividing lines CL2 and ML2 can be set as the deviation degree, or the average value, maximum value, or minimum value of the offsets D1 and D2 can be set as the deviation degree.

[0062] The degree of deviation may also be the degree (size) of the angle formed by the two dividing lines of the comparison object instead of (or based on) the offset of the lateral position mentioned above. Figure 3 In the example, the angle θ1 formed by the dividing lines CL1 and ML1 and the angle θ2 formed by the dividing lines CL2 and ML2 may be respectively set as the deviation degrees, or the average value, maximum value, or minimum value of the angles θ1 and θ2 may be set as the deviation degree.

[0063] The degree of deviation may also be determined by the degree (magnitude) of the difference in curvature variation between the dividing lines, rather than the aforementioned lateral position offset or the angle formed by the dividing lines (or in addition thereto). The curvature variation is primarily used when the lane is curved. The determination unit 142 may use the average of the difference in curvature variation between the dividing lines CL1 and ML1 and the difference in curvature variation between the dividing lines CL2 and ML2, or the maximum or minimum of these differences. The determination unit 142 may also use the difference between the average of the curvature variation between the dividing lines CL1 and CL2 and the average of the curvature variation between the dividing lines ML1 and ML2. Alternatively, the difference between the curvature variation of the lane (lane L1) identified from the camera image and the curvature variation of the lane identified from map information may be used.

[0064] For example, if the recognition accuracy of camera dividing lines CL1 and CL2 identified by the first recognition unit 132 falls below a threshold or the camera dividing lines cannot be recognized, the determination unit 142 may derive the degree of deviation using the angles between the driving trajectories K1 and K2 of other vehicles traveling nearby and the map dividing line ML. The determination unit 142 may also set an imaginary dividing line parallel to the driving trajectories K1 and K2 and determine the degree of deviation between the set imaginary dividing line and the map dividing line ML. The determination unit 142 may also perform a deviation determination from the dividing line using the driving trajectories K1 and K2 regardless of the recognition result of the camera dividing line CL. Similarly, if the camera dividing line CL is identified and the map information cannot identify the surrounding map dividing lines ML, the determination unit 142 may perform a deviation determination between the camera dividing line CL and the driving trajectories K1 and K2, and determine whether the camera dividing line CL is correct based on the determination result. The determination unit 142 may also perform a deviation determination between the camera dividing line CL and the driving trajectories K1 and K2 regardless of the recognition result of the map dividing line ML.

[0065] By using the above-described deviation determination using the degree of deviation, if the camera dividing line CL and the map dividing line ML are determined to be aligned, the determination unit 142 determines that the camera dividing line CL and the map dividing line ML are correct dividing lines during the accuracy determination. If the camera dividing line CL and the map dividing line ML are determined to be aligned, the determination unit 142 determines that at least one of the camera dividing line CL and the map dividing line ML is incorrect. For example, if the camera dividing line CL and the map dividing line ML are aligned and driving control is being performed for the vehicle M to avoid a forward obstacle, the determination unit 142 determines that the camera dividing line CL is incorrect (or the map dividing line ML is correct).

[0066] The determination unit 142 determines that the camera dividing line CL is incorrect (or that the map dividing line ML is correct) when the camera dividing line CL deviates from the map dividing line ML and a predetermined number or more of the recognized driving trajectories of multiple other vehicles are driving trajectories along the map dividing line ML (including a predetermined allowable range). Furthermore, the determination unit 142 determines that the map dividing line ML is incorrect (or that the camera dividing line CL is correct) when the camera dividing line CL deviates from the map dividing line ML and a predetermined number or more of the recognized driving trajectories of multiple other vehicles are driving trajectories along the camera dividing line CL (including a predetermined allowable range).

[0067] If the first recognition unit 132 recognizes information indicating a lane change (e.g., lane addition or deletion), such as a road construction sign or a lane addition or deletion road sign, but the road information obtained from the map information does not contain any lane change information, the determination unit 142 may determine that the map dividing line is incorrect (or the camera dividing line is correct) as outdated map information (or information inconsistent with the current road shape). For example, if the degree of deviation is greater than an upper threshold value or the camera dividing line and the map dividing line have different numbers of dividing lines, the determination unit 142 may determine that both the camera dividing line and the map dividing line are incorrect.

[0068] Here, for example, if the other vehicles m1 and m2 identified by the first recognition unit 132 have moved laterally in front of the vehicle M and their positions after the lateral movement differ by a predetermined distance or more, the determination unit 142 does not make a correctness determination based on the other vehicles m1 and m2. Instead of not making a correctness determination, the determination unit 142 may not determine that at least one of the camera dividing line CL and the map dividing line ML is correct. The so-called lateral movement refers to, for example, movement of the map dividing line ML in the road width direction exceeding a threshold value (in Figure 3 Movement in the Y-axis direction). The so-called threshold value can be, for example, the width of one lane equivalent to the lane change (for example, the width W1 of the lane L1) or a fixed distance. The so-called lateral movement can also be a movement above the threshold in a direction perpendicular to the direction of travel of the vehicle M (in other words, the vehicle width direction of the vehicle M). The so-called lateral movement can also be the reference position (for example, center of gravity, center, front end) of the other vehicles m1, m2, the entire vehicle body or the driving trajectory K1, K2 crossing the dividing line (for example, the map dividing line ML) that divides the lane in which the vehicle is traveling (which can also be renamed as "crossing" or "passing"). The specified distance can be, for example, set based on the width W1 of the lane L1 in which the vehicle M is traveling, or it can be a fixed distance. The width W1 can be, for example, the width of the lane divided by the camera dividing lines CL1, CL2, or the width of the lane divided by the map dividing lines ML1, ML2.

[0069] exist Figure 3 In the example, the determination unit 142 obtains positions P1 and P2, respectively, across the map dividing line ML2 as the positions of the lateral movements of other vehicles m1 and m2, and obtains a distance TD1 between the obtained positions P1 and P2. Distance TD1 is, for example, the distance in the direction of extension of the map dividing line ML2 (or the direction of travel of the host vehicle M). If distance TD1 is greater than a predetermined distance (e.g., width W1 or width W1 + predetermined value α), the determination unit 142 determines that the other vehicles m1 and m2 are changing lanes. For example, assume that other vehicles m1 and m2 are traveling in the same lane (e.g., lane L1) and then make a lateral movement. If distance TD1 at this time is greater than width W1 of lane L1, it is highly likely that the other vehicles m1 and m2 are not traveling in the same lane. Therefore, the determination unit 142 can accurately determine that the other vehicles m1 and m2 are changing lanes when the above conditions are met, and can more appropriately determine the validity of the lane dividing line without making a lane change determination based on the other vehicles m1 and m2 making the lane change. The road shape determination accuracy can be improved.

[0070] The predetermined distance may be set based on the width W1 of the driving lane L1 as described above, instead of being set based on the width W1 of the driving lane L1 as described above. Figure 3 The figure shows the distance corresponding to the distance TD2 between the lateral positions of the other vehicles m1 and m2 at the same point before they move laterally based on the driving trajectories K1 and K2. For example, the determination unit 142 increases the predetermined distance as the distance TD2 between the lateral positions increases. For example, if the other vehicles m1 and m2 are traveling at different lateral positions (e.g., typically one lane apart) before the lateral movement, the predetermined distance, such as typically two lanes, is set as the predetermined distance. This allows for highly accurate determination of lane change between the other vehicles m1 and m2, even when one of the other vehicles is traveling in an adjacent lane to the lane of the other vehicle.

[0071] [Scene 2]

[0072] Figure 4 is a diagram for explaining the determination process in the second scenario. Figure 4 In the example, Figure 3 Compared to the first scene shown, the other vehicle m3 is different in that there is another vehicle m1 and m2. The other vehicle m3 is an example of a "third other vehicle". Figure 4In the example, another vehicle m3 is traveling in lane L1 at a speed Vm3 in front of the host vehicle M. In the second scenario, the first recognition unit 132 recognizes the position (relative position with respect to the host vehicle M), speed (relative speed with respect to the host vehicle M), travel direction, and travel position information (e.g., travel trajectory K3) of the other vehicle m3 in addition to the other vehicles m1 and m2.

[0073] like Figure 4 As shown, the determination unit 142 may also perform a correctness determination based on the other vehicle m3 if the first recognition unit 132 recognizes another vehicle m3, different from the other vehicles m1 and m2 that are moving laterally, in the vicinity (ahead) of the host vehicle M, and the recognized other vehicle m3 is not moving laterally in the same direction (including directions within a predetermined allowable range) as the other vehicles m1 and m2 that are moving laterally at a position at least a predetermined distance apart. In this case, the determination unit 142 determines the correctness of the dividing line based on the degree of deviation between the driving trajectory K3 of the other vehicle m3 and at least one of the camera dividing line CL and the map dividing line ML.

[0074] Since it can be predicted that the other vehicles m1 and m2 that have moved laterally at positions that are at least a predetermined distance apart are changing lanes (the driving trajectories K1 and K2 do not follow the camera dividing line CL and the map dividing line ML), the determination unit 142 may also determine that the driving trajectory K3 of the other vehicle m3 follows at least one of the camera dividing line CL and the map dividing line ML. Figure 4 In the example, since the camera dividing lines CL1 and CL2 near the other vehicle m3 cannot be recognized, the determination unit 142 determines that the driving track K3 follows the map dividing lines ML1 and ML2. In this case, the determination unit 142 may also determine that the map dividing lines ML1 and ML2 are correct.

[0075] In this way, the correctness of the dividing line can be determined more appropriately based on the relationship between the driving trajectories K1 to K3 of the other vehicles m1 to m3. Figure 4 As shown, even if there are two other vehicles m1 and m2 moving in the same direction and one other vehicle m3 traveling in a direction different from the other vehicles m1 and m2, the majority will not be determined to be correct. Even if it is the minority, the road shape can be determined with high accuracy based on the other vehicle m3.

[0076] [Scene 3]

[0077] Figure 5 This is a diagram for explaining the determination process in the third scenario. Figure 5 In the example, with Figure 3The first scenario shown differs in that an obstacle OB1, such as a construction site, exists ahead of the host vehicle M. Obstacle OB1, in addition to the construction site, can also be a parked vehicle, an accident vehicle, or other object that prevents the host vehicle M from traveling in the same lane (necessitating evasive driving). Sections containing such obstacles are examples of locations where the number of drivable lanes is reduced (reduced lane number locations).

[0078] In the third scenario, the first recognition unit 132 identifies an obstacle OB1 in front of the host vehicle M based on the recognized surrounding conditions. For example, the first recognition unit 132 may also identify obstacles OB1, such as construction sites, based on road signs or signs in front of the host vehicle M identified from camera images, and may also identify obstacles OB1, such as parked vehicles, through object detection using the detection device DD. The first recognition unit 132 designates the lane where the obstacle OB1 is located as a non-travelable lane and identifies a location within a specified range from the location of the obstacle OB1 as a location with reduced lane count. The first recognition unit 132 may also communicate with an external device via the communication device 20 and, based on the location information and travel direction of the host vehicle M, obtain information from the external device regarding the presence of an obstacle (e.g., an accident vehicle, a construction site) or a location with reduced lane count in front of the host vehicle M (in the direction of travel).

[0079] In the third scenario, if there is a lane reduction point ahead of the host vehicle M and other vehicles m1 and m2 have made lateral movements within a specified range ahead of the lane reduction point as viewed from the host vehicle M, the determination unit 142 performs a correct / incorrect decision based on the other vehicles m1 and m2. For example, if there is a lane reduction point ahead, such as a construction site, the position at which other vehicles change lanes closer to the point of reduction depends on individual drivers' preferences, resulting in variations in the positions of the lateral movements of each other vehicle. However, in this case, if correct / incorrect decisions based on the other vehicles are not made, the vehicle may enter the construction site. Therefore, as in the third scenario, if other vehicles have made lateral movements within a specified range ahead of the lane reduction point, correct / incorrect decisions based on the other vehicles m1 and m2 are performed regardless of the position of the lateral movement, thereby enabling more appropriate driving control based on the situation.

[0080] In addition to the case where the obstacle OB1 is present, the reduced lane point may be a point where the road shape has reduced lanes regardless of the presence or absence of an obstacle. In this case, the reduced lane point may be recognized by the first recognition unit 132 or by the second recognition unit 134 from map information.

[0081] [About driving control]

[0082] The execution control unit 144 determines the driving control for the host vehicle M based on the determination result of the determination unit 142 and executes the determined driving control. "Determining driving control" may include, for example, determining the content (type) of driving control and whether to execute (or suppress) the driving control. "Executing driving control" may include, for example, not only switching and executing driving control but also continuing an already executed driving control. Suppressing driving control not only means not executing (terminating) a driving control but also may include lowering the automation level of the driving control. The driving control executed by the execution control unit 144 may include ACC, TJP, LKAS, ALC, CMBS, and other driving controls, as well as various other driving controls designed to avoid contact with surrounding vehicles. The execution control unit 144 generates a target trajectory for executing the driving control and outputs the generated target trajectory to the second control unit 160.

[0083] In the first scenario, the driving control executed by the execution control unit 144 includes at least a first driving control and a second driving control. The first driving control, for example, involves executing at least a steering control of the steering or speed of the host vehicle M based on a dividing line recognized by the first recognition unit 132 or the second recognition unit 134 (e.g., a dividing line in a portion where the camera dividing line and the map dividing line do not deviate). For example, the first driving control involves driving the host vehicle M so that its representative point passes through the center of a lane defined by the dividing line. The second driving control, for example, involves executing at least a steering control of the steering or speed of the host vehicle M based on a map dividing line or driving position information of other vehicles. For example, the second driving control involves driving the host vehicle M so that its representative point follows the driving trajectory of the other vehicle m1.

[0084] Furthermore, the driving control may include a third driving control for prioritizing camera dividing lines over map dividing lines to execute at least steering control of the steering and speed of the vehicle M, and a fourth driving control for prioritizing map dividing lines over camera dividing lines to execute at least steering control of the steering and speed of the vehicle M. Prioritizing camera dividing lines over map dividing lines means, for example, that processing based on the camera dividing lines is basically performed, but if, for example, the recognition accuracy of the camera dividing lines falls below a threshold or recognition is impossible, processing based on the map dividing lines is temporarily switched. Prioritizing map dividing lines over camera dividing lines means that processing based on the map dividing lines is basically performed, but if, for example, the map dividing lines cannot be determined, processing based on the camera dividing lines is temporarily switched. The third and fourth driving controls are, for example, driving controls for situations where the camera dividing lines deviate from the map dividing lines.

[0085] Driving control may include multiple driving control levels based on automation levels (an example of the degree of automation). Automation levels include, for example, a first level, a second level with a lower degree of driving control automation than the first level, and a third level with a lower degree of driving control automation than the second level. Automation levels may also include a fourth level (an example of a fourth control level) with a lower degree of driving control automation than the third level. The automation level may be determined by standardized information, regulations, or other factors, or may be an independently set indicator value. Therefore, the type, content, and number of automation levels are not limited to the following examples. For example, a low degree of driving control automation means a low degree of automation in driving control and a high level of driver workload (heavy workload). A low level of driving control automation means a low degree of control over steering or acceleration / deceleration by the automatic driving control device 100 (a high degree of driver intervention in steering or acceleration / deceleration operations). Examples of driver workload include monitoring the surroundings of the vehicle M and operating driving control elements. Operations of driving control elements include, for example, a state where the driver is gripping the steering wheel (hereinafter referred to as a "hands-on state"). For example, tasks assigned to the driver are tasks (driver tasks) required of passengers in order to maintain automated driving of the vehicle M. Therefore, if the passenger is unable to perform the assigned tasks, the automation level is lowered. For example, the first level of driving control may include driving controls such as ACC, ALC, LKAS, and TJP. The second or third levels of driving control may include driving controls such as ACC, ALC, and LKAS. The fourth level of driving control may include manual driving. For example, driving controls such as ACC may be performed in the fourth level of driving control. Of the first to fourth levels, the first level of driving control has the highest degree of automation, and the fourth level has the lowest.

[0086] In the first level, there are no tasks assigned to the occupants (the tasks assigned to the driver are the lightest). In the second level, for example, tasks assigned to the occupants include monitoring the surroundings of the host vehicle M (particularly the front). In the third level, for example, tasks assigned to the occupants include, in addition to monitoring the surroundings of the host vehicle M, maintaining a hands-on state. In the fourth level, for example, tasks assigned to the occupants (e.g., the driver), in addition to monitoring the surroundings of the host vehicle M and maintaining a hands-on state, include controlling the steering and speed of the host vehicle M using the driving control elements 80. In other words, in the fourth level, driving can be immediately handed over to the occupants, and the tasks assigned to the driver are the heaviest. The content of driving control and the tasks assigned to the occupants in each automation level are not limited to the examples described above. The automatic driving control device 100 executes driving control at any of the first to fourth levels based on the surrounding conditions of the host vehicle M and the tasks currently being performed by the occupants. At least a portion of the first to fourth levels may also be associated with the first to fourth driving control levels described above.

[0087] For example, if the determination unit 142 determines that both the camera dividing line CL and the map dividing line ML are correct (e.g., the camera dividing line CL and the map dividing line ML do not deviate from each other), the execution control unit 144 generates a target trajectory for executing the first driving control. If one of the camera dividing line CL and the map dividing line ML is determined to be correct, the execution control unit 144 generates a target trajectory for executing any of the second through fourth driving controls based on the correct dividing line. Based on the determination result, the execution control unit 144 may also perform control such as terminating driving control of the host vehicle M and switching to manual driving by the occupant. Furthermore, the execution control unit 144 may also switch the automation level corresponding to the driving control based on the determination result. In this case, for example, if the camera dividing line CL and the map dividing line ML are determined to be correct, the first level of driving control is executed. If they are determined to be incorrect, the second through fourth levels of driving control are executed depending on the situation.

[0088] [Processing Flow]

[0089] Hereinafter, the processing executed by the automatic driving control device 100 according to the embodiment will be described. Figure 6 This is a flowchart showing an example of the processing performed by the automatic driving control device 100 of the embodiment. The following description will focus on the correctness determination process of at least one of the camera dividing line CL and the map dividing line ML in the processing performed by the automatic driving control device 100. The automatic driving control device 100 performs the following processing according to the example of the automatic driving control device 100. Figure 6The driving control of the host vehicle M is executed based on the result of the determination process shown. The process shown below may be repeatedly executed at a predetermined timing or at a predetermined cycle, or may be repeatedly executed while the automatic driving control device 100 is executing the automatic driving.

[0090] exist Figure 6 In the example, the first recognition unit 132 identifies a demarcation line (camera demarcation line) around the vehicle M based on the output of the detection device DD that detects the surrounding conditions of the vehicle M (step S100). Next, the first recognition unit 132 identifies other vehicles around the vehicle M (step S110). Next, the second recognition unit 134 references map information based on the location information of the vehicle M and identifies a demarcation line (map demarcation line) around the vehicle M from the map information (step S120).

[0091] Next, the determination unit 142 determines whether the first and second other vehicles are present in front of the host vehicle M (step S130). If the determination unit 142 determines that the first and second other vehicles are present, the determination unit 142 determines whether the first and second other vehicles have moved laterally (step S140). The determination unit 142 may determine whether the first and second other vehicles are present in step S130, and if the determination unit 142 determines that the first and second other vehicles are present, the determination unit 142 may determine whether the first and second other vehicles have moved laterally in front of the host vehicle M in step S140.

[0092] If lateral movement is determined, the determination unit 142 determines whether the positions of the first and second other vehicles after their respective lateral movements differ by a predetermined distance or more (step S150). If the difference is determined to be greater than the predetermined distance, the determination unit 142 does not determine the correctness of the dividing line (at least one of the camera dividing line and the map dividing line) based on the first and second other vehicles (step S160). In this case, the determination unit 142 may determine the correctness of the dividing line based on the travel trajectory of another vehicle (e.g., a third other vehicle) that does not move laterally, or may determine that both the camera dividing line and the map dividing line are incorrect.

[0093] If, in step S130, the first and second other vehicles are not located ahead of the host vehicle M, if it is determined in step S140 that the first and second other vehicles have not moved laterally, or if it is determined in step S150 that their positions after lateral movement are not different by more than a predetermined distance, the correctness of the dividing line is determined based on the dividing line (at least one of the camera dividing line and the map dividing line) and the driving trajectory of the other vehicle (step S170). For example, if, in step S150, the positions after lateral movement are less than the predetermined distance, the determination unit 142 determines whether the driving trajectories of the first and second other vehicles deviate from the map dividing line. If the degree of deviation is greater than a threshold, the map dividing line is determined to be incorrect. The process of this flowchart then terminates.

[0094] [Variation]

[0095] In an embodiment, the determination unit 142 may determine that the first and second other vehicles are performing a lane change when, for example, the first and second other vehicles identified by the first recognition unit 132 have moved laterally in front of the host vehicle M, and their positions after the lateral movement differ by a predetermined distance or more. The determination unit 142 may determine that the map dividing line is correct when it is determined that a lane change is being performed.

[0096] According to the above-described embodiment, the determination device (recognition unit 130, determination unit 142) includes: a first recognition unit 132 for recognizing the surrounding conditions of the host vehicle M, including a camera dividing line (first dividing line) that divides the lane of the host vehicle M and other vehicles present in the surroundings of the host vehicle M, based on the output of a detection device DD that detects the surrounding conditions of the host vehicle M; a second recognition unit 134 for recognizing the camera dividing line (second dividing line) that divides the lane of the surroundings of the host vehicle M from map information based on the position information of the host vehicle M; and a determination unit 142 for performing an accuracy determination on whether at least one of the camera dividing line and the map dividing line is correct based on at least one of the camera dividing line and the travel trajectories of other vehicles. If the first other vehicle and the second other vehicle identified by the first recognition unit 132 have moved laterally in front of the host vehicle M and their positions after the lateral movement differ by a predetermined distance or more, the determination unit 142 does not perform an accuracy determination based on the first other vehicle and the second other vehicle. This allows for more appropriate determination of the accuracy of the dividing line based on the surroundings of the host vehicle and the travel conditions of the other vehicles. According to the embodiment, more appropriate driving control can be executed based on the determination result, thereby improving the sustainability of driving control and contributing to the development of a sustainable transportation system.

[0097] According to the embodiments, for example, if a first other vehicle and a second other vehicle cross a map dividing line at the same location (when the position after lateral movement is less than a predetermined distance), the other vehicles can be used to determine the correctness of the map dividing line, thereby determining that the map dividing line is incorrect. According to the embodiments, for example, if a first other vehicle and a second other vehicle cross a map dividing line at different locations in the direction of travel (when the position after lateral movement is more than a predetermined distance), it can be determined that the first and second other vehicles are changing lanes, and the other vehicles are not used for determining the correctness of the map dividing line, thereby achieving more accurate road shape determination. According to the embodiments, for example, if there are three other vehicles, two of which are making lateral movements and one is not (for example, when traveling straight), the correctness determination is made based on the situation of a single vehicle in the minority, rather than a majority decision. This allows for more appropriate correctness determination of the dividing line according to the situation.

[0098] The above-described embodiment can be expressed as follows.

[0099] A determination device, wherein:

[0100] The determination device comprises:

[0101] a storage medium storing computer-readable instructions; and

[0102] a processor connected to the storage medium,

[0103] The processor performs the following processing by executing the computer-readable instructions:

[0104] identifying, based on an output of a detection device that detects a surrounding condition of the host vehicle, a surrounding condition including a first dividing line that divides a lane in which the host vehicle is traveling and other vehicles that are present around the host vehicle;

[0105] identifying, from map information based on the position information of the host vehicle, a second dividing line that divides lanes around the host vehicle;

[0106] determining whether at least one of the first dividing line and the second dividing line is correct based on at least one of the first dividing line and the second dividing line and the driving trajectory of the other vehicle; and

[0107] When the identified first and second other vehicles have moved laterally in front of the host vehicle and their positions after the lateral movement differ by a predetermined distance or more, the correctness determination is not performed based on the first and second other vehicles.

[0108] While specific embodiments of the present invention have been described above, the present invention is not limited to these embodiments at all, and various modifications and substitutions can be made without departing from the spirit of the present invention.

Claims

1. A determination device, wherein: The determination device comprises: a first recognition unit for recognizing a surrounding condition including a first dividing line that divides a lane of the host vehicle and other vehicles that are present around the host vehicle based on an output of a detection device that detects a surrounding condition of the host vehicle; a second recognition unit that recognizes a second dividing line that divides a lane around the host vehicle from map information based on the position information of the host vehicle; as well as a determination unit that determines whether at least one of the first dividing line and the second dividing line is correct based on at least one of the first dividing line and the second dividing line and the driving trajectory of the other vehicle; The determination unit does not perform the correctness determination based on the first other vehicle and the second other vehicle identified by the first recognition unit when the first other vehicle and the second other vehicle identified by the first recognition unit have moved laterally in front of the vehicle and their positions after the lateral movement differ by more than a predetermined distance.

2. The determination device according to claim 1, wherein: The prescribed distance is set based on the width of the lane divided by the first dividing line or the width of the lane divided by the second dividing line.

3. The determination device according to claim 1, wherein: The predetermined distance is set based on the distance between the lateral positions of the first other vehicle and the second other vehicle before the lateral movement.

4. The determination device according to claim 1, wherein: The determination unit makes the correctness determination based on the third other vehicle when the first identification unit identifies a third other vehicle that is different from the first other vehicle and the second other vehicle, and the third other vehicle does not move laterally in the same direction as the first other vehicle and the second other vehicle that have moved laterally at positions that are more than a specified distance apart.

5. The determination device according to claim 1, wherein: The determination unit makes the correctness determination based on the first other vehicle and the second other vehicle when there is a location in front of the vehicle where the number of lanes in which the vehicle can travel is reduced and the first other vehicle and the second other vehicle have moved laterally within a specified range in front of the location.

6. A determination method, wherein: The determination method enables the computer to perform the following processing: identifying, based on an output of a detection device that detects a surrounding condition of the host vehicle, a surrounding condition including a first dividing line that divides a lane in which the host vehicle is traveling and other vehicles that are present around the host vehicle; identifying, from map information based on the position information of the host vehicle, a second dividing line that divides lanes around the host vehicle; determining whether at least one of the first dividing line and the second dividing line is correct based on at least one of the first dividing line and the second dividing line and the driving trajectory of the other vehicle; as well as When the identified first and second other vehicles have moved laterally in front of the host vehicle and their positions after the lateral movement differ by a predetermined distance or more, the correctness determination is not performed based on the first and second other vehicles.

7. A storage medium storing a program, wherein: The program causes the computer to perform the following processing: identifying, based on an output of a detection device that detects a surrounding condition of the host vehicle, a surrounding condition including a first dividing line that divides a lane in which the host vehicle is traveling and other vehicles that are present around the host vehicle; identifying, from map information based on the position information of the host vehicle, a second dividing line that divides lanes around the host vehicle; determining whether at least one of the first dividing line and the second dividing line is correct based on at least one of the first dividing line and the second dividing line and the driving trajectory of the other vehicle; as well as When the identified first and second other vehicles have moved laterally in front of the host vehicle and their positions after the lateral movement differ by a predetermined distance or more, the correctness determination is not performed based on the first and second other vehicles.

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