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 road dividing lines around a vehicle and the conditions of surrounding vehicles. A determination device according to an embodiment is provided with: a first recognition unit that recognizes, on the basis of an output from a detection device that detects a surrounding situation of a host vehicle, a surrounding situation including a first division line that divides a travel lane of the host vehicle and a surrounding vehicle existing in the periphery of the host vehicle; a second recognition unit that recognizes, from 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 the first dividing line and the second dividing line deviate, the determination unit being configured to differ in a manner of determining whether or not the first dividing line and the second dividing line deviate in a case where the first recognition unit recognizes the surrounding vehicle and a case where the surrounding vehicle is not recognized.

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 access to sustainable transportation systems that also take into account vulnerable traffic participants have become increasingly active. To achieve this, research and development efforts are underway in autonomous driving technologies to further improve traffic safety and convenience. In this regard, there is a known technique for controlling a vehicle's driving mode based on the degree of parallelism between the driving trajectories of other vehicles in the vicinity and the camera dividing lines when a deviation is determined between road dividing lines shown in camera images (camera dividing lines) and road dividing lines shown in map information (map dividing lines). (For example, see Japanese Patent Application Laid-Open No. 2023-148405). Summary of the Invention

[0003] However, in previous autonomous driving technologies, when using the trajectory of a vehicle traveling side by side in an adjacent lane, the timing of trajectory changes is delayed compared to the trajectory of the preceding vehicle, potentially delaying the accuracy determination of the camera dividing line. Conventional camera dividing lines are also sometimes difficult to discern at long distances, making it impossible to compare the camera dividing lines with the trajectories of other vehicles at such distances, similarly resulting in a potential delay in the accuracy determination.

[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 road dividing lines based on the vehicle's surrounding road dividing lines and the conditions of surrounding vehicles. Furthermore, this can contribute to the development of sustainable transportation systems.

[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 a surrounding condition including a first dividing line dividing a lane of the vehicle and surrounding 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 the first dividing line deviates from the second dividing line, wherein the determination unit determines whether the first dividing line deviates from the second dividing line in a manner different between a case where the surrounding vehicle is recognized by the first recognition unit and a case where the surrounding vehicle is not recognized.

[0007] (2): Based on the solution of (1) above, the surrounding vehicles include adjacent vehicles that are traveling in an adjacent lane adjacent to the lane in which the host vehicle is traveling and are located within a specified distance from the host vehicle. In the case where the surrounding vehicles include adjacent vehicles, the determination unit makes the determination method different.

[0008] (3): In the embodiment of (1), when the surrounding vehicle is recognized, the determination unit is more likely to determine that the first dividing line and the second dividing line are deviated from each other than when the surrounding vehicle is not recognized.

[0009] (4): Based on the solution of (2) above, when the surrounding vehicle is identified by the first identification unit, the determination unit determines whether the first dividing line deviates from the second dividing line based on the driving trajectory of the surrounding vehicles other than the adjacent vehicle among the identified surrounding vehicles and located in a position ahead of the adjacent vehicle in the direction of travel and the second dividing line.

[0010] (5): Based on the solution of (4), the determination unit sets the first imaginary dividing line according to the driving trajectory of the surrounding vehicles, and determines whether the set first imaginary dividing line deviates from the second dividing line.

[0011] (6): Based on the solution of (2) above, when the first recognition unit identifies the surrounding vehicle, the determination unit determines whether the first dividing line deviates from the second dividing line at a position less than a specified distance from the host vehicle, and determines whether the driving trajectory of the surrounding vehicles other than the adjacent vehicles and ahead of the adjacent vehicles deviates from the second dividing line at a position more than a specified distance from the host vehicle.

[0012] (7): In the embodiment of (5), the determination unit determines whether the imaginary first dividing line and the second dividing line are offset from each other at a position that is a predetermined distance or more from the host vehicle.

[0013] (8): Based on the solution of (2) above, after the determination unit uses the surrounding vehicles to determine whether the first dividing line and the second dividing line deviate, if the first recognition unit no longer recognizes the adjacent vehicle, the determination result of whether the deviation occurs is reset.

[0014] (9): Based on the solution of (4) above, the determination unit obtains a driving trajectory of the surrounding vehicle whose deviation angle relative to the extension direction of the second dividing line is greater than a predetermined angle, and determines whether the obtained driving trajectory deviates from the second dividing line.

[0015] (10): Based on the solution of (4), the determination unit obtains the deviation direction of the driving trajectory of the surrounding vehicles relative to the extension direction of the second dividing line, and determines whether the driving trajectory of the party with the same deviation direction and the larger number of the driving trajectories deviate from the second dividing line.

[0016] (11): Based on the solution of (4) above, the determination unit obtains the deviation direction of the driving trajectory of the surrounding vehicle relative to the extension direction of the second dividing line. When the number of driving trajectories with the same deviation direction is the same in multiple different directions, the determination of whether the driving trajectory of the surrounding vehicle deviates from the second dividing line is not performed.

[0017] (12): A determination method according to one embodiment of the present invention 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 surrounding 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; determine whether the first dividing line deviates from the second dividing line; and make the method of determining whether the first dividing line deviates from the second dividing line different in a case where the surrounding vehicles are identified and a case where the surrounding vehicles are not identified.

[0018] (13): 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 driving lane of the vehicle and surrounding vehicles existing around 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; determining whether the first dividing line deviates from the second dividing line; and making the method of determining whether the first dividing line deviates from the second dividing line different when the surrounding vehicles are identified and when the surrounding vehicles are not identified.

[0019] According to the above aspects (1) to (13), the correctness of the road dividing lines around the vehicle and the conditions of the surrounding vehicles can be more appropriately determined. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 It is a structural diagram of a vehicle system including the vehicle control device according to the embodiment.

[0021] Figure 2 This is a functional structure diagram of the first control unit and the second control unit.

[0022] Figure 3 1 is a diagram for explaining the determination process and driving control of the host vehicle M in the first scenario.

[0023] Figure 4 This is a diagram for explaining the content of determination using the virtual camera dividing line.

[0024] Figure 5 3 is a diagram for explaining the determination process and driving control of the host vehicle M in the second scenario.

[0025] Figure 6 This is a flowchart showing an example of processing executed by the automatic driving control device according to the embodiment. DETAILED DESCRIPTION

[0026] 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) demarcating the lane in which the vehicle is traveling is correct is 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.

[0027] [Overall structure]

[0028] 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.

[0029] Vehicle system 1 includes, for example, a camera 10, a radar device 12, a LIDAR (Light Detection and Ranging) device 14, an object recognition device 16, a communication device 20, 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."

[0030] 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.

[0031] 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.

[0032] 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.

[0033] 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).

[0034] 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.

[0035] 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.

[0036] 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.

[0037] 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.

[0038] 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.

[0039] 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 .

[0040] 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.

[0041] 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.

[0042] 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).

[0043] 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.

[0044] 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).

[0045] 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.

[0046] 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.

[0047] 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.

[0048] 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.

[0049] 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."

[0050] 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 generating unit 140 at a predetermined timing.

[0051] 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.

[0052] 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.

[0053] 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.

[0054] 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.

[0055] 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.

[0056] 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.

[0057] [Identification Department and Action Plan Generation Department]

[0058] Next, the functions of the recognition unit 130 (first recognition unit 132 and second recognition unit 134) and the action plan generation unit 140 (determination unit 142 and execution control unit 144) will be described in detail. The following description will focus on the determination process and driving control (travel control) based on the determination process in the embodiment, divided into several scenarios.

[0059] [Scene 1]

[0060] Figure 3 1 is a diagram for explaining the determination process and driving control of the host vehicle M in the first scenario. Figure 3In the example shown, dividing lines CL1 to CL3 recognized by the detection device DD and dividing lines ML1 to ML3 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, and lane L2 is divided by dividing lines ML2 and ML3. Lanes L1 and L2 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 CL3 are examples of “first dividing lines”, and the dividing lines ML1 to ML3 are examples of “second dividing lines”. Figure 3 In the example, the vehicle M is traveling in lane L1 at a speed VM, and another vehicle m1 is traveling in lane L2, which is adjacent to lane L1, at a speed Vm1. The other vehicle m1 is the adjacent vehicle of the vehicle M. An adjacent vehicle is, for example, a vehicle traveling in an adjacent lane adjacent to the lane in which the vehicle is traveling (driving side by side). In addition, an adjacent vehicle may also be a vehicle that is within a predetermined distance from the vehicle M. Figure 3 In the example of , the vehicle M performs predetermined driving control (eg, LKAS) based on the surrounding conditions, instructions from the occupants, etc. Figure 3 In the example of , in front of the host vehicle M (and the other vehicle m1 ), the other vehicle m2 is traveling at a speed Vm2 , and the other vehicle m3 is traveling at a speed Vm3 .

[0061] 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 (external environment) 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 CL3 that divides the adjacent lane (lane L2) adjacent to the driving lane. Hereinafter, the dividing lines CL1 to CL3 are sometimes referred to as "camera dividing lines CL1 to CL3". 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 recognize the camera dividing lines CL1 to CL3 in the image plane. The first recognition unit 132 converts the positions of the camera dividing lines CL1 to CL3 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 CL3, for example. The first recognition unit 132 may also recognize the amount of change in the curvature of the camera dividing lines CL1 to CL3. The amount of change in curvature refers to, for example, the time rate of change of the curvature at the front x [m] of the camera dividing lines CL1 to CL3 recognized by the camera 10 when viewed from the vehicle M. The first recognition unit 132 may also recognize the curvature or the amount of change in curvature of the lane divided by the camera dividing lines CL1 to CL3 by averaging the curvature or the amount of change in curvature of each of the camera dividing lines CL1 to CL3. The camera dividing lines CL1 to CL3 may be recognized or corrected based on the output of a detection device other than the camera 10 (for example, the radar device 12, the LIDAR 14).

[0062] The first recognition unit 132 recognizes other vehicles (surrounding 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 (adjacent vehicle) m1 traveling alongside the host vehicle M in an adjacent lane, and other vehicles (preceding vehicles) m2 and m3 traveling ahead of the host vehicle M (adjacent vehicles), based on the output of the detection device DD that detects the surrounding conditions of the host vehicle M. The first recognition unit 132 identifies the position (relative position relative to the host vehicle M) and speed (relative speed relative to the host vehicle M) of each of the other vehicles m1-m3, as well as the lane, vehicle orientation, and travel direction of the other vehicles m1-m3. The first recognition unit 132 may also identify the driving position information of the other vehicles m1-m3. The driving position information, for example, refers to the driving trajectories K1-K3 based on the respective reference positions (e.g., center or center of gravity) of the other vehicles m1-m3 during driving within a predetermined time period.

[0063] The second recognition unit 134 identifies lane dividing lines that demarcate the surrounding area of ​​the vehicle M 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 to ML3 that are located in the direction in which the vehicle M is traveling or in the direction in which the vehicle M can travel. Hereinafter, the lane dividing lines ML1 to ML3 may be referred to as "map lane dividing lines ML1 to ML3."

[0064] The second recognition unit 134 may also recognize map dividing lines ML1 and ML2 among the recognized map dividing lines ML1 to ML3 as dividing lines that divide the lane L1 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 ML3 from the second map information 62. The second recognition unit 134 may also recognize the curvature or curvature change of the lane divided by the map dividing lines by averaging the curvature or curvature change of each of the map dividing lines ML1 to ML3.

[0065] The determination unit 142 determines whether the camera dividing lines CL1-CL3 identified by the first recognition unit 132 deviate from the map dividing lines ML1-ML3 identified by the second recognition unit 134. For example, the determination unit 142 derives the degree of deviation between the dividing lines CL1 and ML1, which are located closest to the left side of the vehicle M, the degree of deviation between the dividing lines CL2 and ML2, which are located closest to the right side of the vehicle M, and the degree of deviation between the dividing lines CL3 and ML3 on the adjacent lane side. If the derived degree of deviation is greater than a threshold, the determination unit 142 determines that the camera dividing lines and the map dividing lines have deviated; if the derived degree of deviation is less than the threshold, the determination unit 142 determines that there has been no deviation. This deviation determination is repeated at a predetermined timing or period.

[0066] For example, the determination unit 142 overlaps the camera dividing lines CL1, CL2, and CL3, and overlaps the map dividing lines ML1, ML2, and ML3, based on the position of the representative point of the vehicle M in the plane of the vehicle coordinate system (XY plane). Furthermore, when determining the dividing lines of the comparison object (dividing lines CL1 and ML1, dividing lines CL2 and ML2, dividing lines CL3 and ML3), 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 degree of deviation refers to, for example, the degree of offset in 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, and the lateral position offset D3 between the dividing line CL3 and ML3, or by using the maximum or minimum value of the offsets D1, D2, and D3.

[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, and the angle θ3 formed by the dividing lines CL3 and ML3 can be used, or the maximum or minimum value of the angles θ1, θ2, and θ3 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 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, and the difference between dividing lines CL3 and ML3, 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-CL3 and the average of the curvature variation between dividing lines ML1-ML3. Alternatively, the difference between the curvature variation of the lanes (lanes L1 and L2) identified from the camera image and the curvature variation of the lanes identified from map information can be used.

[0069] In this embodiment, the determination unit 142 uses different methods for determining whether the camera dividing line and the map dividing line deviate from each other depending on whether the first recognition unit 132 recognizes a surrounding vehicle or whether it does not. "Differentiating the determination method" means, for example, varying the threshold used in the deviation determination or the determination processing cycle, among other factors. The determination unit 142 may also use a different determination method when the recognized surrounding vehicles include neighboring vehicles. By varying the determination method to more quickly prevent interference (contact) between the host vehicle M and the neighboring vehicle, more appropriate deviation determination and driving control based on the determination results can be achieved.

[0070] The determination unit 142 may also determine whether the lane demarcation line (e.g., a camera-based demarcation line) that demarcates the lane in which the host vehicle M is traveling is correct, based on the deviation determination result. Alternatively, or in addition to determining whether the lane demarcation line is correct, the determination unit 142 may determine whether the lane demarcated by the demarcation line is correct. If the lane demarcation line is determined to be correct, the execution control unit 144 generates a target trajectory so that the host vehicle M travels along the correct demarcation line, and the second control unit 160 executes driving control (travel control) of the host vehicle M based on the target trajectory.

[0071] For example, when the first recognition unit 132 identifies surrounding vehicles, including an adjacent vehicle, the determination unit 142 performs a deviation determination based on the driving trajectory of a surrounding vehicle other than the adjacent vehicle (or the host vehicle M) that is located ahead (toward in the direction of travel) of the adjacent vehicle (or the host vehicle M) and the map dividing line. When an adjacent vehicle is identified, by comparing the driving trajectory of a surrounding vehicle other than the adjacent vehicle (e.g., the preceding vehicle) with the map dividing line, a deviation determination can be made even when distant camera dividing lines cannot be identified. This allows the host vehicle M to proactively implement driving control that prevents interference with the adjacent vehicle based on the prediction that the adjacent vehicle will also be traveling along the preceding vehicle's trajectory, thereby more reliably preventing interference.

[0072] For example, Figure 3 As shown in the scene, the recognition range of the camera dividing lines CL1 to CL3 in front of the vehicle M recognized by the first recognition unit 132 (the range in which the recognition accuracy is greater than the threshold) is Figure 3 At the location P1 shown in FIG. 1 , the vehicle M cannot recognize the camera dividing line in front of it (farther than the location P1 when viewed from the vehicle M). In this case, the determination unit 142 considers the driving trajectory of the surrounding vehicle recognized by the first recognition unit 132 ( Figure 3 Among the driving trajectories K1-K3 of other vehicles m1-m3 (shown), the driving trajectories K2 and K3 of other vehicles m2 and m3, which are located farther from point P1, are treated similarly to the camera dividing line and compared with the map dividing line ML to determine whether there is deviation. To determine whether there is deviation, the determination unit 142 derives the degree of deviation of each based on, for example, the angles θa and θb formed between the extending directions of the map dividing lines ML1-ML3 and the extending directions of the driving trajectories K2 and K3 (which can also be replaced by "the angle of deviation of the driving trajectories K2 and K3 with respect to the extending directions of the map dividing lines ML1-ML3"). If at least one of the derived degrees of deviation, or the average of the degrees of deviation, is greater than a threshold, it is determined that the dividing line has deviated; if it is less than the threshold, it is determined that there is no deviation. The determination unit 142 may also determine that the camera dividing lines CL1 and CL2 are the correct dividing lines if deviation is determined under the above conditions.

[0073] The conditions for determining that the camera dividing lines CL1 and CL2 are correct dividing lines may include, for example, the case where the driving trajectory K1 of the adjacent vehicle m1 does not deviate from the camera dividing lines CL1 and CL2. The location where the determination unit 142 can make a deviation determination farther from the location P1 may be, for example, the driving trajectory of a surrounding vehicle (at the location farthest from the vehicle M (or the adjacent vehicle m1)) recognized by the vehicle M. Figure 3In the example, the reference point P2 is the end (far end) of the travel trajectory K2 of the other vehicle m2, but it may be a distant point a predetermined distance from the point P1. Thus, by limiting the range in this way, erroneous judgments at distant locations can be suppressed.

[0074] The judgment unit 142 can also use the driving trajectories K2 and K3 instead of the driving trajectories K2 and K3 of other vehicles m2 and m3 to virtually set camera dividing lines (virtual camera dividing lines, virtual first dividing lines), and compare the set virtual camera dividing lines with the map dividing lines to perform deviation judgment, or to determine whether the camera dividing lines are correct dividing lines. Figure 4 This is a diagram for explaining the content of determination using the virtual camera dividing line. Figure 4 is with Figure 3 The same scene as the first scene shown. Figure 4 In the example, the determination unit 142 sets virtual camera dividing lines VCL1-VCL3 extending from the end (point P1) of the camera dividing lines CL1-CL3 identified by the first recognition unit 132 parallel to the direction of travel paths K2 and K3 of other vehicles m2 and m3 located farther away from point P1. The lengths of the virtual camera dividing lines VCL1-VCL3 are set based on, for example, the location of the preceding vehicle's travel path. The endpoints of the virtual camera dividing lines VCL1-VCL3, when point P1 is used as the starting point, can be either point P2, which is based on the end (far end) of the travel path K2 of other vehicle m2 identified as being the farthest from the host vehicle M (or other vehicle m1), or point P3, which is obtained by adding a predetermined length. The lengths of the virtual camera dividing lines VCL1-VCL3 can be predetermined fixed lengths. By limiting the range in this way, erroneous judgments at distant locations can be suppressed.

[0075] Then, the determination unit 142 regards the set virtual camera dividing lines VCL1 to VCL3 as camera dividing lines, compares them with the map dividing lines ML1 to ML3, and determines whether they deviate from each other. Figure 4 In the example, the determination unit 142 obtains the angles θα, θβ, and θγ formed by the extension directions of the map dividing lines ML1 to ML3 and the extension directions of the virtual camera dividing lines VCL1 to VCL3, uses the obtained angles θα, θβ, and θγ to derive a degree of deviation, and performs deviation determination based on the derived degree of deviation and a threshold value. The degree of deviation in this case is derived based on, for example, the average, maximum, or minimum value of the angles θα, θβ, and θγ.

[0076] Thus, even when the camera dividing line cannot be directly identified, it is possible to compare the camera dividing line, which is hypothetically set based on the driving trajectory of surrounding vehicles, with the map dividing line to determine deviation, etc. This can prevent the camera dividing line from being directly determined to be incorrect due to the inability to identify the camera dividing line, and it is possible to continue driving control to make the host vehicle M travel along either the camera dividing line or the map dividing line.

[0077] When the distance to the host vehicle M is short (e.g., less than a predetermined distance), the determination unit 142 determines the deviation between the camera dividing line and the road dividing line even if there is a surrounding vehicle (e.g., an adjacent vehicle) at a short distance. On the other hand, when the distance to the host vehicle M is long (e.g., more than a predetermined distance), the determination unit 142 determines the deviation between the camera dividing line and the road dividing line. Figure 3 As shown, the deviation determination is performed based on the driving trajectory of the surrounding vehicle that is located in front of the adjacent vehicle (or the host vehicle M) and the map dividing line. The predetermined distance may be a predetermined fixed distance or a variable distance depending on the speed VM of the host vehicle M, the road shape, the recognition range of the camera dividing line, etc. The determination unit 142 may also determine that the vehicle is at a long distance from the host vehicle M, such as Figure 4 As shown in FIG, deviation determination is performed based on the virtual camera dividing line and the map dividing line.

[0078] The determination unit 142 may also determine the surrounding vehicles based on the above-mentioned Figure 3 、 Figure 4 After determining that the camera dividing line and the map dividing line are not deviated from each other by the deviation determination method shown, if the surrounding vehicles no longer include the adjacent vehicle (the adjacent vehicle is no longer recognized by the first recognition unit 132), the deviation determination result is reset. Figure 3 、 Figure 4 The method shown here aims to preemptively determine deviation from and correctness of the dividing line to minimize interference between the vehicle M and a neighboring vehicle (the other vehicle m1). Therefore, if no neighboring vehicle is present, the deviation determination result is reset. In this case, the determination unit 142 re-determines the deviation based on the current situation or determines deviation from the map dividing line within the range where the camera dividing line can be identified. This allows for more appropriate determination based on surrounding conditions.

[0079] In the embodiment, when the first recognition unit 132 recognizes a surrounding vehicle, the determination unit 142 reduces the threshold used in the deviation determination (the threshold used to compare the degree of deviation) compared to when no surrounding vehicle is recognized, making it easier to determine that the vehicle has deviated from the dividing line. This allows the determination result of the deviation from the dividing line to be obtained earlier when a surrounding vehicle is present, allowing the driving control currently being executed by the host vehicle M to be switched. This allows for more appropriate suppression of interference (contact) between the host vehicle M and surrounding vehicles. The determination unit 142 may also accelerate the deviation determination cycle when a surrounding vehicle is recognized compared to when no surrounding vehicle is recognized. This allows for determinations to be made under conditions more closely resembling the current situation, enabling earlier switching of the driving control currently being executed by the host vehicle M based on the situation, thereby more appropriately suppressing interference between the host vehicle M and surrounding vehicles. The phrase "when a surrounding vehicle is recognized" can be replaced with "when the surrounding vehicles include adjacent vehicles," and the phrase "when no surrounding vehicles are recognized" can be replaced with "when the surrounding vehicles do not include adjacent vehicles (or when no adjacent vehicles exist)."

[0080] If the determination unit 142 does not recognize any surrounding vehicles, it does not perform Figure 3 、 Figure 4 The deviation determination shown uses the traveling track of the surrounding vehicles, and the deviation determination from the map dividing line is performed within the range where the camera dividing line can be recognized.

[0081] 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 (or terminating) the driving control but also reducing the automation level of the driving control. 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.

[0082] 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 vehicle M's steering or speed based on a dividing line recognized by the first recognition unit 132 or the second recognition unit 134 (e.g., a dividing line where the camera dividing line and the map dividing line do not deviate). For example, the first driving control involves driving the 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 vehicle M's steering or speed based on the camera dividing line recognized by the first recognition unit 132 and driving position information of other vehicles. For example, the second driving control involves driving the vehicle M so that its representative point follows the driving trajectory of the other vehicle m1.

[0083] 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 or 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 or 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 temporarily switches to processing based on the map dividing lines when, for example, the recognition accuracy of the camera dividing lines falls below a threshold or becomes unrecognizable. Prioritizing map dividing lines over camera dividing lines means that processing based on the map dividing lines is basically performed, but temporarily switches to processing based on the camera dividing lines when, for example, the map dividing lines cannot be determined. The third and fourth driving controls are, for example, driving controls for situations where the camera dividing lines deviate from the map dividing lines.

[0084] 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.

[0085] 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.

[0086] For example, if the determination unit 142 determines that the camera dividing line and the map dividing line have not deviated, the execution control unit 144 executes the first driving control. If the determination unit 142 determines that the camera dividing line and the map dividing line have not deviated, the execution control unit 144 executes one of the second to fourth driving control modes depending on the situation. 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 determination unit 142 determines that the camera dividing line and the map dividing line have not deviated, the first to second driving control modes are executed. If the determination unit 142 determines that the camera dividing line and the map dividing line have not deviated, the third to fourth driving control modes are executed depending on the situation.

[0087] [Scene 2]

[0088] Figure 5 1 is a diagram for explaining the determination process and driving control of the host vehicle M in the second scenario. Figure 5 In the example above, Figure 3Compared to the example, the difference is that, in addition to the other vehicles m1-m3, there is another vehicle (surrounding vehicle) m4 traveling in front of the host vehicle M at a speed Vm4. In the second scenario, the first recognition unit 132 identifies the position, speed, driving lane, vehicle orientation, travel direction, and driving trajectories K1-K4 of the other vehicles m1-m4 surrounding the host vehicle M. The determination unit 142 uses the map dividing lines ML1-ML3 and the driving trajectories K1-K4 of the other vehicles m1-m4 to perform a deviation determination. In this case, the determination unit 142 uses, for example, the deviation angle of the driving trajectories K1-K4 from the extending direction of the map dividing lines ML1-ML3 by a predetermined angle or more to perform a deviation determination. The predetermined angle can be a fixed angle or a variable angle depending on the road shape, etc.

[0089] exist Figure 5 In the example, the deviation angles θa-θc of other vehicles m2-m4 among other vehicles m1-m4 are greater than a predetermined angle. Therefore, the driving trajectories K2-K4 of other vehicles m2-m4 are selected as the target driving trajectories for deviation determination. It is expected that other vehicles whose driving trajectories deviate less than the predetermined angle from the map dividing line are less likely to approach the host vehicle M. Therefore, by excluding driving trajectories with less than the predetermined angle from the target driving trajectories for deviation determination, processing efficiency can be improved.

[0090] In the second scenario, the determination unit 142 may also obtain the deviation direction of the driving tracks K1 to K4 relative to the extension direction of the map dividing line, compare the obtained deviation directions, and use the driving track with the larger deviation direction to determine the deviation from the map dividing line. Figure 5 In the example, with respect to the direction in which map dividing lines ML1-ML3 extend, driving trajectories K2 and K3 deviate to the right, while driving trajectories K4 deviate to the left. Therefore, determination unit 142 uses driving trajectories K2 and K3, which deviate to the right in the most frequent direction, to determine deviations from map dividing lines ML1-ML3. This eliminates other vehicles deviating in the opposite direction to avoid obstacles or change lanes, enabling more appropriate deviation determination based on driving trajectories and map dividing lines.

[0091] The determination unit 142 may also not perform a deviation determination based on the driving track from the map dividing line if the number of driving tracks deviating in the same direction is the same across multiple different directions. For example, if the number of driving tracks deviating to the right and the number of driving tracks deviating to the left relative to the direction in which the map dividing line extends is the same, the determination unit 142 may not perform a deviation determination based on the driving track. If the number of driving tracks deviating to the left and right is the same, it is difficult to determine which is correct. Therefore, by controlling the deviation determination based on the driving track to be omitted when the number is the same, erroneous determinations can be prevented. For example, the processing in the second scenario may be performed only when the surrounding vehicles identified by the first recognition unit 132 include both the preceding vehicle and the adjacent vehicle.

[0092] [Processing Flow]

[0093] Hereinafter, the processing executed by the automatic driving control device 100 according to the embodiment will be described. Figure 6 This is a flowchart illustrating an example of processing performed by the automatic driving control device 100 according to an embodiment. The following description focuses on the processing performed by the automatic driving control device 100, primarily the processing for determining deviation between the map dividing line and the camera dividing line, and the driving control processing based on the determination result. At the start of the process, the vehicle M is performing prescribed driving control based on surrounding conditions, instructions from the occupants, and the like. The processing described below can be repeatedly executed at prescribed times or intervals, or while the automatic driving control device 100 is executing automatic driving.

[0094] exist Figure 6 In the example shown in FIG. 1 , the first recognition unit 132 identifies a dividing line (camera dividing line) 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 surrounding vehicles surrounding the host vehicle M (step S110 ). Next, the second recognition unit 134 references map information based on the positional information of the host vehicle M and identifies a dividing line (map dividing line) surrounding the host vehicle M from the map information (step S120 ).

[0095] Next, the determination unit 142 determines whether the first recognition unit 132 has recognized a surrounding vehicle (step S130). If the determination unit 142 determines that a surrounding vehicle has been recognized, the determination unit 142 compares the camera dividing line with the map dividing line based on a first condition (step S140). If the process in step S130 determines that no surrounding vehicle has been recognized, the determination unit 142 compares the camera dividing line with the map dividing line based on a second condition different from the first condition (step S150). In other words, the determination unit 142 uses different methods to determine whether the camera dividing line and the map dividing line are misaligned depending on whether a surrounding vehicle has been recognized or not.

[0096] Next, the determination unit 142, through the processing of step S140 or S150, determines whether the camera dividing line and the map dividing line have deviated (step S160). If the determination is that there has been a deviation, the execution control unit 144 suppresses driving control of the host vehicle M (step S170). Suppressing driving control includes, for example, switching the currently executing driving control (e.g., from the first driving control to the third driving control or the fourth driving control), terminating the currently executing driving control (or not starting it), or reducing the automation level of the driving control. If the determination in step S160 is that there has been no deviation, the execution control unit 144 executes driving control (or continues the currently executing driving control) based on at least one of the camera dividing line identified by the first recognition unit 132 and the map dividing line identified by the second recognition unit 134 (step S180). This concludes the processing of this flowchart.

[0097] 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 (an example of a first dividing line) that demarcates the lane of the host vehicle M and surrounding vehicles present, based on the output of a detection device that detects the surrounding conditions of the host vehicle M; a second recognition unit 134 for recognizing a map dividing line (a second dividing line) that demarcates the lanes surrounding the host vehicle M from map information based on the position information of the host vehicle M; and a determination unit 142 for determining whether the camera dividing line deviates from the map dividing line. By using different methods for determining whether the camera dividing line deviates from the map dividing line when the first recognition unit 132 recognizes a surrounding vehicle and when it does not recognize a surrounding vehicle, the determination unit 142 can more appropriately determine the correctness of the dividing line based on the surrounding road dividing lines and the surrounding vehicle conditions. Therefore, according to the embodiment, the continuity of driving control can be further improved, thereby contributing to the development of a sustainable transportation system.

[0098] According to the embodiments, for example, if there are adjacent vehicles around the host vehicle M, the driving trajectory of the host vehicle M or a neighboring vehicle traveling ahead of the adjacent vehicle can be used to determine in advance whether the host vehicle M deviates from the map dividing line or whether the camera dividing line is correct. This can prevent interference (contact) between the host vehicle M and the adjacent vehicle, allowing for more appropriate driving control.

[0099] The above-described embodiment can be expressed as follows.

[0100] A determination device comprising:

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

[0102] a processor connected to the storage medium,

[0103] The processor executes the computer-readable instructions to perform the following processing:

[0104] identifying a surrounding condition including a first dividing line dividing a lane of the host vehicle and surrounding vehicles existing around the host vehicle based on an output of a detection device that detects a surrounding condition of 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 the first dividing line deviates from the second dividing line; and

[0107] The method of determining whether the first dividing line and the second dividing line deviate from each other is different between a case where the surrounding vehicle is recognized and a case where the surrounding vehicle is not recognized.

[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 surrounding 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 configured to determine whether the first dividing line and the second dividing line deviate from each other; The determination unit determines whether the first dividing line and the second dividing line are offset from each other in different ways depending on whether the surrounding vehicle is recognized by the first recognition unit and when the surrounding vehicle is not recognized.

2. The determination device according to claim 1, wherein: The surrounding vehicles include adjacent vehicles that are traveling in an adjacent lane adjacent to the lane in which the host vehicle is traveling and are located within a predetermined distance from the host vehicle. When the surrounding vehicles include an adjacent vehicle, the determination unit makes the determination in a different manner.

3. The determination device according to claim 1, wherein: When the surrounding vehicle is recognized, the determination unit is more likely to determine that the first dividing line and the second dividing line are deviated from each other than when the surrounding vehicle is not recognized.

4. The determination device according to claim 2, wherein: When the surrounding vehicle is identified by the first identification unit, the determination unit determines whether the first dividing line deviates from the second dividing line based on the driving trajectory of the surrounding vehicles other than the adjacent vehicle among the identified surrounding vehicles and located in a position ahead of the adjacent vehicle in the direction of travel and the second dividing line.

5. The determination device according to claim 4, wherein: The determination unit sets the first virtual dividing line according to the travel trajectory of the surrounding vehicles, and determines whether the set first virtual dividing line deviates from the second dividing line.

6. The determination device according to claim 2, wherein: When the first recognition unit recognizes the surrounding vehicle, the determination unit At a position less than a predetermined distance from the host vehicle, determining whether the first dividing line deviates from the second dividing line; At a position at a predetermined distance or more from the host vehicle, it is determined whether a travel trajectory of a surrounding vehicle other than the adjacent vehicle and ahead of the adjacent vehicle among the surrounding vehicles deviates from the second dividing line.

7. The determination device according to claim 5, wherein: The determination unit determines whether the imaginary first dividing line and the second dividing line are deviated from each other at a position that is a predetermined distance or more from the host vehicle.

8. The determination device according to claim 2, wherein: After determining whether the first dividing line and the second dividing line deviate from each other using the surrounding vehicle, the determination unit resets the result of the deviation determination when the adjacent vehicle is no longer recognized by the first recognition unit.

9. The determination device according to claim 4, wherein: The determination unit acquires a traveling trajectory of the surrounding vehicle whose deviation angle from the extending direction of the second dividing line is greater than or equal to a predetermined angle, and determines whether the acquired traveling trajectory deviates from the second dividing line.

10. The determination device according to claim 4, wherein: The determination unit obtains a deviation direction of the driving tracks of the surrounding vehicles from the extending direction of the second dividing line, and determines whether a larger number of driving tracks having the same deviation direction deviate from the second dividing line.

11. The determination device according to claim 4, wherein: The determination unit obtains the deviation direction of the driving track of the surrounding vehicle relative to the extension direction of the second dividing line, and does not determine whether the driving track of the surrounding vehicle deviates from the second dividing line when the number of driving tracks with the same deviation direction is the same in multiple different directions.

12. A determination method, wherein: The determination method enables the computer to perform the following processing: identifying a surrounding condition including a first dividing line dividing a lane of the host vehicle and surrounding vehicles existing around the host vehicle based on an output of a detection device that detects a surrounding condition 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 the first dividing line deviates from the second dividing line; as well as The method of determining whether the first dividing line and the second dividing line deviate from each other is different between a case where the surrounding vehicle is recognized and a case where the surrounding vehicle is not recognized.

13. A storage medium, wherein: The storage medium stores a program that causes the computer to perform the following processing: identifying a surrounding condition including a first dividing line dividing a lane of the host vehicle and surrounding vehicles existing around the host vehicle based on an output of a detection device that detects a surrounding condition 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 the first dividing line deviates from the second dividing line; as well as The method of determining whether the first dividing line and the second dividing line deviate from each other is different between a case where the surrounding vehicle is recognized and a case where the surrounding vehicle is not recognized.

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