Determination device, determination method and program

Abstract

To determine correctness of a lane marking with improved appropriateness.SOLUTION: A determination device comprises: a first recognition section which recognizes a surrounding situation including a first lane marking partitioning a travel lane of an own vehicle and another vehicle around the own vehicle based on output of a detection device; a second recognition section which recognizes a second lane marking partitioning a travel lane around the own vehicle from map information based on positional information of the own vehicle; and a determination section which performs correctness determination whether or not at least one of the first lane marking and the second lane marking is correct based on at least the one of the first lane marking and the second lane marking and a travel trajectory of the other vehicle. When a first other vehicle and a second other vehicle recognized by the first recognition section perform a lateral move in front of the own vehicle and positions after the lateral move are different from each other by a predetermined distance or more, the determination section does not perform the correctness determination based on the first other vehicle and the second other vehicle.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a determination device, a determination method, and a program. [Background technology]

[0002] In recent years, efforts to provide access to sustainable transportation systems that take into consideration vulnerable traffic participants have been gaining momentum. To achieve this, efforts are being focused on research and development into autonomous driving technology to further improve traffic safety and convenience. In this regard, a technology has been known that, when it is determined that there is a discrepancy between road markings shown in a camera image (camera markings) and road markings shown in map information (map markings), controls the vehicle's driving mode based on the parallelism between the driving trajectories of other vehicles in the vicinity and the camera markings (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Publication No. 2023-148405 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in conventional automated driving technology, when determining whether camera-captured lane markings or map-captured lane markings are correct based on the driving trajectory of other vehicles, there is a possibility that it may not be possible to properly determine whether the other vehicle is driving along the lane markings or changing lanes. Therefore, there is a problem in that it may not be possible to properly determine whether the lane markings are correct based on the driving trajectory of other vehicles.

[0005] In order to solve the above-mentioned problems, one of the objects of the present application is to provide a determination device, a determination method, and a program that can more appropriately determine whether a lane marking is correct or not, depending on the lane markings around the vehicle and the driving conditions of other vehicles, thereby contributing to the development of a sustainable transportation system. [Means for solving the problem]

[0006] The determination device, the determination method, and the program according to the present invention employ the following configuration. (1): A determination device according to one embodiment of the present invention includes a first recognition unit that recognizes the surrounding conditions, including a first dividing line that divides the lane in which the host vehicle is traveling and other vehicles present around the host vehicle, based on the output of a detection device that detects the surrounding conditions of the host vehicle; a second recognition unit that recognizes a second dividing line that divides the lanes around the host vehicle from map information based on position information of the host vehicle; and 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 traveling trajectory of the other vehicle, and the determination unit does not make the determination based on the first other vehicle and the second other vehicle when a first other vehicle and a second other vehicle recognized by the first recognition unit move laterally in front of the host vehicle and the positions of the laterally moved differ by more than a predetermined distance.

[0007] (2): In the above aspect (1), the predetermined distance is set based on the width of the lane defined by the first dividing line or the width of the lane defined by the second dividing line.

[0008] (3) In the above aspect (1), the predetermined distance is set in accordance with the distance between the lateral positions of the first other vehicle and the second other vehicle before they move laterally.

[0009] (4): In the above aspect (1), the judgment unit makes the correct / incorrect judgment based on the third other vehicle when the first recognition unit recognizes a third other vehicle different from the first other vehicle and the second other vehicle, and the third other vehicle does not move laterally in the same direction as the first other vehicle and the second other vehicle, which have moved laterally at positions different by more than a predetermined distance.

[0010] (5): In the above aspect (1), when there is a point ahead of the host vehicle where the number of lanes on which the host vehicle can travel is reduced, and when the first other vehicle and the second other vehicle move laterally within a predetermined range before the point, the judgment unit makes the correct or incorrect judgment based on the first other vehicle and the second other vehicle.

[0011] (6): A determination method according to one embodiment of the present invention is a determination method in which a computer recognizes the surrounding conditions, including a first dividing line that divides the lane in which the host vehicle is traveling and other vehicles present around the host vehicle, based on the output of a detection device that detects the surrounding conditions of the host vehicle; recognizes a second dividing line that divides the lane in which the host vehicle is traveling from map information based on the position information of the host vehicle; 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 traveling trajectory of the other vehicle; and does not make the determination based on the first other vehicle and the second other vehicle if the recognized first other vehicle and second other vehicle move laterally in front of the host vehicle and the positions of the laterally moved differ by more than a predetermined distance.

[0012] (7): A program according to one embodiment of the present invention causes a computer to recognize the surrounding conditions, including a first dividing line that divides the lane in which the host vehicle is traveling and other vehicles present around the host vehicle, based on the output of a detection device that detects the surrounding conditions of the host vehicle; recognize a second dividing line that divides the lane in which the host vehicle is traveling from map information based on the position information of the host vehicle; determine 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 traveling trajectory of the other vehicle; and if the recognized first other vehicle and second other vehicle move laterally in front of the host vehicle and the positions of the laterally moved differ by more than a predetermined distance, the program does not cause the computer to determine whether the first other vehicle and second other vehicle are correct. [Effects of the Invention]

[0013] According to the above aspects (1) to (7), it is possible to more appropriately determine whether the marking lines are correct or not, depending on the marking lines around the vehicle and the traveling conditions of other vehicles. [Brief explanation of the drawings]

[0014] [Figure 1] 1 is a configuration diagram of a vehicle system 1 including a vehicle control device according to an embodiment. [Figure 2] 2 is a functional configuration diagram of a first control unit 120 and a second control unit 160. FIG. [Figure 3] FIG. 10 is a diagram for explaining the determination process in the first scene. [Figure 4] FIG. 10 is a diagram for explaining the determination process in the second scene. [Figure 5] FIG. 10 is a diagram for explaining the determination process in the third scene. [Figure 6] 3 is a flowchart illustrating an example of processing executed by the automatic driving control device 100 according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0015] Hereinafter, with reference to the drawings, embodiments of a determination device, a determination method, and a program according to the present invention will be described. Hereinafter, as an example, an embodiment will be described in which a vehicle control device including a determination device that determines whether a road dividing line (or lane) that divides a lane on which a vehicle is traveling is a correct dividing line (or lane) is applied to an autonomous vehicle. Autonomous driving refers to, for example, automatically controlling one or both of the steering and speed of a vehicle to perform driving control. The above-mentioned driving control may include, for example, an adaptive cruise control system (ACC), a traffic jam pilot (TJP), a lane keeping assistance system (LKAS), an automated lane change (ALC), a collision mitigation brake system (CMBS), and the like. Furthermore, autonomous vehicles may be manually controlled by a vehicle user (e.g., a passenger) (so-called manual driving). Although the following description will be given for a case where a law stipulates driving on the left side of the road, if a law stipulates driving on the right side of the road, the terms left and right may be reversed.

[0016] [Overall configuration] 1 is a configuration diagram of a vehicle system 1 including a vehicle control device according to an embodiment. The vehicle (hereinafter referred to as host vehicle M) on which the vehicle system 1 is mounted is, for example, a two-wheeled, three-wheeled, or four-wheeled vehicle, and its drive source is an internal combustion engine such as a diesel engine or a gasoline engine, an electric motor, or a combination of these. The electric motor operates using power generated by a generator connected to the internal combustion engine, or discharged power from a battery (storage battery) such as a secondary battery or a fuel cell.

[0017] The 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 operator 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 connected to each other via multiplexed communication lines such as a CAN (Controller Area Network) communication line, serial communication lines, a wireless communication network, etc. Note that the configuration shown in FIG. 1 is merely an example, and some of the configuration may be omitted, or other configurations may be added. The combination of the camera 10, the radar device 12, the LIDAR 14, and the object recognition device 16 is an example of a "detection device DD." The HMI 30 is an example of an "output device." The automatic driving control device 100 is an example of a "vehicle control device."

[0018] The camera 10 is a digital camera that uses a solid-state imaging element such as a CCD (Charge Coupled Device) or a CMOS (Complementary Metal Oxide Semiconductor). The camera 10 is attached to any location of the host vehicle M in which the vehicle system 1 is installed. When capturing an image of the front, the camera 10 is attached to the top of the front windshield, the back of the rearview mirror, the front of the vehicle body, etc. When capturing an image of the rear, the camera 10 is attached to the top of the rear windshield, the back door, etc. When capturing an image of the side, the camera 10 is attached to a door mirror, etc. The camera 10 periodically and repeatedly captures images of the surroundings of the host vehicle M, for example. The camera 10 may be a stereo camera.

[0019] The radar device 12 emits radio waves such as millimeter waves around the vehicle M and detects radio waves reflected by surrounding objects (reflected waves) to detect at least the position (distance and direction) of the objects. The radar device 12 is attached to any location on the vehicle M. The radar device 12 may detect the position and speed of the objects using an FM-CW (Frequency Modulated Continuous Wave) method.

[0020] The LIDAR 14 irradiates light around the vehicle M and measures the scattered light. The LIDAR 14 detects the distance to the target based on the time between emitting and receiving the light. The irradiated light is, for example, a pulsed laser beam. The LIDAR 14 is attached to any location on the vehicle M.

[0021] The object recognition device 16 performs sensor fusion processing on the detection results from some or all of the camera 10, the radar device 12, and the LIDAR 14 to recognize the position, type, speed, etc. of the object. The object recognition device 16 outputs the recognition results to the automatic driving control device 100. Alternatively, the object recognition device 16 may output the detection results from the camera 10, the radar device 12, and the LIDAR 14 directly to the automatic driving control device 100. In that case, the object recognition device 16 may be omitted from the configuration of the vehicle system 1 (detection device DD).

[0022] The communication device 20 communicates with, for example, other vehicles in the vicinity of the vehicle M, terminal devices of users using the vehicle M, or various server devices, using networks such as a cellular network, a Wi-Fi network, Bluetooth (registered trademark), DSRC (Dedicated Short Range Communication), LAN (Local Area Network), WAN (Wide Area Network), or the Internet.

[0023] The HMI 30 outputs various information to the occupants of the vehicle M and accepts input operations by the occupants. The HMI 30 includes, for example, various display devices, speakers, buzzers, touch panels, switches, keys, microphones, and the like.

[0024] The vehicle sensor 40 includes a vehicle speed sensor that detects the speed of the host vehicle M, an acceleration sensor that detects acceleration, a yaw rate sensor that detects the yaw rate (for example, the rotational angular velocity around a vertical axis passing through the center of gravity of the host vehicle M), and a direction sensor that detects the orientation of the host vehicle M. The vehicle sensor 40 may also be provided with a position sensor that detects the position of the vehicle. The position sensor is an example of a "position measurement unit." The position sensor is, for example, a sensor that acquires position information (longitude and latitude information) from a GPS (Global Positioning System) device. The position sensor may also be a sensor that acquires position information using a GNSS (Global Navigation Satellite System) receiver 51 of the navigation device 50. The vehicle sensor 40 may derive the speed of the host vehicle M from the difference (i.e., distance) of the position information at a predetermined time in the position sensor. The results detected by the vehicle sensor 40 are output to the automatic driving control device 100.

[0025] 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 a flash memory. The GNSS receiver 51 identifies the position of the vehicle M based on signals received from GNSS satellites. The position of the vehicle M may be identified or supplemented by an inertial navigation system (INS) that uses the output of the vehicle sensor 40. The navigation HMI 52 includes a display device, a speaker, a touch panel, keys, etc. The GNSS receiver 51 may be provided in the vehicle sensor 40. The navigation HMI 52 may share some or all of the components with the HMI 30 described above. The route determination unit 53 determines, for example, a route (hereinafter, a route on a map) from the position of the vehicle M identified by the GNSS receiver 51 (or an arbitrary input position) to a destination input by the occupant using the navigation HMI 52, with reference to the first map information 54. The first map information 54 is information in which road shapes are expressed by, for example, links indicating roads and nodes connected by the links. The first map information 54 may also include POI (Point Of Interest) information and the like. The route on the map is output to the MPU 60. The navigation device 50 may provide route guidance using the navigation HMI 52 based on the route on the map. The navigation device 50 may transmit the current position 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.

[0026] The MPU 60 includes, for example, a recommended lane determination unit 61, and stores second map information 62 in a storage device such as an HDD or flash memory. The recommended lane determination unit 61 divides the route on the map provided by the navigation device 50 into a plurality of blocks (for example, by dividing it into 100 m intervals in the vehicle 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, for example, which lane from the left the vehicle should travel in. When there is a branch point on the route on the map, the recommended lane determination unit 61 determines a recommended lane so that the vehicle M can travel on a reasonable route to the branch point.

[0027] 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 the center of lanes, and road boundary information. The second map information 62 may also include information on whether the road boundary includes a structure that prevents a vehicle from passing (including crossing or coming into contact with). Examples of such structures include guardrails, curbs, medians, and fences. The term "impassable" may include the presence of a low level step that allows passage if unusual vehicle vibrations are tolerated. The second map information 62 may also include road shape information, traffic regulation information, address information (address and postal code), facility information, parking information, telephone number information, and the like. The road shape information includes, for example, the curvature (which may be interpreted as the radius of curvature; the same applies below), width, and gradient of the road. The second map information 62 may be updated as needed by the communication device 20 communicating with an external device. The first map information 54 and the second map information 62 may be provided as an integrated piece of map information.

[0028] The driving operators 80 include, for example, a steering wheel, an accelerator pedal, and a brake pedal. The driving operators 80 may also include a shift lever, a variable steering wheel, a joystick, or other operators. Each operator of the driving operators 80 is equipped with an operation detection unit that detects, for example, the amount of operation of the operator by the occupant or whether or not the operator is operated. The operation detection unit detects, for example, the steering angle and steering torque of the steering wheel, the amount of depression of the accelerator pedal and the brake pedal, etc. The operation detection unit then outputs the detection results to the automatic driving control device 100 or one or both of the driving force output device 200, the brake device 210, and the steering device 220.

[0029] The automatic driving control device 100 executes various types of driving control associated with automatic driving for 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 realized by, for example, a hardware processor such as a CPU (Central Processing Unit) executing a program (software). Furthermore, some or all of these components may be realized by 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 an SOC (System On Chip), or may be realized by a combination of software and hardware. The above-mentioned program may be stored in advance in a storage device (a storage device with a non-transitory storage medium) such as an HDD or flash memory of the automatic driving control device 100, or may be stored in a removable storage medium such as a DVD, CD-ROM, or memory card, and installed in the storage device of the automatic driving control device 100 by inserting the storage medium (non-transitory storage medium) into a drive device, card slot, etc.

[0030] The storage unit 190 may be realized by the various storage devices described above, or an EEPROM (Electrically Erasable Programmable Read Only Memory), a ROM (Read Only Memory), or a RAM (Random Access Memory). The storage unit 190 stores, for example, various types of information, programs, and the like in the embodiments. The storage unit 190 may also store map information (for example, the first map information 54 and the second map information 62).

[0031] FIG. 2 is a functional configuration diagram of the first control unit 120 and the second control unit 160. The first control unit 120 includes, for example, a recognition unit 130 and an action plan generation unit 140. The first control unit 120, for example, implements functions based on AI (Artificial Intelligence) and functions based on a predefined model in parallel. For example, the function of "recognizing intersections" may be implemented by concurrently executing intersection recognition using deep learning or the like and recognition based on predefined conditions (such as the presence of traffic lights and road markings that can be pattern matched), and then scoring and comprehensively evaluating both. This ensures the reliability of autonomous driving. The first control unit 120 also executes control related to autonomous driving of the host vehicle M based on instructions from, for example, the MPU 60, the HMI control unit 180, or the like.

[0032] The recognition unit 130 recognizes the surrounding situation of the host vehicle M based on the recognition results of the detection device DD (information input from the camera 10, the radar device 12, and the LIDAR 14 via the object recognition device 16). For example, the recognition unit 130 recognizes the status of objects present around the host vehicle M (within a predetermined distance), such as their position, speed, and acceleration. The objects include, for example, other vehicles (surrounding vehicles), traffic participants (pedestrians, bicycles, etc.) traveling on the road, road structures, and other obstacles present in the vicinity. The road structures include, for example, road signs, traffic signals, railroad crossings, curbs, medians, guardrails, fences, etc. The position of the object is recognized as a position on an absolute coordinate system with a representative point of the host vehicle M (such as the center of gravity or the center of the drive shaft) as the origin, and is used for control. The position of the object may be represented by a representative point such as the center of gravity or a corner of the object, or by a represented area. The "state" of an object may include, for example, if the object is a moving object such as another vehicle, the acceleration or jerk of the moving object, or the "behavioral state" (for example, whether the other vehicle is changing lanes or is about to change lanes).

[0033] The recognition unit 130 includes, for example, a first recognition unit 132 and a second recognition unit 134. The functions of these units will be described in detail later.

[0034] The behavior plan generation unit 140 generates a behavior plan for driving the host vehicle M by autonomous driving based on the recognition results of the recognition unit 130, etc. For example, the behavior plan generation unit 140 generates a target trajectory for the host vehicle M to travel automatically (without driver operation) in the future, so that the host vehicle M can respond to the surrounding conditions of the host vehicle M, while essentially traveling in the recommended lane determined by the recommended lane determination unit 61, based on the recognition results by the recognition unit 130 and the surrounding road shapes based on the current position of the host vehicle M acquired from map information, etc. The target trajectory includes, for example, a speed element. For example, the target trajectory is expressed as a sequential arrangement of points (trajectory points) to be reached by the host vehicle M. The trajectory points are points to be reached by the host vehicle M at predetermined travel distances (e.g., on the order of several meters) along the road, and separately, target speeds and target accelerations for predetermined sampling times (e.g., on the order of a few tenths of a second) are generated as part of the target trajectory. Alternatively, the trajectory points may be positions to be reached by the host vehicle M at the sampling times for each predetermined sampling time. In this case, the target speed and target acceleration information are expressed as the interval between trajectory points.

[0035] The action plan generation unit 140 may set an autonomous driving event when generating the target trajectory. Examples of the event include a constant speed driving event in which the host vehicle M drives in the same lane at a constant speed, a following driving event in which the host vehicle M follows another vehicle that is within a predetermined distance (for example, within 100 m) ahead of the host vehicle M and is closest to the host vehicle M, a lane change event in which the host vehicle M changes lanes from the host vehicle's own lane to an adjacent lane, a branching event in which the host vehicle M branches off into a lane on the destination side at a road branching point, a merging event in which the host vehicle M merges into a main lane at a merging point, a takeover event in which the autonomous driving is terminated and the host vehicle M switches to manual driving, and so on. Examples of the event may also include an overtaking event in which the host vehicle M temporarily changes lanes to an adjacent lane, overtakes a leading vehicle in the adjacent lane, and then changes lanes back to the original lane, and an avoidance event in which the host vehicle M performs at least one of braking and steering to avoid an obstacle ahead of the host vehicle M.

[0036] Furthermore, the behavior plan generation unit 140 may change an event already determined for the current section to another event or set a new event for the current section, depending on the surrounding conditions of the host vehicle M recognized while the host vehicle M is traveling. Furthermore, the behavior plan generation unit 140 may change an event already set for the current section to another event or set a new event for the current section, depending on the operation of the occupant on the HMI 30. The behavior plan generation unit 140 generates a target trajectory according to the set event.

[0037] The behavior plan generation unit 140 also 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 an example of a "determination device." The execution control unit 144 and the second control unit 160 are an example of a "driving control unit."

[0038] The second control unit 160 controls the traveling 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 the scheduled time.

[0039] 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 points) 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 brake device 210 based on a speed element associated with the target trajectory stored in the memory. The steering control unit 166 controls the steering device 220 according to the curvature of the target trajectory stored in the memory. The processing of the speed control unit 164 and the steering control unit 166 is realized by, for example, a combination of feedforward control and feedback control. As an example, the steering control unit 166 executes a combination of feedforward control according to the curvature of the road ahead of the host vehicle M and feedback control based on the deviation from the target trajectory.

[0040] Returning to FIG. 1 , the HMI control unit 180 notifies the occupant of predetermined information via the HMI 30. The predetermined information includes, for example, information related to the traveling of the vehicle M, such as information related to the state of the vehicle M and information related to driving control. The information related to the state of the vehicle M includes, for example, the speed of the vehicle M, engine speed, and shift position. The information related to driving control includes, for example, whether or not driving control is being performed by autonomous driving, information inquiring about whether or not to start autonomous driving, information related to the driving control status by autonomous driving, information related to the automation level, and information prompting the occupant to drive when switching from autonomous driving to manual driving. The predetermined information may also include information unrelated to the traveling of the vehicle M, such as television programs and content (e.g., movies) stored on a storage medium such as a DVD. The predetermined information may also include, for example, information related to the current location and destination during autonomous driving, and the remaining amount of fuel in the vehicle M. The HMI control unit 180 may output the information received by the HMI 30 to the communication device 20, the navigation device 50, the first control unit 120, etc.

[0041] Furthermore, the HMI control unit 180 may cause the HMI 30 to output inquiry information for the occupant, processing results by the first control unit 120 and the second control unit 160, etc. Furthermore, the HMI control unit 180 may transmit various pieces of information to be output by the HMI 30 to a terminal device used by the user of the vehicle M via the communication device 20.

[0042] Traveling drive force output device 200 outputs a traveling drive force (torque) to the drive wheels for the vehicle to travel. Traveling drive force output device 200 includes, for example, a combination of an internal combustion engine, an electric motor, a transmission, etc., and an ECU (Electronic Control Unit) that controls these. The ECU controls the above components according to information input from second control unit 160 or information input from the accelerator pedal of driving operator 80.

[0043] The braking device 210 includes, for example, a brake caliper, a cylinder that transmits hydraulic pressure to the brake caliper, an electric motor that generates hydraulic pressure in the cylinder, and a brake ECU. The brake ECU controls the electric motor according to information input from the second control unit 160 or information input from the brake pedal of the driving operation device 80, so that a braking torque corresponding to the braking operation is output to each wheel. The braking device 210 may include a backup mechanism that transmits hydraulic pressure generated by operation of the brake pedal to the cylinder via a master cylinder. Note that the braking device 210 is not limited to the configuration described above, and may also be an electronically controlled hydraulic braking device that controls an actuator according to information input from the second control unit 160 to transmit hydraulic pressure from the master cylinder to the cylinder.

[0044] The steering device 220 includes, for example, a steering ECU and an electric motor. The electric motor changes the direction of the steered wheels by, for example, applying a force to a rack and pinion mechanism. The steering ECU drives the electric motor to change the direction of the steered wheels in accordance with information input from the second control unit 160 or information input from the steering wheel of the driving operator 80.

[0045] [Recognition and Action Plan Generation] Next, the functions of the recognition unit 130 (first recognition unit 132, second recognition unit 134) and the behavior plan generation unit 140 (determination unit 142, execution control unit 144) will be described in detail. Note that the following mainly describes the determination process in the embodiment and the contents of driving control (cruising control) based on the determination result, and the determination process will be explained in several stages.

[0046] [Scene 1] FIG. 3 is a diagram illustrating the determination process in the first scenario. The example in FIG. 3 shows lane markings CL1 and CL2 recognized by the detection device DD and lane markings ML1 to ML3 obtained from map information (e.g., second map information 62) based on the position information of the host vehicle M. In the map information, lane L1 is defined by lane markings ML1 and ML2, and lane L2 is defined by lane markings ML2 and ML3. Lanes L1 and L2 are lanes along which vehicles can travel in the same direction (the X-axis direction in the figure). In the example in FIG. 3, lane markings CL1 to CL2 are an example of a "first lane marking," and lane markings ML1 to ML3 are an example of a "second lane marking." Also, in FIG. 3, the host vehicle M is traveling on lane L1 at a speed VM, another vehicle m1 is traveling ahead of the host vehicle M at a speed Vm1, and another vehicle m2 is traveling ahead of the other vehicle m1 (host vehicle M) at a speed Vm2. The other vehicle m1 is an example of a "first other vehicle," and the other vehicle m2 is an example of a "second other vehicle."

[0047] The first recognition unit 132 recognizes the surrounding conditions of the host vehicle M based on the output of the detection device DD, which detects the surrounding conditions of the host vehicle M. For example, the first recognition unit 132 recognizes left and right lane markings CL1 and CL2 that define the lane (lane L1) of the host vehicle M based on an image captured by the camera 10 (hereinafter, referred to as a camera image). The first recognition unit 132 may also recognize a lane marking that defines an adjacent lane (lane L2) adjacent to the lane. Hereinafter, the lane markings CL1 and CL2 may be referred to as "camera lane markings CL1 and CL2." For example, the first recognition unit 132 analyzes the camera image, extracts edge points in the image that have a large difference in brightness from adjacent pixels, and recognizes the camera lane markings CL1 and CL2 on the image plane by connecting the edge points. The first recognition unit 132 also converts the positions of the camera lane markings CL1 and CL2 based on the position of the representative point of the host vehicle M into a vehicle coordinate system (e.g., the XY plane coordinates in FIG. 3). The first recognition unit 132 may also recognize the curvature or curvature change amount of each of the camera lane markings CL1 and CL2. The curvature change amount is, for example, the time rate of change in the curvature of the camera lane markings CL1 and CL2 recognized by the camera 10 at a distance X [m] ahead as viewed from the host vehicle M. The first recognition unit 132 may also recognize the curvature or curvature change amount of the lane markings CL1 and CL2 by averaging the curvatures or curvature change amounts of each of the camera lane markings CL1 and CL2. The camera lane markings CL1 and CL2 may be recognized or corrected based on the output of a detection device other than the camera 10.

[0048] The first recognition unit 132 also recognizes other vehicles present in the vicinity (within a predetermined distance) of the host vehicle M. For example, the first recognition unit 132 recognizes other vehicles m1 and m2 present ahead of 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 also recognizes the positions (relative positions with respect to the host vehicle M) and speeds (relative speeds with respect to the host vehicle M) of the other vehicles m1 and m2. The first recognition unit 132 may also recognize traveling position information of the other vehicles m1 and m2. The traveling position information is, for example, traveling trajectories K1 and K2 based on the positions of representative points of the other vehicles m1 and m2 at a predetermined time while they are traveling. The traveling position information may also include, for example, information on the predicted future traveling trajectories of the other vehicles m1 and m2 based on the traveling trajectories K1 and K2 and the orientations of the other vehicles m1 and m2.

[0049] The second recognition unit 134 recognizes lane markings around (within a predetermined distance from) the host vehicle M from map information based on the position of the host vehicle M detected by, for example, the vehicle sensor 40 or the GNSS receiver 51. For example, the second recognition unit 134 refers to map information based on the position information of the host vehicle M, and recognizes lane markings ML1 to ML3 that exist in the direction in which the host vehicle M is traveling or in which the host vehicle M can travel. Hereinafter, the lane markings ML1 to ML3 may be referred to as "map lane markings ML1 to ML3."

[0050] Furthermore, the second recognition unit 134 may recognize, among the recognized map division lines ML1 to ML3, map division lines ML1 and ML2 as division lines that divide the lane L1 on which the host vehicle M is traveling. Furthermore, the second recognition unit 134 recognizes the curvature or curvature change amount of each of the map division lines ML1 to ML3 from the map information. Furthermore, the second recognition unit 134 may average the curvature or curvature change amount of each of the map division lines ML1 to ML3 to recognize the curvature or curvature change amount for each of the lanes L1 and L2 divided by the map division lines.

[0051] The determination unit 142 determines whether at least one of the camera lane lines CL (CL1, CL2) and the map lane lines ML (ML1-ML3) is correct, for example, based on at least one of the camera lane lines CL and the map lane lines ML and the travel trajectories K1 and K2 of the other vehicles m1 and m2. The execution control unit 144 generates a target trajectory for performing driving control so that the host vehicle M travels along the lane lines determined to be correct based on the determination result by the determination unit 142, or performs control to end driving control (or not start driving control) if both lane lines are determined to be incorrect.

[0052] In the example of FIG. 3, the determination unit 142 first determines whether the camera lane markings CL (CL1, CL2) recognized by the first recognition unit 132 and the map lane markings ML (ML1, ML2) recognized by the second recognition unit 134 diverge from each other as a success or failure determination. For example, the determination unit 142 derives the degree of deviation between the lane markings CL1 and ML1 located closest to the left of the host vehicle M, and the degree of deviation between the lane markings CL2 and ML2 located closest to the right of the host vehicle M. If the derived degree of deviation is equal to or greater than a threshold, the determination unit 142 determines that the camera lane markings CL and the map lane markings ML diverge from each other; if the derived degree of deviation is less than the threshold, the determination unit 142 determines that there is no deviation. The above-described deviation determination is repeatedly performed at a predetermined timing or period.

[0053] For example, the determination unit 142 superimposes the camera lane lines CL1 and CL2 and the map lane lines ML1 and ML2 on the plane (XY plane) of the vehicle coordinate system, based on the position of the representative point of the vehicle M. When determining the deviation between the compared lane lines (lane lines CL1 and ML1, and lane lines CL2 and ML2), the determination unit 142 determines that the lane lines deviate if the deviation degree of each lane line is equal to or greater than a threshold, and determines that the lane lines do not deviate if the deviation degree is less than the threshold. The deviation degree is, for example, the degree of deviation in lateral position (e.g., the Y-axis direction in the figure). Note that in the example of FIG. 3, the deviation degree may be the deviation amount D1 of the lateral position between lane lines CL1 and ML1 and the deviation amount D2 of the lateral position between lane lines CL2 and ML2, or the average, maximum, or minimum value of the deviation amounts D1 and D2.

[0054] Furthermore, the degree of deviation may be, for example, the degree (magnitude) of the angle formed by the two marking lines being compared, instead of (or in addition to) the amount of lateral deviation described above. In the example of Fig. 3, the angle θ1 formed by marking lines CL1 and ML1 and the angle θ2 formed by marking lines CL2 and ML2 may each be the degree of deviation, or the average, maximum, or minimum value of the angles θ1 and θ2 may be the degree of deviation.

[0055] Furthermore, the degree of deviation may be the degree (magnitude) of the difference in curvature change between the lane lines, instead of (or in addition to) the lateral position deviation or the angle formed by the lane lines. The curvature change is mainly used when the lane is a curved road. The determination unit 142 may use the average value of the difference in curvature change between the lane lines CL1 and ML1 and the difference in curvature change between the lane lines CL2 and ML2, or may use the maximum or minimum value of the difference. The determination unit 142 may also use the difference between the average value of the curvature change between the lane lines CL1 and CL2 and the average value of the curvature change between the lane lines ML1 and ML2. The determination unit 142 may also use the difference between the curvature change of the lane (lane L1) recognized from the camera image and the curvature change of the lane recognized from map information.

[0056] Furthermore, for example, if the recognition accuracy of the camera lane lines CL1 and CL2 recognized by the first recognition unit 132 falls below a threshold or the camera lane lines CL1 and CL2 cannot be recognized, the determination unit 142 may derive the degree of deviation using the angle between the travel trajectories K1 and K2 of other vehicles traveling in the vicinity and the map lane lines ML. The determination unit 142 may also set virtual lane lines parallel to the travel trajectories K1 and K2 and determine the degree of deviation between the set virtual lane lines and the map lane lines ML. The determination unit 142 may also determine the deviation from the lane lines using the travel trajectories K1 and K2 regardless of the recognition results of the camera lane lines CL. Similarly, if the camera lane lines CL are recognized but the surrounding map lane lines ML cannot be recognized from the map information, the determination unit 142 may determine the deviation between the camera lane lines CL and the travel trajectories K1 and K2 and determine whether the camera lane lines CL are correct based on the determination result. Furthermore, the determination unit 142 may determine the deviation between the camera lane markings CL and the traveling trajectories K1 and K2 regardless of the recognition result of the map lane markings ML.

[0057] If the deviation determination using the deviation degree described above determines that the camera lane line CL and the map lane line ML do not deviate, the determination unit 142 determines that the camera lane line CL and the map lane line ML are correct lane lines in the correct / incorrect determination. Furthermore, if the determination unit 142 determines that the camera lane line CL and the map lane line ML deviate, the determination unit 142 determines that at least one of the camera lane line CL and the map lane line ML is incorrect. For example, if the camera lane line CL and the map lane line ML deviate and the host vehicle M is performing driving control to avoid an obstacle ahead, the determination unit 142 determines that the camera lane line CL is incorrect (or the map lane line ML is correct).

[0058] Furthermore, when the camera lane line CL and the map lane line ML deviate from each other and a predetermined number or more of the recognized travel trajectories of the other vehicles are travel trajectories that follow the map lane line ML (including within a predetermined tolerance range), the determination unit 142 determines that the camera lane line CL is incorrect (or the map lane line ML is correct). Furthermore, when the camera lane line CL and the map lane line ML deviate from each other and a predetermined number or more of the recognized travel trajectories of the other vehicles are travel trajectories that follow the camera lane line CL (including within a predetermined tolerance range), the determination unit 142 determines that the map lane line ML is incorrect (or the camera lane line CL is correct).

[0059] Furthermore, if the first recognition unit 132 recognizes information indicating a lane change (for example, an increase or decrease in lanes), such as a road construction sign or a road sign indicating an increase or decrease in lanes, but the road information acquired from the map information does not contain information indicating a lane change, the determination unit 142 may determine that the map lane markings are incorrect (or that the camera lane markings are correct) because the map information is outdated (or does not match the current road shape). Furthermore, the determination unit 142 may determine that the camera lane markings and map lane markings are incorrect, for example, if the degree of deviation is equal to or greater than an upper limit value that is greater than a threshold, or if the number of lane markings between the camera lane markings and the map lane markings differ.

[0060] Here, for example, when the other vehicles m1 and m2 recognized by the first recognition unit 132 move laterally in front of the host vehicle M and the positions of the laterally moved differ by a predetermined distance or more, the determination unit 142 does not perform a correct / incorrect determination based on the other vehicles m1 and m2. Note that instead of not performing a correct / incorrect determination, the determination unit 142 may not determine that at least one of the camera lane markings CL and the map lane markings ML is correct. The lateral movement is, for example, a movement of the map lane markings ML in the road width direction by a threshold or more (movement in the Y-axis direction in FIG. 3). The threshold may be, for example, the width of one lane corresponding to a lane change (e.g., the width W1 of the lane L1) or a fixed distance. The lateral movement may also be a movement of, for example, a threshold or more in a direction perpendicular to the traveling direction of the host vehicle M (in other words, the vehicle width direction of the host vehicle M). Furthermore, lateral movement may refer to the reference positions (e.g., center of gravity, center, tip) of the other vehicles m1, m2, the entire vehicle body, or the travel trajectories K1, K2 crossing (this may also be referred to as "straddling" or "passing through") a lane marking (e.g., map marking ML) that marks the lane in which the vehicle is traveling. The predetermined distance may be set, for example, based on the width W1 of the lane L1 in which the host vehicle M is traveling, or may be a fixed distance. The width W1 may be, for example, the width of the lane marked by the camera marking lines CL1, CL2, or the width of the lane marked by the map marking lines ML1, ML2.

[0061] In the example of FIG. 3, the determination unit 142 acquires positions P1 and P2 where the other vehicles m1 and m2 have moved laterally, where the other vehicles have crossed the map division line ML2, and acquires the distance TD1 between the acquired positions P1 and P2. The distance TD1 is, for example, the distance in the extension direction of the map division line ML2 (or the traveling direction of the host vehicle M). If the distance TD1 is equal to or greater than a predetermined distance (for example, the width W1, or the width W1 plus a predetermined value α), the determination unit 142 determines that the other vehicles m1 and m2 have changed lanes. For example, suppose the other vehicles m1 and m2 were traveling in the same lane (for example, lane L1) and then moved laterally. If the distance TD1 at that time is equal to or greater than the width W1 of lane L1, it is highly likely that the other vehicles m1 and m2 are not traveling in the same lane. Therefore, when the above conditions are satisfied, the determination unit 142 can accurately determine that the other vehicles m1 and m2 are changing lanes, and by not making the determination based on the other vehicles m1 and m2 that are changing lanes, the determination unit 142 can more appropriately determine the correctness of the lane markings. Also, the determination accuracy of the road shape can be improved.

[0062] Note that the predetermined distance may be set based on the width W1 of the travel lane L1 as described above, but may also be a distance corresponding to the lateral position distance TD2 between the other vehicles m1 and m2 at the same point before they move laterally, based on the travel trajectories K1 and K2, as shown in FIG. 3. For example, the determination unit 142 increases the predetermined distance as the lateral position distance TD2 increases. For example, if the other vehicles m1 and m2 are traveling in different lateral positions (e.g., positions that differ by one lane in a typical road) before they move laterally, the determination unit 142 sets the predetermined distance to, for example, a distance equivalent to two lanes in a typical road. This makes it possible to accurately determine that the other vehicles m1 and m2 are changing lanes, even when one of the other vehicles is traveling in an adjacent lane adjacent to the travel lane of the other vehicle.

[0063] [Second Scene] FIG. 4 is a diagram for explaining the determination process in the second scene. The example of FIG. 4 differs from the first scene shown in FIG. 3 in that another vehicle m3 is present in addition to the other vehicles m1 and m2. The other vehicle m3 is an example of a "third other vehicle." In the example of FIG. 4, the other vehicle m3 is traveling ahead of the host vehicle M on the lane L1 at a speed Vm3. In the second scene, the first recognition unit 132 recognizes the position (relative position with respect to the host vehicle M), speed (relative speed with respect to the host vehicle M), traveling direction, and traveling position information (for example, traveling trajectory K3) of the other vehicle m3 in addition to the other vehicles m1 and m2.

[0064] 4, the determination unit 142 may perform a correct / incorrect determination based on the other vehicle m3 when the first recognition unit 132 recognizes a vehicle m3 different from the other vehicles m1 and m2 moving laterally in the vicinity (forward) of the host vehicle M and the recognized other vehicle m3 is not moving laterally in the same direction (a direction including a predetermined tolerance range) as the other vehicles m1 and m2 that moved laterally at positions different by a predetermined distance or more. In this case, the determination unit 142 performs a correct / incorrect determination of the lane markings based on the degree of deviation between the travel trajectory K3 of the other vehicle m3 and at least one of the camera lane markings CL and the map lane markings ML.

[0065] Furthermore, since it can be predicted that the other vehicles m1 and m2, which have moved laterally at positions different by more than a predetermined distance, are changing lanes (their traveling trajectories K1 and K2 are not aligned with the camera lane lines CL and the map lane lines ML), the determination unit 142 may determine that the traveling trajectory K3 of the other vehicle m3 is aligned with at least one of the camera lane lines CL and the map lane lines ML. Note that in the example of FIG. 4, the camera lane lines CL1 and CL2 near the other vehicle m3 cannot be recognized, so the determination unit 142 determines that the traveling trajectory K3 is aligned with the map lane lines ML1 and ML2. In this case, the determination unit 142 may also determine that the map lane lines ML1 and ML2 are correct.

[0066] This makes it possible to more appropriately determine whether the lane markings are correct or not based on the relationship between the travel trajectories K1 to K3 of the other vehicles m1 to m3. Also, as shown in Figure 4, even if there are two other vehicles m1 and m2 moving in the same direction and one other vehicle m3 traveling in a different direction from the other vehicles m1 and m2, it is possible to accurately determine the road shape based on the other vehicle m3 even if it is the minority, without determining that the majority is correct.

[0067] [Scene 3] Fig. 5 is a diagram for explaining the determination process in the third scene. The example of Fig. 5 differs from the first scene shown in Fig. 3 in that an obstacle OB1, such as a construction site, is present ahead of the host vehicle M. The obstacle OB1 may be a construction site, a parked vehicle, an accident vehicle, or another object that the host vehicle M cannot travel in the same lane as (and must avoid). A section where such an obstacle exists is an example of a point where the number of drivable lanes decreases (a point where the number of lanes decreases).

[0068] In a third scenario, the first recognition unit 132 recognizes an obstacle OB1 ahead of the host vehicle M based on the recognized surrounding conditions. For example, the first recognition unit 132 may recognize an obstacle OB1, such as a construction site, from road signs or billboards ahead of the host vehicle M recognized from a camera image, or may recognize an obstacle OB1, such as a parked vehicle, through object detection by the detection device DD. The first recognition unit 132 may also recognize a lane in which the obstacle OB1 exists as an impassable lane and a predetermined range from the position of the obstacle OB1 as a point where the number of lanes decreases. The first recognition unit 132 may also communicate with an external device via the communication device 20 and acquire from the external device, based on the position information and traveling direction of the host vehicle M, information that an obstacle (e.g., an accident vehicle or a construction site) or a point where the number of lanes decreases is present ahead of the host vehicle M (in the traveling direction).

[0069] In a third scenario, when a lane narrowing point exists ahead of the host vehicle M and the other vehicles m1 and m2 move laterally within a predetermined range before the lane narrowing point as viewed from the host vehicle M, the determination unit 142 performs a correct / incorrect determination based on the other vehicles m1 and m2. For example, when a lane narrowing point, such as a construction site, exists ahead, the distance before the point at which the other vehicles change lanes varies depending on the driver's preference, resulting in variations in the position of the lateral movement of each other vehicle. However, in this case, if the correct / incorrect determination is not performed based on the other vehicles, there is a possibility that the vehicle will enter the construction site. Therefore, when the other vehicles move laterally within a predetermined range from the lane narrowing point, as in the third scenario, more appropriate driving control can be performed according to the situation by performing a correct / incorrect determination based on the other vehicles m1 and m2 regardless of the position of the lateral movement.

[0070] The point where the number of lanes decreases may be a point where the road shape has a decrease in the number of lanes, regardless of the presence or absence of an obstacle, as well as a point where the number of lanes decreases, regardless of the presence or absence of an obstacle. In this case, the point where the number of lanes decreases may be recognized by the first recognition unit 132, or may be recognized by the second recognition unit 134 from map information.

[0071] [About operation control] The execution control unit 144 determines driving control for the host vehicle M based on the determination result by the determination unit 142, and executes the determined driving control. "Determining driving control" may include, for example, determining the content (type) of driving control and determining whether to execute (suppress) driving control. "Executing driving control" may include, for example, switching the content of driving control and executing it, as well as continuing driving control that is already being executed. "Suppressing driving control" may include not only not executing (terminating) driving control, but also lowering the automation level of driving control. Furthermore, the driving control executed by the execution control unit 144 may include ACC, TJP, LKAS, ALC, CMBS, etc., and may also include various other driving controls for avoiding contact with surrounding vehicles. The execution control unit 144 generates a target trajectory for executing driving control and outputs the generated target trajectory to the second control unit 160.

[0072] Here, 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 is, for example, driving control that executes at least steering control of the steering or speed of the host vehicle M based on a lane marking recognized by the first recognition unit 132 or the second recognition unit 134 (e.g., a portion of the lane marking that does not deviate from the camera lane marking). For example, the first driving control is driving control that causes the host vehicle M to travel so that a representative point of the host vehicle M passes through the center of a lane marked by lane markings. The second driving control is, for example, driving control that executes at least steering control of the steering or speed of the host vehicle M based on map lane markings and traveling position information of other vehicles. For example, the second driving control is driving control that causes the host vehicle M to travel so that a representative point of the host vehicle M travels on a trajectory that follows the traveling trajectory of the other vehicle m1.

[0073] Furthermore, the driving control may include a third driving control in which at least steering control of the steering or speed of the host vehicle M is performed while giving priority to camera lane lines over map lane lines, and a fourth driving control in which at least steering control of the steering or speed of the host vehicle M is performed while giving priority to map lane lines over camera lane lines. Prioritizing camera lane lines over map lane lines means, for example, that processing is basically performed based on the camera lane lines, but temporarily switches to processing based on the map lane lines when, for example, the recognition accuracy of the camera lane lines falls below a threshold or they become unrecognizable. Prioritizing map lane lines over camera lane lines means, for example, that processing is basically performed based on the map lane lines, but temporarily switches to processing based on the camera lane lines when, for example, the map lane lines cannot be identified. The third driving control and the fourth driving control are driving controls performed, for example, when the camera lane lines and the map lane lines are separated from each other.

[0074] Furthermore, the driving control may include multiple driving control levels based on automation levels (an example of the degree of automation). The automation levels may include, for example, a first level, a second level with a lower degree of automation of driving control than the first level, and a third level with a lower degree of automation of driving control than the second level. The automation levels may also include a fourth level (an example of a fourth control level) with a lower degree of automation of driving control than the third level. Here, the automation level may be a level determined by standardized information, laws and regulations, or an index value set independently of such. Therefore, the types, contents, and number of automation levels are not limited to the following examples. A low degree of automation of driving control means, for example, a low automation rate in driving control and a large (heavy) task imposed on the driver. A low degree of automation of driving control means a low degree of control of the steering or acceleration / deceleration of the host vehicle M by the automatic driving control device 100 (a high degree of need for the driver to intervene in steering or acceleration / deceleration operations). Tasks assigned to the driver include, for example, monitoring the surroundings of the vehicle M and operating driving controls. Operating driving controls includes, for example, the driver gripping the steering wheel (hereinafter referred to as a hands-on state). Tasks assigned to the driver include, for example, tasks for the occupant (driver tasks) required to maintain the autonomous driving of the vehicle M. Therefore, if the occupant is unable to perform the assigned tasks, the automation level will be reduced. For example, the first level of driving control may include driving controls such as ACC, ALC, LKAS, and TJP. Furthermore, 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. Furthermore, the fourth level of driving control may include, for example, driving controls such as ACC. Of the first to fourth levels, the first level has the highest degree of automation of driving control, and the fourth level has the lowest degree of automation of driving control.

[0075] Furthermore, at the first level, no tasks are assigned to the occupant (the driver has the lightest tasks). At the second level, the task assigned to the occupant is, for example, monitoring the surroundings (particularly ahead) of the vehicle M. At the third level, the task assigned to the occupant includes, for example, monitoring the surroundings of the vehicle M as well as being in a hands-on state. At the fourth level, the task assigned to the occupant (e.g., the driver) is, for example, monitoring the surroundings of the vehicle M and being in a hands-on state, as well as operating the driving operator 80 to control the steering and speed of the vehicle M. In other words, at the fourth level, the occupant is ready to immediately take over driving, and the driver has the most severe tasks. The content of driving control and the tasks assigned to the occupant at each automation level are not limited to the examples described above. The automatic driving control device 100 executes driving control at one of the first to fourth levels based on the surrounding conditions of the vehicle M and the tasks being performed by the occupant. At least some of the first to fourth levels may be associated with the above-mentioned first to fourth operational controls, for example.

[0076] For example, if the determination unit 142 determines that both the camera lane markings CL and the map lane markings ML are correct (for example, the camera lane markings CL and the map lane markings ML do not deviate from each other), the execution control unit 144 generates a target trajectory for executing a first driving control. Furthermore, if the determination unit 142 determines that one of the camera lane markings CL and the map lane markings ML is correct, the execution control unit 144 generates a target trajectory for executing one of the second to fourth driving controls based on the correct lane marking. Furthermore, based on the determination result, the execution control unit 144 may perform control such as terminating driving control of the host vehicle M and switching to manual driving by the occupant. Furthermore, based on the determination result, the execution control unit 144 may switch the automation level corresponding to the driving control. In this case, for example, if the camera lane markings CL and the map lane markings ML are determined to be correct, a first level driving control is executed, and if they are determined to be incorrect, a second level driving control is executed, depending on the situation. [Processing flow] The following describes the processing executed by the automatic driving control device 100 of the embodiment. FIG. 6 is a flowchart showing an example of the processing executed by the automatic driving control device 100 of the embodiment. The following mainly describes the processing executed by the automatic driving control device 100, focusing on the process of determining whether at least one of the camera lane markings CL and the map lane markings ML is correct. The automatic driving control device 100 executes driving control of the host vehicle M in accordance with the result of the determination process shown in FIG. 6. The processing described below may be repeatedly executed at a predetermined timing or at a predetermined cycle, and may be repeatedly executed while automatic driving by the automatic driving control device 100 is being performed.

[0077] 6, the first recognition unit 132 recognizes the lane markings (camera lane markings) present around 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 recognizes other vehicles present around the host vehicle M (step S110). Next, the second recognition unit 134 refers to map information based on the position information of the host vehicle M, and recognizes the lane markings (map lane markings) present around the host vehicle M from the map information (step S120).

[0078] Next, the determination unit 142 determines whether or not a first other vehicle and a second other vehicle are present ahead of the host vehicle M (step S130). If it is determined that the first other vehicle and the second other vehicle are present, the determination unit 142 determines whether or not the first other vehicle and the second other vehicle have moved laterally (step S140). Note that the determination unit 142 may determine whether or not the first other vehicle and the second other vehicle are present in the processing of step S130, and if it is determined that the first other vehicle and the second other vehicle are present, determine whether or not the first other vehicle and the second other vehicle have moved laterally ahead of the host vehicle M in the processing of step S140.

[0079] If it is determined that the first other vehicle and the second other vehicle have moved laterally, the determination unit 142 determines whether the positions to which the first other vehicle and the second other vehicle have moved laterally differ by a predetermined distance or more (step S150). If it is determined that the positions differ by a predetermined distance or more, the determination unit 142 does not determine the accuracy of the lane markings (at least one of the camera lane markings and the map lane markings) based on the first other vehicle and the second other vehicle (step S160). In this case, the determination unit 142 may determine the accuracy of the lane markings based on, for example, the travel trajectory of a vehicle (e.g., a third other vehicle) other than the first other vehicle and the second other vehicle that has not moved laterally, or may determine that both the camera lane markings and the map lane markings are incorrect.

[0080] Furthermore, if the processing of step S130 determines that the first other vehicle and the second other vehicle are not present ahead of the host vehicle M, if the processing of step S140 determines that the first other vehicle and the second other vehicle have not moved laterally, or if the processing of step S150 determines that the positions of the laterally moved vehicles do not differ by more than a predetermined distance, the determination unit 142 determines whether the lane markings are correct based on the lane markings (at least one of the camera lane markings and the map lane markings) and the travel trajectories of the other vehicles (step S170). For example, if the position of the laterally moved vehicle is less than the predetermined distance in the processing of step S150, the determination unit 142 determines whether the map lane markings are incorrect because the degree of deviation is greater than or equal to a threshold. This ends the processing of this flowchart.

[0081] [Variations] In the embodiment, for example, the determination unit 142 may determine that the first other vehicle and the second other vehicle have changed lanes when the first other vehicle and the second other vehicle recognized by the first recognition unit 132 have moved laterally in front of the host vehicle M and the positions of the laterally moved vehicles differ by a predetermined distance or more. Furthermore, the determination unit 142 may determine that the map division line is correct when it is determined that the vehicles have changed lanes.

[0082] According to the above-described embodiment, the determination device (recognition unit 130, determination unit 142) includes a first recognition unit 132 that recognizes the surrounding situation including camera demarcation lines (first demarcation lines) that demarcate the lane in which the host vehicle M is traveling and other vehicles present around the host vehicle M based on the output of a detection device DD that detects the surrounding situation of the host vehicle M, a second recognition unit 134 that recognizes camera demarcation lines (second demarcation lines) that demarcate the lanes around the host vehicle M from map information based on the position information of the host vehicle M, and at least one of the camera demarcation lines and the map demarcation lines, and a determination unit 142 that determines whether at least one of the camera lane markings and the map lane markings is correct based on the travel trajectories of the other vehicles. When the first other vehicle and the second other vehicle recognized by the first recognition unit 132 move laterally in front of the host vehicle M and the positions of the laterally moved differ by a predetermined distance or more, the determination unit 142 does not perform the determination of whether the first other vehicle and the second other vehicle are correct. This allows the determination of whether the lane markings are correct more appropriately depending on the lane markings around the host vehicle and the travel conditions of the other vehicles. Furthermore, according to the embodiment, more appropriate driving control can be performed based on the determination result, further improving the continuity of driving control. This can ultimately contribute to the development of a sustainable transportation system.

[0083] Furthermore, according to the embodiment, for example, if a first other vehicle and a second other vehicle cross a map lane line at the same position (if the lateral movement position is less than a predetermined distance), the other vehicles can be used to determine whether the map lane line is correct, and it can be determined that the map lane line is incorrect. Furthermore, according to the embodiment, for example, if a first other vehicle and a second other vehicle cross a map lane line at different positions in the traveling direction (if the lateral movement position is equal to or greater than a predetermined distance), it can be determined that the first other vehicle and the second other vehicle are changing lanes. By not using the other vehicles to determine whether the map lane line is correct, it is possible to determine the road shape more accurately. Furthermore, according to the embodiment, for example, if there are three other vehicles, two of which have moved laterally and one has not (for example, if traveling straight), the determination of whether the lane line is correct is based on the minority vehicle, depending on the situation, rather than by majority vote, and therefore it is possible to achieve a more appropriate determination of whether the lane line is correct depending on the situation.

[0084] The above-described embodiment can be expressed as follows. a storage medium for storing computer-readable instructions; a processor connected to the storage medium; The processor executes the computer-readable instructions to: Based on an output from a detection device that detects a surrounding situation of the host vehicle, the surrounding situation including a first dividing line that divides the lane in which the host vehicle is traveling and other vehicles that exist around the host vehicle is recognized; Recognizing second lane markings that demarcate lanes around the vehicle from map information based on the position information of the vehicle; determining whether or not at least one of the first demarcation line and the second demarcation line is correct based on at least one of the first demarcation line and the second demarcation line and the travel path of the other vehicle; When the recognized first other vehicle and the recognized second other vehicle move laterally in front of the host vehicle and the positions of the laterally moved differ by a predetermined distance or more, the correct / incorrect determination is not made based on the first other vehicle and the second other vehicle. Judgment device.

[0085] The above describes the form for carrying out the present invention using an embodiment, but the present invention is not limited to such an embodiment, and various modifications and substitutions can be made within the scope that does not deviate from the gist of the present invention. [Explanation of symbols]

[0086] 1...vehicle system, 10...camera, 12...radar device, 14...LIDAR, 16...object recognition device, 20...communication device, 30...HMI, 40...vehicle sensor, 50...navigation device, 60...MPU, 80...driving operator, 100...automatic driving control device, 120...first control unit, 130...recognition unit, 132...first recognition unit, 134...second recognition unit, 140...action plan generation unit, 142...determination unit, 144...execution control unit, 160...second control unit, 162...target trajectory acquisition unit, 164...speed control unit, 166...steering control unit, 180...HMI control unit, 190...memory unit, 200...driving force output device, 210...brake device, 220...steering device, M...host vehicle

Claims

1. a first recognition unit that recognizes a surrounding situation including a first dividing line that divides the lane in which the host vehicle is traveling and other vehicles that exist around the host vehicle based on an output of a detection device that detects a surrounding situation of the host vehicle; a second recognition unit that recognizes second lane markings that demarcate lanes around the host vehicle from map information based on the position information of the host vehicle; a determination unit that determines whether at least one of the first demarcation line and the second demarcation line is correct based on at least one of the first demarcation line and the second demarcation line and the travel path of the other vehicle, the determination unit does not perform the true / false determination based on the first other vehicle and the second other vehicle when the first other vehicle and the second other vehicle recognized by the first recognition unit move laterally in front of the host vehicle and the positions of the laterally moved vehicles differ by a predetermined distance or more. Judgment device.

2. The predetermined distance is set based on the width of the lane defined by the first dividing line or the width of the lane defined by the second dividing line. The determination device according to claim 1 .

3. the predetermined distance is set according to a distance between lateral positions of the first other vehicle and the second other vehicle before they move laterally. The determination device according to claim 1 .

4. the determination unit performs the true / false determination based on the third other vehicle when the first recognition unit recognizes a third other vehicle different from the first other vehicle and the second other vehicle, and the third other vehicle does not move laterally in the same direction as the first other vehicle and the second other vehicle, which have moved laterally at positions different by a predetermined distance or more. The determination device according to claim 1 .

5. the determination unit performs the success / failure determination based on the first other vehicle and the second other vehicle when there is a point ahead of the host vehicle where the number of lanes on which the host vehicle can travel is reduced and when the first other vehicle and the second other vehicle move laterally within a predetermined range before the point. The determination device according to claim 1 .

6. The computer Based on an output from a detection device that detects a surrounding situation of the host vehicle, the surrounding situation including a first dividing line that divides the travel lane of the host vehicle and other vehicles present around the host vehicle is recognized; recognizes second lane markings that demarcate lanes around the vehicle from map information based on the position information of the vehicle; determining whether or not at least one of the first demarcation line and the second demarcation line is correct based on at least one of the first demarcation line and the second demarcation line and the travel path of the other vehicle; When the recognized first other vehicle and the recognized second other vehicle move laterally in front of the host vehicle and the positions of the laterally moved differ by a predetermined distance or more, the correct / incorrect determination is not made based on the first other vehicle and the second other vehicle. Judgment method.

7. On the computer, Based on an output from a detection device that detects a surrounding situation of the host vehicle, a surrounding situation including a first dividing line that divides the travel lane of the host vehicle and other vehicles present around the host vehicle is recognized; a second dividing line that divides a lane around the vehicle from map information based on the position information of the vehicle; determining whether at least one of the first demarcation line and the second demarcation line is correct based on at least one of the first demarcation line and the second demarcation line and the travel path of the other vehicle; When the recognized first other vehicle and the recognized second other vehicle move laterally in front of the host vehicle and the positions of the laterally moved differ by a predetermined distance or more, the correct / incorrect determination is not made based on the first other vehicle and the second other vehicle. program.

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