Determination device, determination method, and storage medium

Abstract

Provided are a determination device, a determination method, and a storage medium that appropriately determine whether or not a division line is correct. The present invention is provided with: a first recognition unit that recognizes, on the basis of the output of a detection device that detects the surroundings of a host vehicle, the surroundings of other vehicles that exist in the surroundings of the host vehicle and a first division line that divides the travel lane of the host 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; a determination unit that determines whether or not at least one of the first division line and the second division line is correct on the basis of the traveling trajectory of the other vehicle and at least one of the first division line and the second division line; the determination unit does not perform correctness determination on the basis of the travel trajectory of the first other vehicle when there are a first other vehicle that moves laterally forward of the host vehicle and a second other vehicle that does not move laterally forward of the first other vehicle among the plurality of other vehicles recognized by the first recognition unit.

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 systems that take into account vulnerable traffic participants have become increasingly active. To achieve this, research and development efforts related to autonomous driving technologies are being pursued to further improve traffic safety and convenience. In this regard, a technology is known that includes a first sensor unit that captures or measures a dividing line in the vehicle's direction of travel and a second sensor unit that detects the movement of the preceding vehicle. The technology controls a driving actuator based on the positional information of the dividing line obtained by the first sensor unit and the reliability of that information (e.g., Japanese Patent Application Laid-Open No. 2022-39469). Summary of the Invention

[0003] However, in previous autonomous driving technologies, there was the following problem: when using the identified dividing lines and the driving trajectories of other vehicles to determine whether the dividing lines are correct, if some of the other vehicles among multiple other vehicles change lanes, it may not be possible to properly determine whether the dividing lines are correct.

[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 line surrounding the vehicle and the 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 of the present invention employ the following structures.

[0006] (1): A determination device according to one embodiment of the present invention comprises: a first recognition unit for recognizing the surrounding conditions including a first dividing line dividing the lane of the vehicle and other vehicles existing around the vehicle based on the output of a detection device that detects the surrounding conditions of the vehicle; a second recognition unit for recognizing the second dividing line dividing the lane of the vehicle from map information based on the 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. If a first other vehicle that is moving laterally ahead of the vehicle and a second other vehicle that is not moving laterally ahead of the first other vehicle are present among the plurality of other vehicles identified by the first recognition unit, the determination unit does not perform the determination based on the driving trajectory of the first other vehicle.

[0007] (2): Based on the solution of (1) above, when the second other vehicle is located at the same position as the destination of the first other vehicle's lateral movement or the lateral position before the first other vehicle's lateral movement and ahead of the first other vehicle, the determination unit does not perform the correctness determination based on the driving trajectory of the first other vehicle.

[0008] (3): In the embodiment of (1), the determination unit determines that the dividing line along the travel trajectory of the second other vehicle among the first dividing line and the second dividing line is the correct dividing line.

[0009] (4): In the embodiment of (1), the determination unit determines that the dividing line of the first dividing line and the second dividing line whose deviation angle with respect to the traveling direction of the second other vehicle is less than a threshold value is a correct dividing line.

[0010] (5): Based on the solution of (2) above, when there is a third other vehicle at a position behind the second other vehicle that has undergone a lateral movement or whose lateral position before the lateral movement is different from the lateral position of the second other vehicle, the determination unit does not perform the correctness determination based on the driving trajectory of the first other vehicle and the driving trajectory of the third other vehicle.

[0011] (6): Based on the solution of (5) above, the third other vehicle is a vehicle whose destination for lateral movement or whose lateral position before lateral movement is the lateral position before the first other vehicle lateral movement.

[0012] (7): Based on the solution of (5) above, the third other vehicle is a vehicle that moves laterally in the same direction as the first other vehicle.

[0013] (8): In the embodiment of (1), the determination unit performs the correctness determination in an area of ​​a predetermined width in the vehicle width direction based on the position of the second other vehicle.

[0014] (9): Based on the solution of (1) above, when the driving trajectory of the second other vehicle only obtains a driving trajectory farther away from the point where the lateral movement of the first other vehicle that is moving laterally in front of the own vehicle is completed, the determination unit makes the correctness determination based on the driving trajectory of the first other vehicle and the driving trajectory of the second other vehicle.

[0015] (10): A determination method according to one embodiment of the present invention enables 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 lane of the vehicle and other vehicles existing in the surroundings of the vehicle; based on the position information of the vehicle, identify a second dividing line that divides the lane of 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 there is a first other vehicle that moves laterally at a position ahead of the vehicle and a second other vehicle that does not move laterally at a position ahead of the first other vehicle among the identified multiple other vehicles, the correctness determination based on the driving trajectory of the first other vehicle is not performed.

[0016] (11): A storage medium of 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 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 of 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 there is a first other vehicle that moves laterally at a position ahead of the vehicle and a second other vehicle that does not move laterally at a position ahead of the first other vehicle among the multiple identified other vehicles, not making the correctness judgment based on the driving trajectory of the first other vehicle.

[0017] According to the above-mentioned aspects (1) to (11), the correctness of the dividing lines can be determined more appropriately based on the dividing lines around the own vehicle and the conditions of other vehicles. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Figure 1 It is a structural diagram of a vehicle system including the vehicle control device according to the embodiment.

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

[0020] Figure 3 This is a diagram for explaining the determination process of the host vehicle in the first scenario.

[0021] Figure 4 This is a diagram for explaining the determination process of the host vehicle in the second scenario.

[0022] Figure 5 This is a diagram for explaining the determination process of the host vehicle in the third scenario.

[0023] Figure 6 This is a diagram for explaining the determination process of the host vehicle in the fourth scenario.

[0024] Figure 7 This is a flowchart showing an example of processing executed by the automatic driving control device according to the embodiment. DETAILED DESCRIPTION

[0025] 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 vehicle's lane is the correct one (or lane) will be described below. Autonomous driving refers to, for example, automatically controlling one or both of a vehicle's steering and speed to perform driving control. Examples of such driving control include ACC (Adaptive Cruise Control System), TJP (Traffic Jam Pilot), LKAS (Lane Keeping Assistance System), ALC (Automated Lane Change), and CMBS (Collision Mitigation Brake System). Autonomous vehicles can 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. However, when right-hand traffic regulations apply, the left and right terms can be reversed.

[0026] [Overall structure]

[0027] 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, and 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 storage battery (battery) such as a secondary battery or a fuel cell.

[0028] 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 1The 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."

[0029] 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 on any part of the vehicle M equipped with the vehicle system 1. When photographing the front, the camera 10 is mounted on the upper part of the 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 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.

[0030] The radar device 12 radiates radio waves, such as millimeter waves, around the 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 can be mounted anywhere on the vehicle M. The radar device 12 can also detect the position and velocity of objects using the FM-CW (Frequency Modulated Continuous Wave) method.

[0031] The LIDAR 14 irradiates light around the vehicle M and measures the scattered light. The LIDAR 14 detects the distance to an object based on the time between light emission and light reception. The irradiated light is, for example, pulsed laser light. The LIDAR 14 is mounted at any location on the vehicle M.

[0032] The object recognition device 16 performs sensor fusion processing on some or all of the detection results from the camera 10, the radar device 12, and the LIDAR 14 to identify the position, type, speed, and other aspects of an object. The object recognition device 16 outputs the recognition results to the automatic driving control device 100. The object recognition device 16 can directly output the detection results from the camera 10, the radar device 12, and the LIDAR 14 to the automatic driving control device 100. In this case, the object recognition device 16 can be omitted from the configuration of the vehicle system 1 (detection device DD).

[0033] 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.

[0034] 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.

[0035] 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 / latitude information) 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 within a predetermined time period from the position sensor. The detection results of the vehicle sensors 40 are output to the automatic driving control device 100.

[0036] 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 share 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 mapped 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 represents the road shape by, for example, displaying road links and the nodes connecting 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 the 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.

[0037] 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 to travel in from the left. If the route on the map branches, the recommended lane determination unit 61 determines the recommended lane so that the host vehicle M can travel on a reasonable route to the branch destination.

[0038] 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, or 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). Structures include, for example, guardrails, curbs, medians, and fences. Impassable road surfaces 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 also include road shape information, traffic restriction information, address information (address / zip code), facility information, parking information, and telephone number information. Road shape information includes, for example, road curvature (which may be replaced by curvature radius, the same applies hereinafter), 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 .

[0039] 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 wheel, a joystick, and other operating parts. Each operating part of the driving operating parts 80 is equipped with, for example, 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, and one or both of the driving force output device 200, the braking device 210, and the steering device 220.

[0040] 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 180 are each implemented by a hardware processor, such as a CPU (Central Processing Unit), executing a program (software). Some or all of these components can 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.

[0041] The storage unit 190 can also 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 can also store map information (e.g., the first map information 54 and the second map information 62).

[0042] 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 "intersection recognition" function can be achieved by performing, in parallel, intersection recognition based on deep learning and other methods and recognition based on pre-given conditions (the presence of signals, road signs, etc. that can be pattern matched), and comprehensively evaluating both by scoring them. In this way, the reliability of autonomous driving can be ensured. The first control unit 120 performs control related to autonomous driving of the vehicle M based on instructions from, for example, the MPU 60, the HMI control unit 180, etc.

[0043] 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). Objects include, for example, other vehicles (surrounding vehicles), road users (pedestrians, bicycles, etc.), road structures, and other surrounding obstacles. Examples of road structures include road signs, traffic signals, intersections, curbs, medians, guardrails, and fences. The position of an object is identified, for example, 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, for control purposes. The position of an object can be represented by a representative point such as the object's center of gravity or a corner, or by a displayed area. For example, when the object is a moving body such as another vehicle, the so-called "state" of the object may also include the acceleration, jerk, or "action state" of the moving body (for example, whether the other vehicle is changing lanes or is about to change lanes).

[0044] 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.

[0045] The action plan generation unit 140 generates an action plan for autonomously driving 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 (independent of driver control) travel in the future, in a manner that allows the host vehicle M to travel in the recommended lane determined by the recommended lane determination unit 61 and, based on the recognition results from the recognition unit 130 or the surrounding road shape based on the current position of the host vehicle M obtained from map information, adapts to the surrounding conditions of the host vehicle M. 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 predetermined travel distances (e.g., a few meters) along the route. Separately, 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 may be locations that the host vehicle M should reach at the specified sampling times. In this case, information on the target speed and target acceleration is expressed by the intervals between track points.

[0046] 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 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 closer to the destination at a road divergence point; a merging event that causes the host vehicle M to merge onto a 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 steer to avoid an obstacle ahead of the host vehicle M.

[0047] 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 while the vehicle M is traveling. 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.

[0048] The action plan generation unit 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."

[0049] The second control unit 160 controls the 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.

[0050] 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 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 factor associated with the target trajectory stored in the memory. The steering control unit 166 controls the steering device 220 based on the degree of 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, by a combination of feedforward control and feedback control. As an example, the steering control unit 166 performs a combination of feedforward control corresponding to the curvature of the road ahead of the vehicle M and feedback control based on deviation from the target trajectory.

[0051] return Figure 1 The HMI control unit 180 notifies the occupant of specified information via the HMI 30. The specified information includes, for example, 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. Information related to the status of the host vehicle M includes, for example, the speed, engine speed, and shift position of the host vehicle M. Information related to driving control includes, for example, whether driving control based on autonomous driving is being executed, information inquiring whether to start autonomous driving, information related to the status of driving control based on autonomous driving, information related to the automation level, and information urging the occupant to drive when switching from autonomous driving to manual driving. The specified information may 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). For example, the specified information may 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 the information received by the HMI 30 to the communication device 20, the navigation device 50, the first control unit 120, and other devices.

[0052] The HMI control unit 180 may also output inquiry information to the occupant, processing results performed by the first control unit 120 and the second control unit 160, etc. to the HMI 30. The HMI control unit 180 may also transmit various information output to the HMI 30 to a terminal device used by a user of the vehicle M via the communication device 20.

[0053] The driving force output device 200 outputs the driving force (torque) for vehicle propulsion to the drive wheels. The driving force output device 200 includes, 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 above components based on information input from the second control unit 160 or from the accelerator pedal of the driving control element 80.

[0054] The brake 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 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 member 80, and outputs a braking torque corresponding to the braking operation to each wheel. The brake 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 brake device 210 is not limited to the structure described above, and may also be an electronically controlled hydraulic brake device that controls the actuator based on information input from the second control unit 160 and transmits the hydraulic pressure of the master hydraulic cylinder to the hydraulic cylinder.

[0055] The steering system 220 includes, for example, a steering ECU and an electric motor. The electric motor applies force to, for example, a rack-and-pinion mechanism to change the direction of the steering wheel. The steering ECU drives the electric motor to change the direction of the steering wheel based on information input from the second control unit 160 or information input from the steering wheel of the driving control unit 80.

[0056] [Identification Department and Action Plan Generation Department]

[0057] 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, breaking down the determination process into several scenarios.

[0058] [Scene 1]

[0059] Figure 3 1 is a diagram for explaining the determination process of the host vehicle M in the first scenario. Figure 3In the example shown, dividing lines CL1 to CL4 identified by the detection device DD and dividing lines ML1 to ML4 obtained from map information (for example, 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, lane L2 is divided by dividing lines ML2 and ML3, and lane L3 is divided by dividing lines ML3 and ML4. Lanes L1 to L3 are lanes that can travel in the same direction (X-axis direction in the figure). Figure 3 In the example of , the dividing lines CL1 to CL4 are examples of “first dividing lines”, and the dividing lines ML1 to ML4 are examples of “second dividing lines”. Figure 3 In the example, the vehicle M is traveling in the lane L2 at a speed VM, another vehicle m1 (preceding vehicle) in front of the vehicle M is traveling at a speed Vm1, and another vehicle m2 (further preceding vehicle) traveling ahead of the vehicle m1 is traveling in the lane L1 at a speed Vm2. Figure 3 The illustrated point P1 is a point where the recognition accuracy becomes lower than a threshold value or the camera dividing lines CL1 to CL4 cannot be recognized due to reasons such as weather (in other words, the recognizable range of the camera dividing lines). Figure 3 The illustrated other vehicle m1 is an example of a “first other vehicle”, and the illustrated other vehicle m2 is an example of a “second other vehicle”.

[0060] 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. For example, the first recognition unit 132 recognizes the left and right dividing lines CL2 and CL3 that divide the driving lane (lane L2) of the vehicle M based on the image captured by the camera 10 (hereinafter, camera image). The first recognition unit 132 recognizes the dividing lines CL1 and CL2 that divide the adjacent lane L1 adjacent to the driving lane and the dividing lines CL3 and CL4 that divide the adjacent lane L3. Hereinafter, the dividing lines CL1 to CL4 are sometimes referred to as "camera dividing lines CL1 to CL4". 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 to CL4 in the image plane. The first recognition unit 132 converts the positions of the camera dividing lines CL1 to CL4 based on the position of the representative point of the vehicle M into the vehicle coordinate system (for example, Figure 3XY plane coordinates). The first recognition unit 132 may also recognize the curvature of the camera dividing lines CL1 to CL4, for example. The first recognition unit 132 may also recognize the curvature or curvature change of each camera dividing line CL1 to CL4. The curvature change refers to, for example, the time rate of change of the curvature at the front x [m] of the camera dividing lines CL1 to CL4 recognized by the camera 10 when viewed from the vehicle M. The first recognition unit 132 may also recognize the curvature or curvature change of the lane divided by the camera dividing lines CL1 to CL4 by averaging the curvature or curvature change of each camera dividing line CL1 to CL4. The camera dividing lines CL1 to CL4 may be recognized or corrected based on the output of a detection device other than the camera 10.

[0061] The first recognition unit 132 recognizes other vehicles that are present around the host vehicle M (within a predetermined distance). Figure 3 In the example, the first recognition unit 132 identifies another vehicle m1 traveling ahead of the host vehicle M and another vehicle m2 traveling ahead of the host vehicle M as viewed from the host vehicle M, based on the output of the detection device DD that detects the surrounding conditions of the host vehicle M. The first recognition unit 132 recognizes the position (relative position with respect to the host vehicle M) and speed (relative speed with respect to the host vehicle M) of each of the other vehicles m1 and m2, or identifies the lanes in which the other vehicles m1 and m2 are traveling. The first recognition unit 132 may also recognize the driving position information of the other vehicles m1 and m2. The driving position information refers to, for example, the driving trajectories K1 and K2 based on the positions of representative points of each of the other vehicles m1 and m2 during travel within a predetermined time period.

[0062] The second recognition unit 134 recognizes lane dividing lines around the vehicle M (within a predetermined distance) from the map information based on the position of the vehicle M detected by the vehicle sensor 40 and the GNSS receiver 51, for example. Figure 3 In the example of FIG, the second recognition unit 134 refers to the map information based on the position information of the host vehicle M and recognizes the dividing lines ML1 to ML4 that exist in the direction in which the host vehicle M is traveling or in which the host vehicle M can travel. Hereinafter, the dividing lines ML1 to ML4 may be referred to as "map dividing lines ML1 to ML4."

[0063] The second recognition unit 134 may also recognize map dividing lines ML2 and ML3 among the recognized map dividing lines ML1 to ML4 as dividing lines that divide the lane L2 in which the host vehicle M is traveling. The second recognition unit 134 may recognize the curvature or curvature change of each of the map dividing lines ML1 to ML4 from the second map information 62. The second recognition unit 134 may also average the curvature or curvature change of each of the map dividing lines ML1 to ML4 to recognize the curvature or curvature change of each of the lanes L1 to L3 divided by the map dividing lines.

[0064] The determination unit 142 determines whether at least one of the camera dividing line and the map dividing line is correct, for example, based on the travel trajectories 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 controls driving control to terminate (or not initiate) if both dividing lines are determined to be incorrect.

[0065] To determine if the camera dividing lines CL1-CL4 identified by the first recognition unit 132 deviate from the map dividing lines ML1-ML4 identified by the second recognition unit 134. For example, the determination unit 142 derives the degree of deviation between the nearest left dividing lines CL2 and ML2 as viewed from the host vehicle M; the nearest right dividing lines CL3 and ML3 as viewed from the host vehicle M; and the degree of deviation between the adjacent lane dividing lines CL1 and ML1, and between the adjacent lane dividing lines CL4 and ML4. The determination unit 142 then determines that the camera dividing lines and the map dividing lines deviate if the derived degree of deviation is greater than a threshold; otherwise, it determines that the camera dividing lines and the map dividing lines do not deviate. This deviation determination is repeated at a predetermined timing or cycle.

[0066] For example, the determination unit 142 overlaps the camera dividing lines CL1 to CL4 and the map dividing lines ML1 to ML4 in the plane of the vehicle coordinate system (XY plane) with the position of the representative point of the vehicle M as a reference. Furthermore, when the determination unit 142 determines the deviation of the dividing lines of the comparison object (dividing lines CL1 and ML1, dividing lines CL2 and ML2, dividing lines CL3 and ML3, dividing lines CL4 and ML4), it 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 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 deviation can be determined by using the average value of the lateral position offset D1 between the dividing line CL1 and ML1, the lateral position offset D2 between the dividing line CL2 and ML2, the lateral position offset D3 between the dividing line CL3 and ML3, and the lateral position offset D4 between the dividing line CL4 and ML4, or by using the maximum or minimum value of the offsets D1 to D4.

[0067] The degree of deviation can also replace the above-mentioned offset of the lateral position (or be based on it), for example, and is the degree (size) of the angle formed by the two dividing lines of the comparison object. Figure 3In the example, the average value of the angle θ1 formed by the dividing lines CL1 and ML1, the angle θ2 formed by the dividing lines CL2 and ML2, the angle θ3 formed by the dividing lines CL3 and ML3, and the angle θ4 formed by the dividing lines CL4 and ML4 can be used, or the maximum or minimum value of the angles θ1 to θ4 can be used.

[0068] The degree of deviation can also be determined by the degree (magnitude) of the difference in curvature variation between the dividing lines, instead of (or in addition to) the aforementioned lateral position offset or the angle formed by the dividing lines. The curvature variation is primarily used when the lane curves. Determination unit 142 can use the average of the differences in curvature variation between dividing lines CL1 and ML1, the difference between dividing lines CL2 and ML2, the difference between dividing lines CL3 and ML3, and the difference between dividing lines CL4 and ML4, or the maximum or minimum of these differences. Determination unit 142 can also use the difference between the average of the curvature variation between dividing lines CL1 to CL4 and the average of the curvature variation between dividing lines ML1 to ML4. Alternatively, the difference between the curvature variation of lanes (lanes L1 to L3) identified from camera images and the curvature variation of lanes identified from map information can be used.

[0069] For example, if the recognition accuracy of camera dividing lines CL1-CL4 identified by the first recognition unit 132 falls below a threshold or the camera dividing lines become unrecognizable, the determination unit 142 may derive the degree of deviation using the angles formed between the driving trajectories K1 and K2 of other vehicles traveling nearby and the map dividing lines ML1-ML4. The determination unit 142 may also set an imaginary dividing line parallel to the driving trajectory K1 and determine the degree of deviation between the set imaginary dividing line and the map dividing line. The determination unit 142 may also perform the deviation determination from the dividing line using the driving trajectories K1 and K2, regardless of the recognition results of the camera dividing lines CL1-CL4.

[0070] If the deviation determination using the above-described degree of deviation determines that the camera dividing line and the map dividing line are not deviating, the determination unit 142 determines that the camera dividing line and the map dividing line are correct dividing lines in the correctness determination. If the camera dividing line and the map dividing line are deviating, the determination unit 142 determines that at least one of the camera dividing line and the map dividing line is incorrect. For example, if the camera dividing line and the map dividing line are deviating and the host vehicle M is performing driving control to avoid a forward obstacle, the determination unit 142 may determine that the camera dividing line is incorrect (or the map dividing line is correct). Alternatively, the determination unit 142 may determine that the map dividing line is correct if the degree of deviation between the driving trajectory of the preceding vehicle ahead of the host vehicle M (or an imaginary dividing line using the driving trajectory) and the map dividing line is less than a threshold.

[0071] The determination unit 142 determines that the map dividing line is incorrect (or the camera dividing line is correct) if the camera dividing line deviates from the map dividing line and a predetermined number or more of the recognized driving trajectories of multiple other vehicles follow the camera dividing line (including a predetermined tolerance range). If the first recognition unit 132 recognizes information indicating a lane change (e.g., an increase or decrease in lanes), such as a road construction billboard or a road sign indicating lane addition or deletion, but no lane change information is present in the road information obtained from the map information, the determination unit 142 may determine that the map dividing line is incorrect (or the camera dividing line is correct) due to outdated map information (or information inconsistent with the current road shape). For example, the determination unit 142 may determine that both the camera dividing line and the map dividing line are incorrect if the degree of deviation is greater than an upper limit value greater than a threshold, or if the camera dividing line and the map dividing line have different numbers of dividing lines.

[0072] Here, in a situation where the determination unit 142 uses the driving trajectories of other vehicles to determine the correctness of the dividing line (or to determine the deviation from the dividing line), when the first recognition unit 132 recognizes multiple other vehicles, if there is a first other vehicle that is moving laterally ahead of the host vehicle M and a second other vehicle that is not moving laterally ahead of the first other vehicle, the correctness determination (deviation determination) based on the driving trajectory of the first other vehicle is not performed. For example, lateral movement refers to movement of the map dividing line in the road width direction by more than a predetermined distance ( Figure 3 Lateral movement may also be movement in a direction perpendicular to the travel direction of the host vehicle M (in other words, in the vehicle width direction of the host vehicle M) by a predetermined distance or more. The predetermined distance may be, for example, the width of one lane corresponding to the lane change, or may be a variable distance or a fixed distance depending on the road shape.

[0073] exist Figure 3 In the example, the determination unit 142 determines that the other vehicle m1 that is located ahead of the vehicle M is moving laterally, and determines that the other vehicle m2 that is located ahead of the other vehicle m1 is not moving laterally, based on the driving trajectories K1 and K2 of the other vehicles m1 and m2 identified by the first recognition unit 132. Then, the determination unit 142 does not make a correctness determination based on the driving trajectory K1 of the other vehicle m1. In this case, the determination unit 142 may also make a correctness determination based on the driving trajectories of multiple other vehicles other than the other vehicle m1, or make a correctness determination based on the driving trajectories of some of the other vehicles other than the first other vehicle. Figure 3In the example of , the correctness determination is performed based on the travel trajectory K2 of the other vehicle m2 other than the other vehicle m1.

[0074] For example, the determination unit 142 determines that a dividing line (at least one of the camera dividing line CL and the map dividing line ML) that follows the driving trajectory K2 of the other vehicle m2 is a correct dividing line. A dividing line that follows the driving trajectory K2 is a dividing line that has been determined to be non-deviated from the driving trajectory K2 through deviation determination. The determination unit 142 may also determine as a correct dividing line a dividing line whose deviation angle (the angle formed by the driving direction of the other vehicle m2 and the extending direction of the dividing line) relative to the driving direction of the other vehicle m2 (or the longitudinal direction of the other vehicle m2) of the camera dividing line CL or the map dividing line ML is less than a threshold. This improves the accuracy of the correctness determination (road shape determination) using the dividing line of the other vehicle based on the driving trajectory K2 of the other vehicle m2.

[0075] exist Figure 3 In the example, for example, the determination unit 142 determines whether the driving trajectory K2 deviates from the camera dividing line CL and the map dividing line ML, and determines the accuracy of the deviation determination result based on the deviation determination result, from the current position of the host vehicle M to a point P1 a predetermined distance ahead (the recognizable range of the camera dividing line). The determination unit 142 determines whether the driving trajectory K2 deviates from the map dividing line ML, and determines the accuracy of the deviation determination result based on the deviation determination result, in a section farther from the point P1 when viewed from the host vehicle M (the section where the driving trajectory K2 exists).

[0076] The determination unit 142 may also perform deviation determination and correctness determination on the dividing line included in the area of ​​a predetermined width to the left and right of the position where the other vehicle m2 exists along the vehicle width direction of the other vehicle m2. Figure 3 In the example, the determination unit 142 sets a line AL1 extending longitudinally from the other vehicle m2 at a predetermined distance DL to the left in the vehicle width direction, and a line AL2 extending longitudinally from the other vehicle m2 at a predetermined distance DR to the right in the vehicle width direction. Furthermore, the determination unit 142 uses the camera dividing lines CL1 and CL2 and the map dividing lines ML1 and ML2, contained within the area demarcated by the lines AL1 and AL2, as targets for deviation and accuracy determination. The predetermined distances DL and DR can each be a fixed distance, such as one lane (or the sum of DL and DR equals one lane), or a variable distance depending on the road shape and the distance between the host vehicle M and the other vehicle m2. It is assumed that accurate determination can be made at least from the other vehicle m2 to the specified area. Therefore, by limiting the range, the accuracy of the accuracy determination using the road shape (dividing lines) of the other vehicle can be improved.

[0077] For example, if another vehicle m2, not moving laterally, is located ahead of another vehicle m1 that is moving laterally when viewed from the host vehicle M, the lateral movement of the other vehicle m1, located closer to the host vehicle m2, is likely to be a lane change. Therefore, by not determining the correctness of the driving trajectory K1 based on the other vehicle m1 (or not determining that the driving trajectory K1 is correct), the accuracy of the road shape determination can be improved. This allows for highly accurate road shape determination based on the other vehicle m2.

[0078] The determination unit 142 may also add further conditions to the relationship between the other vehicle m1 (the first other vehicle) and the other vehicle m2 (the second other vehicle). For example, if the other vehicle m2 exists at the same lateral position (including a predetermined tolerance) as the destination of the lateral movement of the other vehicle m1, and the other vehicle m2 does not move laterally but exists in front of the other vehicle m1, the determination unit 142 does not make a correctness determination based on the travel trajectory K1 of the other vehicle m1. The determination of whether the other vehicle m2 exists at the same lateral position as the destination of the lateral movement of the other vehicle m1, for example, Figure 3 As shown, the lateral (Y-axis direction in the figure) offset W1 of the driving trajectories K1 and K2 is compared. If the offset W1 is less than a threshold, it is determined that they are in the same position. If the offset W1 is greater than the threshold, it is determined that they are not in the same position.

[0079] [Scene 2]

[0080] Instead of referring to the "destination where the other vehicle m1 has moved laterally," the determination unit 142 may also consider the case where the other vehicle m2 is located at the same lateral position as "before the lateral movement." This will be described below as a second scenario, focusing on the differences from the first scenario.

[0081] Figure 4 is a diagram for explaining the determination process of the host vehicle M in the second scenario. Figure 4 In the example, with Figure 3 Compared with the first scenario shown in FIG, the difference is that the other vehicle m2 is traveling in the lane L2. In the second scenario, when the other vehicle m2 is present at the same lateral position as the other vehicle m1 before the lateral movement, and the other vehicle m2 is present in front of the other vehicle m1 (or the host vehicle M) without lateral movement, the determination unit 142 does not make a correctness determination based on the driving trajectory K1 of the other vehicle m1. The determination of whether the other vehicle m2 is present at the same lateral position as the other vehicle m1 before the lateral movement is made, for example, Figure 4As shown, the lateral (Y-axis direction in the figure) offset W2 of the driving trajectories K1 and K2 is compared. If the offset W2 is less than a threshold, it is determined that they are in the same position. If the offset W2 is greater than the threshold, it is determined that they are not in the same position.

[0082] Thus, by not performing the accuracy determination based on the driving trajectory of the other vehicle m1 estimated to be changing lanes to enter or exit the lane of the other vehicle m2, the accuracy of the determination of the road shape using other vehicles can be further improved.

[0083] [Scene 3]

[0084] Figure 5 : is a diagram for explaining the determination process of the host vehicle M in the third scenario. Figure 5 In the example, with Figure 3 Compared to the first scenario shown, in addition to other vehicles m1 and m2, another vehicle m3 is present. Other vehicle m3 is the preceding vehicle traveling at a speed Vm3 as viewed from the host vehicle M. Other vehicle m3 is an example of a "third other vehicle." For example, the third other vehicle is located behind other vehicle m2, laterally relative to other vehicle m1 (within a predetermined distance in the lateral and longitudinal directions of other vehicle m1), and is moving laterally (a vehicle traveling alongside other vehicle m1).

[0085] In the third scenario, the first recognition unit 132 recognizes the position (relative position to the host vehicle M), speed (relative speed to the host vehicle M), driving lane, and driving position information (e.g., driving trajectory K3) of the other vehicle m3 in addition to the other vehicles m1 and m2. The determination unit 142 determines based on the recognition results, for example, Figure 5 As shown, when there is another vehicle m3 in addition to the other vehicle m1 at a position behind the other vehicle m2, and the lateral position of the destination of the lateral movement of the other vehicle m3 or the lateral position after the lateral movement is different from the lateral position of the second other vehicle m2, the correctness of the driving trajectories K1 and K3 of the other vehicles m1 and m3 is not determined.

[0086] The determination unit 142 may replace (or in addition to) the determination that "the lateral position of the destination of the lateral movement of the other vehicle m3 or the lateral position after the lateral movement is different from the lateral position of the other vehicle m2" with "the lateral position of the destination of the lateral movement of the other vehicle m3 is the same as the lateral position of the other vehicle m1". Figure 5As shown, the lateral (Y-axis direction in the figure) offset W3 of the driving trajectory K1 of the other vehicle m1 before the lateral movement and the driving trajectory K3 of the other vehicle m3 after the lateral movement are compared. If the offset W3 is less than the threshold, it is determined that they are in the same position. If the offset W3 is greater than the threshold, it is determined that they are not in the same position. Figure 5 In the example, it is determined that the lateral position (Y-axis coordinate) of the destination where the other vehicle m3 has moved laterally is different from the lateral position of the other vehicle m2 and is the same as the lateral position of the other vehicle m1 before the lateral movement.

[0087] [Scene 4]

[0088] Instead of the aforementioned scenario where the lateral position of the destination of the lateral movement of the other vehicle m3 is the same as the lateral position of the other vehicle m1, the determination unit 142 may alternatively determine that the lateral position of the other vehicle m3 before the lateral movement is the same as the lateral position of the other vehicle m1. This scenario will be described below as a fourth scenario, focusing on the differences from the third scenario.

[0089] Figure 6 4 is a diagram for explaining the determination process of the host vehicle M in the fourth scenario. Figure 6 In the example, Figure 5 Compared to the third scenario shown, the difference is that other vehicle m3 moves laterally to the right (from lane L2 to lane L3) rather than to the left (from lane L3 to lane L2). In the third scenario, other vehicle m1 and other vehicle m3 move laterally in the same direction. In the fourth scenario, other vehicle m1 and other vehicle m3 move laterally in opposite directions.

[0090] In the fourth scenario, when there is another vehicle m3 in addition to the other vehicle m1 at a position behind the other vehicle m2 and the lateral position of the other vehicle m3 before the lateral movement is the same as the lateral position of the other vehicle m1, the determination unit 142 does not make a correctness determination based on the driving trajectories K1 and K3 of the other vehicles m1 and m3. Figure 6 As shown, the lateral (Y-axis direction in the figure) offset W4 of the travel trajectory K1 of the other vehicle m1 before it moves laterally and the travel trajectory K3 of the other vehicle m3 before it moves laterally are compared. If the offset W4 is less than a threshold, it is determined that they exist at the same position. If the offset W4 is greater than the threshold, it is determined that they do not exist at the same position. Figure 6In the example, it is determined that the lateral position (coordinate on the Y axis) of the other vehicle m3 before the lateral movement is different from the lateral position of the other vehicle m2 and is the same as the lateral position of the other vehicle m1 before the lateral movement.

[0091] For example, the determination unit 142 may include in the condition that the third other vehicle is a vehicle that moves laterally in the same direction as the first other vehicle (the other vehicle m1). Figure 5 In the third scenario shown, the other vehicles m1 and m3 are moving laterally in the same direction (left), so the determination unit 142 does not determine whether the dividing line based on the driving trajectories K1 and K3 is correct. When the driving trajectory K1 of the other vehicle m1 is excluded from the determination, the other vehicle m3 moving laterally in the same direction is also excluded, thereby improving the determination accuracy. Figure 6 In the fourth scenario shown, the other vehicle m3 is not judged as correct or not. However, since the directions of lateral movement of the other vehicles m1 and m3 are opposite, the destination of the lateral movement or the lateral position before the lateral movement are different from those of the other vehicle m2. Therefore, the exclusion based on the other vehicle m2 is not performed, and the judgment unit 142 can also at least judge the correctness of the dividing line based on the driving trajectory K3.

[0092] In this way, even when multiple other vehicles are moving laterally, the dividing line used in the correct / incorrect judgment (or deviation judgment) can be more appropriately selected based on the driving trajectory and positional relationship between the preceding vehicle and the vehicle further ahead. Consequently, the correct / incorrect judgment of the dividing line can be more appropriately performed, improving the accuracy of road shape determination. The dividing line used in the correct / incorrect judgment (or deviation judgment) can be more appropriately selected based on the positional relationship (the lateral positional relationship before or after lateral movement) between the other vehicle m1 (the first other vehicle) and the other vehicle m3 (the third other vehicle), further improving determination accuracy.

[0093] The determination unit 142 may determine whether at least a portion of the preceding vehicle (the first other vehicle or the third other vehicle) is moving laterally, rather than determining whether the preceding vehicle (the first other vehicle or the third other vehicle) is traveling across the map dividing line. In this case, the determination unit 142 determines that the map dividing line is correct if at least a portion of the preceding vehicle is traveling across the map dividing line and the preceding vehicle (the second other vehicle) is traveling along the map dividing line.

[0094] In the first through fourth scenarios described above, if only a portion of the preceding vehicle's trajectory (the other vehicle m2) is available for some reason, the determination unit 142 may also determine the correctness of the dividing line based on the trajectory of the other vehicle m2 and the trajectory of vehicle m1 (and, in the third and fourth scenarios, the other vehicle m3). This may be due to, but is not limited to, the preceding vehicle (the other vehicle m2) being indiscernible (hidden) from the host vehicle M's position due to the presence of the preceding vehicle (e.g., the other vehicle m1) or other obstacles. For example, the portion of the preceding vehicle's trajectory may be located farther from the point where the lateral movement of the other vehicle m1 is completed, as viewed from the host vehicle M, or may be located within the processing distance. Thus, even when only a portion of the preceding vehicle's trajectory is available, determining the correctness of the dividing line by including the preceding vehicle's trajectory allows for a more appropriate determination process tailored to the situation.

[0095] Based on the determination result of the determination unit 142, the execution control unit 144 determines the driving control for the host vehicle M and executes the determined driving control. "Determining driving control" may, for example, include determining the content (type) of driving control and whether to execute (or suppress) the driving control. "Executing driving control" may, for example, include not only switching the content of the driving control to be executed but also continuing an already executed driving control. Suppressing driving control may include not only not executing (terminating) the driving control but also reducing 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 driving controls for avoiding 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.

[0096] 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 the steering control of the host vehicle M based on a dividing line identified 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 the steering control of the host vehicle M based on the map dividing line and 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.

[0097] Furthermore, the driving control may include a third driving control for prioritizing camera dividing lines over map dividing lines to control at least the steering and speed of the vehicle M, and a fourth driving control for prioritizing map dividing lines over camera dividing lines to control at least 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 when, for example, the recognition accuracy of the camera dividing lines falls below a threshold or becomes unrecognizable, processing is temporarily switched to processing based on the map dividing lines. Prioritizing map dividing lines over camera dividing lines means that processing based on the map dividing lines is basically performed, but when, for example, the map dividing lines cannot be determined, processing is temporarily switched to processing based on the camera dividing lines. The third and fourth driving controls are, for example, driving controls for situations where the camera dividing lines deviate from the map dividing lines.

[0098] 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, which has a lower degree of automation than the first level; and a third level, which has a lower degree of automation than the second level. Automation levels may also include a fourth level, which has a lower degree of automation than the third level (an example of a fourth degree of control). Automation levels may be determined by standardized information, regulations, or other factors, or may be independent indicator values. Therefore, the types, content, and number of automation levels are not limited to the following examples. For example, a low degree of automation in driving control means a low degree of automation in driving control and a high (heavy) workload placed on the driver. A low degree of automation in driving control 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, the driver's grip on the steering wheel (hereinafter referred to as "hands-on"). The tasks assigned to the driver are, for example, tasks assigned to the occupants (driver tasks) required to maintain the autonomous driving of the vehicle M. Therefore, if the occupants are unable to perform the assigned tasks, the automation level is reduced. For example, the first level of driving control may include driving controls such as ACC, ALC, LKAS, and TJP. The second or third level of driving control may include driving controls such as ACC, ALC, and LKAS. The fourth level of driving control may include manual driving. The fourth level of driving control may, for example, perform driving controls such as ACC. Of the first to fourth levels, the first level is the most automated level of driving control, and the fourth level is the least automated level of driving control.

[0099] 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, tasks assigned to the occupants (e.g., the driver), for example, include controlling the steering and speed of the host vehicle M using the driving control elements 80, in addition to monitoring the surroundings of the host vehicle M and maintaining a hands-on state. In other words, in the fourth level, the driver is in a state where 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 be associated with the first to fourth driving controls described above.

[0100] 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 to fourth driving controls based on the correct dividing line. Based on the determination result, the execution control unit 144 may also terminate driving control of the host vehicle M and switch to manual driving by the occupant, for example. Furthermore, the execution control unit 144 may 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 to fourth levels of driving control are executed depending on the situation.

[0101] [Processing Flow]

[0102] Hereinafter, the processing executed by the automatic driving control device 100 according to the embodiment will be described. Figure 7 This is a flowchart showing an example of the processing performed by the automatic driving control device 100 of the embodiment. The following mainly describes the processing of determining whether at least one of the camera dividing line CL and the map dividing line ML is correct or not. The automatic driving control device 100 performs the following processing based on the following example: Figure 7 The 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 a predetermined cycle, or may be repeatedly executed while the automatic driving control device 100 is executing the automatic driving.

[0103] exist Figure 7 In the example shown in FIG1 , the first recognition unit 132 identifies a dividing line (camera dividing line CL) surrounding the host vehicle M based on the output of the detection device DD that detects the surrounding conditions of the host vehicle M (step S100). Next, the first recognition unit 132 identifies other vehicles surrounding the host vehicle M (step S110). During step S110, for example, the position, speed, lane, and driving position information (driving trajectory) of the other vehicles are identified. Next, the second recognition unit 134 references map information based on the position information of the host vehicle M and identifies a dividing line (map dividing line ML) surrounding the host vehicle M from the map information (step S120).

[0104] Next, the determination unit 142 determines whether there is a first other vehicle moving laterally in front of the host vehicle M (step S130). If the determination unit 142 determines that there is a first other vehicle, the determination unit 142 determines whether there is a second other vehicle located ahead of the first other vehicle and not moving laterally (step S140). If the determination unit 142 determines that there is a second other vehicle, the determination unit 142 does not perform a correctness determination based on the driving trajectory of the first other vehicle (step S150). In this case, the determination unit 142 performs a correctness determination based on the driving trajectory of vehicles other than the first other vehicle identified by the first recognition unit 132 (step S160).

[0105] If the process of step S130 determines that there is no first other vehicle moving laterally ahead of the host vehicle M, or if the process of step S140 determines that there is no second other vehicle moving laterally ahead of the first other vehicle, the determination unit 142 determines whether the driving trajectory of the other vehicle is correct based on at least one of the camera dividing line and the map dividing line (step S170). The process of this flowchart then ends.

[0106] According to the above-mentioned embodiment, the determination device (recognition unit 130, determination unit 142) is provided with: a first recognition unit 132, which recognizes the surrounding conditions including the camera dividing line (first dividing line) dividing the driving lane of the vehicle M and other vehicles existing in the surroundings of the vehicle M based on the output of the detection device DD that detects the surrounding conditions of the vehicle M; a second recognition unit 134, which recognizes the camera dividing line (second dividing line) dividing the lanes around the vehicle M from the map information based on the position information of the vehicle M; and a determination unit 142, which recognizes the camera dividing line (second dividing line) dividing the lanes around the vehicle M based on the camera dividing line and the map dividing line. The accuracy of at least one of the camera dividing line and the map dividing line is determined based on the driving trajectory of at least one of the other vehicles and the driving trajectory of other vehicles. If, among the multiple other vehicles identified by the first recognition unit 132, there is a first other vehicle that is moving laterally ahead of the host vehicle M and a second other vehicle that is not moving laterally ahead of the first other vehicle, the determination unit 142 does not perform an accuracy determination based on the driving trajectory of the first other vehicle. This allows for more appropriate determination of the accuracy of the dividing line based on the conditions of the dividing line and other vehicles surrounding the host vehicle. Based on the determination result, more appropriate driving control can be performed, further improving the continuity of driving control. This can further contribute to the development of a sustainable transportation system.

[0107] According to the embodiment, based on the driving trajectory of other vehicles traveling in front of the vehicle M, when other vehicles are moving laterally, it is estimated that other vehicles are changing lanes and the correctness of the dividing line is not determined, thereby improving the determination accuracy.

[0108] The above-described embodiment can be expressed as follows.

[0109] A determination device comprising:

[0110] a storage medium storing computer-readable instructions; and

[0111] a processor connected to the storage medium,

[0112] The processor executes the computer-readable instructions to perform the following processing:

[0113] identifying the surrounding conditions including a first dividing line dividing the lane of the host vehicle and other vehicles existing around the host vehicle based on an output of a detection device that detects the surrounding conditions of the host vehicle;

[0114] identifying, from map information based on the position information of the host vehicle, a second dividing line that divides lanes around the host vehicle;

[0115] performing a correctness determination of 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

[0116] When, among the multiple identified other vehicles, there is a first other vehicle that is moving laterally at a position ahead of the vehicle itself and a second other vehicle that is not moving laterally at a position ahead of the first other vehicle, the correctness determination based on the driving trajectory of the first other vehicle is not performed.

[0117] 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; When, among the multiple other vehicles identified by the first identification unit, there is a first other vehicle that is moving laterally at a position ahead of the vehicle itself and a second other vehicle that is not moving laterally at a position ahead of the first other vehicle, the determination unit does not perform the correctness determination based on the driving trajectory of the first other vehicle.

2. The determination device according to claim 1, wherein: When the second other vehicle is located at the same position as the destination of the first other vehicle's lateral movement or the lateral position before the lateral movement and ahead of the first other vehicle, the determination unit does not perform the correctness determination based on the driving trajectory of the first other vehicle.

3. The determination device according to claim 1, wherein: The determination unit determines that, of the first dividing line and the second dividing line, the dividing line that follows the travel trajectory of the second other vehicle is a correct dividing line.

4. The determination device according to claim 1, wherein: The determination unit determines, as a correct dividing line, a dividing line having a deviation angle with respect to the traveling direction of the second other vehicle of the first dividing line and the second dividing line being equal to or smaller than a threshold value.

5. The determination device according to claim 2, wherein: In a case where there is a third other vehicle at a position behind the second other vehicle that has undergone a lateral movement or whose lateral position before the lateral movement is different from the lateral position of the second other vehicle, the determination unit does not perform the correctness determination based on the driving trajectory of the first other vehicle and the driving trajectory of the third other vehicle.

6. The determination device according to claim 5, wherein: The third other vehicle is a vehicle whose destination of the lateral movement or the lateral position before the lateral movement exists at the lateral position before the first other vehicle made the lateral movement.

7. The determination device according to claim 5, wherein: The third other vehicle is a vehicle that moves laterally in the same direction as the first other vehicle.

8. The determination device according to claim 1, wherein: The determination unit performs the correctness determination in a region of a predetermined width in the vehicle width direction based on the position of the second other vehicle.

9. The determination device according to claim 1, wherein: When the driving trajectory of the second other vehicle only obtains a driving trajectory farther than the location where the lateral movement of the first other vehicle that is moving laterally in front of the own vehicle is completed, the judgment unit performs the correctness judgment based on the driving trajectory of the first other vehicle and the driving trajectory of the second other vehicle.

10. A determination method, wherein: The determination method enables the computer to perform the following processing: identifying the surrounding conditions including a first dividing line dividing the lane of the host vehicle and other vehicles existing around the host vehicle based on an output of a detection device that detects the surrounding conditions of 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, among the multiple identified other vehicles, there is a first other vehicle that is moving laterally at a position ahead of the vehicle itself and a second other vehicle that is not moving laterally at a position ahead of the first other vehicle, the correctness determination based on the driving trajectory of the first other vehicle is not performed.

11. A storage medium, wherein: The storage medium stores a program that causes the computer to perform the following processing: identifying the surrounding conditions including a first dividing line dividing the lane of the host vehicle and other vehicles existing around the host vehicle based on an output of a detection device that detects the surrounding conditions of 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, among the multiple identified other vehicles, there is a first other vehicle that is moving laterally at a position ahead of the vehicle itself and a second other vehicle that is not moving laterally at a position ahead of the first other vehicle, the correctness determination based on the driving trajectory of the first other vehicle is not performed.

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