Map Generator

Abstract

A map generating device (60) comprises an extracting unit (171) for extracting feature points from detected information detected by a vehicle-mounted detector (1a) that detects a condition around a host vehicle, a selecting unit (172) for selecting a feature point, from among a plurality of feature points extracted by the extracting unit (171), to be used in a calculation performed by a calculating unit (173), the calculating unit (173), which, on the basis of a plurality of items of detected information, uses a position and attitude of the vehicle-mounted detector (1a) for each of a plurality of different feature points selected by the selecting unit (172) to calculate a three-dimensional position of the same feature point included in the plurality of items of detected information, and a generating unit (174) which uses the three-dimensional positions of the plurality of different feature points calculated by the calculating unit (173) to generate a map including information of each of the three-dimensional positions, wherein the selecting unit (172) selects the feature points, excluding feature points of a specific ground object, and the generating unit (174) adds information relating to a point corresponding to a feature point not selected by the selecting unit (172) to the generated map.

Description

[Technical Field]

[0001] The present invention relates to a map generating device that generates a map used to estimate the position of a vehicle. [Background technology]

[0002] Conventionally, as this type of device, a device configured to create a map using feature points extracted from a captured image acquired by a camera mounted on a traveling vehicle is known (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-174910 Summary of the Invention [Problem to be solved by the invention]

[0004] In conventional technology, when a map is revised by overlapping vehicle trips, the accuracy of the information in the map can be lost. [Means for solving the problem]

[0005] A map generating device according to one aspect of the present invention includes an extraction unit that extracts feature points from detection information detected by an on-board detector that detects the situation around the vehicle; a selection unit that selects feature points to be used for calculation by a calculation unit (described later) from the plurality of feature points extracted by the extraction unit; a calculation unit that calculates, based on the plurality of detection information, the three-dimensional position of the same feature point included in each of the plurality of detection information for each of the plurality of different feature points selected by the selection unit, using the position and orientation of the on-board detector; and a generation unit that generates a map including information on each of the three-dimensional positions using the three-dimensional positions of the plurality of different feature points calculated by the calculation unit, wherein the selection unit selects feature points excluding feature points of predetermined features, and the generation unit adds information on points corresponding to the feature points not selected by the selection unit to the generated map. The map storage unit stores the generated map, the position estimation unit estimates the position of the vehicle by comparing new feature points extracted by the extraction unit from detection information newly detected by the on-board detector with feature points in the map stored in the map storage unit, and the trajectory storage unit stores the past travel trajectory of the vehicle, and when the difference between the position of the new feature point of a predetermined feature acquired based on the detection information newly detected by the on-board detector and the position of a point corresponding to the feature point not selected by the selection unit, which is added to the map stored in the map storage unit, is equal to or less than a predetermined tolerance, the map is deemed complete. and a determination unit that determines that the map is incomplete when the difference exceeds a predetermined tolerance, and that the map is incomplete when the difference exceeds a predetermined tolerance, wherein the generation unit corrects the map information so that the position of the host vehicle estimated by the position estimation unit using the new feature points matches the position of the host vehicle estimated by the position estimation unit during past travel when the position of the host vehicle is on the travel trajectory stored in the trajectory storage unit, and when the determination unit determines that the map is incomplete, adds information of points corresponding to the new feature points of the predetermined feature to the corrected map instead of information of points corresponding to feature points not selected by the selection unit. . [Effects of the Invention]

[0006] According to the present invention, it is possible to appropriately generate a map required for safe vehicle control. [Brief explanation of the drawings]

[0007] [Figure 1] 1 is a block diagram showing an overall configuration of a vehicle control system according to an embodiment of the present invention; [Figure 2] FIG. 1 is a block diagram showing a configuration of a main part of a map generating device according to an embodiment. [Figure 3A] FIG. 10 is a diagram showing an example of a camera image. [Figure 3B] FIG. 10 is a diagram illustrating extracted feature points. [Figure 3C] FIG. 10 is a diagram illustrating selected feature points. [Figure 4A] 6 is a flowchart illustrating an example of processing by a program executed by a controller. [Figure 4B] 6 is a flowchart illustrating an example of processing by a program executed by a controller. [Figure 5A] FIG. 10 is a schematic diagram illustrating information included in the environmental map at the time when the processing of step S70 is completed. [Figure 5B] FIG. 10 is a schematic diagram illustrating information included in the environmental map at the time when the processing in step S80 is completed. DETAILED DESCRIPTION OF THE INVENTION

[0008] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. A map generating device according to an embodiment of the present invention can be applied to a vehicle having an automatic driving function, i.e., an automatic driving vehicle. The vehicle to which the map generating device according to the embodiment is applied may be referred to as the host vehicle to distinguish it from other vehicles. The host vehicle may be an engine vehicle having an internal combustion engine (engine) as a driving source, an electric vehicle having a driving motor as a driving source, or a hybrid vehicle having an engine and a driving motor as driving sources. The host vehicle can run not only in an automatic driving mode in which no driving operation by the driver is required, but also in a manual driving mode in which the driver operates the vehicle.

[0009] First, a schematic configuration of a host vehicle related to autonomous driving will be described. Fig. 1 is a block diagram showing a schematic overall configuration of a vehicle control system 100 of the host vehicle having a map generation device according to an embodiment. As shown in Fig. 1, the vehicle control system 100 mainly includes a controller 10, an external sensor group 1 and an internal sensor group 2, each of which is communicatively connected to the controller 10, an input / output device 3, a positioning unit 4, a map database 5, a navigation device 6, a communication unit 7, and a driving actuator AC.

[0010] The external sensor group 1 is a collective term for a plurality of sensors (external sensors) that detect the external situation, which is information about the surroundings of the host vehicle. For example, the external sensor group 1 includes a lidar that measures the distance from the host vehicle to surrounding obstacles by measuring scattered light in response to light irradiated in all directions of the host vehicle, a radar that detects other vehicles and obstacles around the host vehicle by irradiating electromagnetic waves and detecting reflected waves, and a camera that is mounted on the host vehicle and has an imaging element (image sensor) such as a CCD or CMOS that captures images of the surroundings (front, rear, and sides) of the host vehicle.

[0011] The internal sensor group 2 is a collective term for a plurality of sensors (internal sensors) that detect the driving state of the host vehicle. For example, the internal sensor group 2 includes a vehicle speed sensor that detects the vehicle speed of the host vehicle, an acceleration sensor that detects the longitudinal acceleration and the lateral acceleration (lateral acceleration) of the host vehicle, a rotation speed sensor that detects the rotation speed of the driving source, a yaw rate sensor that detects the rotation angular velocity around the vertical axis of the center of gravity of the host vehicle, etc. The internal sensor group 2 also includes sensors that detect the driver's driving operations in manual driving mode, such as operation of the accelerator pedal, operation of the brake pedal, operation of the steering wheel, etc.

[0012] The input / output device 3 is a general term for devices that input commands from the driver and output information to the driver. For example, the input / output device 3 includes various switches through which the driver inputs various commands by operating operating members, a microphone through which the driver inputs commands by voice, a display that provides information to the driver via displayed images, a speaker that provides information to the driver by voice, etc.

[0013] The positioning unit (GNSS unit) 4 has a positioning sensor that receives positioning signals transmitted from positioning satellites. The positioning satellites are artificial satellites such as GPS satellites and quasi-zenith satellites. The positioning unit 4 measures the current position (latitude, longitude, altitude) of the vehicle using the positioning information received by the positioning sensor.

[0014] The map database 5 is a device that stores general map information used in the navigation device 6, and is configured with, for example, a hard disk or semiconductor elements. The map information includes road position information, road shape information (curvature, etc.), and position information of intersections and branch points. Note that the map information stored in the map database 5 is different from the highly accurate map information stored in the memory unit 12 of the controller 10.

[0015] The navigation device 6 is a device that searches for a target route on roads to a destination input by the driver and provides guidance along the target route. The input of the destination and guidance along the target route are performed via the input / output device 3. The target route is calculated based on the current position of the vehicle measured by the positioning unit 4 and map information stored in the map database 5. The current position of the vehicle can also be measured using detection values ​​from the external sensor group 1, and the target route can be calculated based on this current position and high-precision map information stored in the memory unit 12.

[0016] The communication unit 7 communicates with various servers (not shown) via networks including wireless communication networks such as the Internet and mobile phone networks, and acquires map information, driving history information, traffic information, and the like from the servers periodically or at any timing. In addition to acquiring driving history information, the communication unit 7 may also transmit driving history information of the vehicle to the server. Networks include not only public wireless communication networks but also closed communication networks established for each predetermined management area, such as wireless LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), and the like. The acquired map information is output to the map database 5 and the storage unit 12, where the map information is updated.

[0017] Actuators AC are driving actuators for controlling the driving of the host vehicle. When the driving source is an engine, actuators AC include a throttle actuator that adjusts the opening of the engine's throttle valve (throttle opening). When the driving source is a driving motor, actuators AC include the driving motor. Actuators AC also include a brake actuator that operates the host vehicle's braking device and a steering actuator that drives the steering device.

[0018] The controller 10 is composed of an electronic control unit (ECU). More specifically, the controller 10 includes a computer having an arithmetic unit 11 such as a CPU (microprocessor), a storage unit 12 such as a ROM and RAM, and other peripheral circuits (not shown) such as an I / O interface. Note that although multiple ECUs with different functions, such as an engine control ECU, a traction motor control ECU, and a braking device ECU, can be provided separately, for convenience, the controller 10 is shown in FIG. 1 as a collection of these ECUs.

[0019] The storage unit 12 stores highly accurate, detailed map information (referred to as high-accuracy map information). The high-accuracy map information includes road position information, road shape (curvature, etc.), road gradient information, intersection and branch point position information, types of road dividing lines such as white lines and their positions, the number of lanes, lane width and position information for each lane (information on the center position of the lane and boundary lines of the lane positions), position information of landmarks (buildings, traffic lights, signs, etc.) as markers on the map, and road surface profile information such as road surface irregularities. In the embodiment, center lines, lane boundary lines, outer lane lines, etc. are collectively referred to as road dividing lines. The high-precision map information stored in the memory unit 12 includes map information obtained from outside the vehicle via the communication unit 7 (referred to as external map information), and a map created by the vehicle itself using detection values ​​from the external sensor group 1 or detection values ​​from the external sensor group 1 and the internal sensor group 2 (referred to as internal map information).

[0020] The external map information is, for example, information on a map (called a cloud map) acquired via a cloud server, and the internal map information is information on a map (called an environmental map) made up of three-dimensional point cloud data generated by mapping using a technology such as SLAM (Simultaneous Localization and Mapping). The external map information is shared between the vehicle and other vehicles, whereas the internal map information is map information unique to the vehicle (for example, map information that is solely possessed by the vehicle). For roads that the vehicle has not yet traveled on, newly constructed roads, etc., the vehicle itself creates an environmental map. The internal map information may be provided to a server device or other vehicles via the communication unit 7. In addition to the high-precision map information described above, the storage unit 12 also stores information such as the vehicle's travel trajectory information, various control programs, and threshold values ​​used in the programs.

[0021] The calculation unit 11 has, as functional components, a vehicle position recognition unit 13, an external environment recognition unit 14, a behavior plan generation unit 15, a driving control unit 16, and a map generation unit 17.

[0022] The vehicle position recognition unit 13 recognizes (may also be called estimates) the position of the vehicle on the map (vehicle position) based on the position information of the vehicle obtained by the positioning unit 4 and the map information of the map database 5. The vehicle position may be recognized (estimated) using high-precision map information stored in the memory unit 12 and information about the surroundings of the vehicle detected by the external sensor group 1, thereby enabling the vehicle position to be recognized with high precision. The vehicle's position can also be recognized by calculating the vehicle's movement information (movement direction, movement distance) based on the detection values ​​of the internal sensor group 2. When the vehicle's position can be measured by an external sensor installed on the road or beside the road, the vehicle's position can also be recognized by communicating with the sensor via the communication unit 7.

[0023] The external environment recognition unit 14 recognizes the external situation around the host vehicle based on signals from the external sensor group 1, such as a lidar, radar, and camera. For example, it recognizes the positions, speeds, and accelerations of surrounding vehicles (vehicles ahead and vehicles behind) traveling around the host vehicle, the positions of surrounding vehicles stopped or parked around the host vehicle, and the positions and states of other objects. Examples of other objects include signs, traffic lights, markings such as road dividing lines and stop lines, buildings, guardrails, utility poles, billboards, pedestrians, and bicycles. Examples of the states of other objects include the color of traffic lights (red, green, yellow), the movement speed and direction of pedestrians and bicycles, and the like. Some of the stationary objects among the other objects constitute landmarks that serve as indicators of locations on a map, and the external environment recognition unit 14 also recognizes the positions and types of the landmarks.

[0024] The behavior plan generation unit 15 generates a traveling trajectory (target trajectory) of the host vehicle from the current time to a predetermined time ahead based on, for example, a target route calculated by the navigation device 6, high-precision map information stored in the memory unit 12, the host vehicle position recognized by the host vehicle position recognition unit 13, and the external situation recognized by the external environment recognition unit 14. When there are multiple trajectories that are candidates for the target trajectory on the target route, the behavior plan generation unit 15 selects an optimal trajectory from among them that satisfies criteria such as compliance with laws and regulations and efficient and safe traveling, and sets the selected trajectory as the target trajectory. The behavior plan generation unit 15 then generates a behavior plan according to the generated target trajectory. The behavior plan generation unit 15 generates various behavior plans corresponding to overtaking driving to overtake a preceding vehicle, lane-changing driving to change the traveling lane, following driving to follow a preceding vehicle, lane-keeping driving to maintain the traveling lane without deviating from the traveling lane, decelerating driving, accelerating driving, etc. When generating the target trajectory, the behavior plan generation unit 15 first determines a driving mode and generates the target trajectory based on the driving mode.

[0025] In the autonomous driving mode, the driving control unit 16 controls the actuators AC so that the vehicle travels along the target trajectory generated by the behavior plan generation unit 15. More specifically, in the autonomous driving mode, the driving control unit 16 calculates a required driving force for achieving the target acceleration per unit time calculated by the behavior plan generation unit 15, taking into account the driving resistance determined by the road gradient, etc. Then, for example, the driving control unit 16 feedback-controls the actuators AC so that the actual acceleration detected by the internal sensor group 2 becomes the target acceleration. In other words, the driving control unit 16 controls the actuators AC so that the vehicle travels at the target vehicle speed and target acceleration. Note that in the manual driving mode, the driving control unit 16 controls the actuators AC in response to driving commands (such as steering operations) from the driver acquired by the internal sensor group 2.

[0026] While driving in manual driving mode, the map generation unit 17 uses detection values ​​detected by the external sensor group 1 to generate an environmental map of the area around the road on which the vehicle has traveled as internal map information. For example, edges that indicate the contours of objects are extracted from multiple frames of camera images acquired by a camera based on brightness and color information for each pixel, and feature points are extracted using the edge information. Feature points are, for example, intersections of edges, and correspond to corners of buildings or corners of road signs. The map generation unit 17 estimates the position and orientation of the camera according to the SLAM technology algorithm so that identical feature points converge to a single point across multiple frames of camera images, and calculates the three-dimensional position of the feature points. By performing this calculation process for each of the multiple feature points, an environmental map consisting of three-dimensional point cloud data is generated. It should be noted that instead of using a camera, data acquired by a radar or a lidar may be used to extract feature points of objects around the vehicle and generate an environmental map. Furthermore, when generating the environmental map, if the map generation unit 17 determines by pattern matching processing or the like that a predetermined feature (e.g., road dividing lines, traffic lights, signs, etc.) having feature points that were not used in calculating the above-mentioned three-dimensional positions is included in the camera image, the map generation unit 17 adds the position information of the points corresponding to the feature points of the feature based on the camera image to the environmental map and records it in the memory unit 12.

[0027] The vehicle position recognition unit 13 performs a process of recognizing the position of the vehicle in parallel with the map creation process by the map generation unit 17. That is, the vehicle position is estimated based on changes in the positions of feature points over time. The map creation process and the position recognition (estimation) process are performed simultaneously according to the algorithm of SLAM technology. The map generation unit 17 can generate an environmental map not only when driving in manual driving mode, but also when driving in automatic driving mode. If an environmental map has already been generated and stored in the memory unit 12, the map generation unit 17 may update the environmental map based on newly extracted feature points (which may also be called new feature points) from newly acquired camera images.

[0028] Incidentally, feature points used to generate an environmental map using SLAM technology must be unique feature points that are easily distinguishable from other feature points. In contrast, in actual vehicle control, it is necessary for information about features, such as road markings, to be included in the environmental map. In the embodiment, a map generation device is configured to perform the following processes (1) to (3), thereby appropriately generating an environmental map that includes information necessary for vehicle control.

[0029] (1) The feature points used to generate the environmental map are selected from among those extracted from camera images as unique feature points that are easy to distinguish from other feature points. If the feature points are not unique, it will be difficult to track the same feature points across multiple camera image frames. Therefore, while preferentially selecting unique feature points based on edge information such as building window frames, we avoid selecting feature points based on edge information of specific features such as road markings, signs, and traffic lights, which make it difficult to track the same feature points across multiple camera image frames.

[0030] (2) Information useful for recognizing (estimating) the vehicle's position is added to the environmental map. Because the environmental map does not contain information such as road markings that are necessary for recognizing the vehicle's position due to (1) above, the information such as road markings is added to the environmental map later (this can also be called embedding).

[0031] (3) When the environmental map is corrected, the information added in (2) above is re-added. Generally, in SLAM technology, errors accumulate because the vehicle recognizes its own position while moving. For example, when the vehicle travels around a road that is closed in a square shape, the accumulated errors cause the start and end points to not match. Therefore, when it is recognized that the vehicle's current position is on the previous travel trajectory, a loop closing process is performed in which the vehicle's position recognized using feature points (called new feature points) extracted from a camera image newly acquired at the same travel point as the previous one is set to the same coordinates as the vehicle's position previously recognized using feature points extracted from a camera image acquired during previous travel. In the embodiment, the loop closing process is called environmental map correction, and the 3D position information included in the environmental map is corrected. At this time, the information added in (2) above is deleted and added again to the corrected environmental map.

[0032] The map generating device that performs the above processes (1) to (3) will now be described in more detail. Fig. 2 is a block diagram showing the configuration of a main part of a map generating device 60 according to an embodiment. The map generating device 60 is used to control the traveling operation of the host vehicle, and constitutes a part of the vehicle control system 100 shown in Fig. 1. As shown in Fig. 2, the map generating device 60 has a controller 10, a camera 1a, a radar 1b, and a lidar 1c.

[0033] The camera 1a constitutes part of the external sensor group 1 in Fig. 1. The camera 1a may be a monocular camera or a stereo camera, and captures images of the surroundings of the vehicle. The camera 1a is attached, for example, to a predetermined position in the front of the vehicle, continuously captures images of the space ahead of the vehicle at a predetermined frame rate, and sequentially outputs frame image data (simply referred to as camera images) as detection information to the controller 10. 3A is a diagram showing an example of a camera image of a certain frame captured by camera 1a. Camera image IM includes another vehicle V1 traveling ahead of the host vehicle, another vehicle V2 traveling in the right lane of the host vehicle, traffic lights SG around the host vehicle, pedestrians PE, traffic signs TS1 and TS2, buildings BL1, BL2, and BL3 around the host vehicle, outer roadway lines OL, and lane boundary lines SL.

[0034] The radar 1b in FIG. 2 is mounted on the host vehicle and detects other vehicles, obstacles, etc. around the host vehicle by emitting electromagnetic waves and detecting reflected waves. The radar 1b outputs detection values ​​(detection data) as detection information to the controller 10. The lidar 1c is mounted on the host vehicle and measures scattered light in response to irradiated light in all directions from the host vehicle to detect the distance from the host vehicle to surrounding obstacles. The lidar 1c outputs detection values ​​(detection data) as detection information to the controller 10.

[0035] The controller 10 includes a calculation unit 11 and a storage unit 12. The calculation unit 11 has, as functional components, an information acquisition unit 141, an extraction unit 171, a selection unit 172, a calculation unit 173, a generation unit 174, a determination unit 175, and a vehicle position recognition unit 13. The information acquisition unit 141 is included in, for example, the external environment recognition unit 14 in Fig. 1. The extraction unit 171, the selection unit 172, the calculation unit 173, the generation unit 174, and the determination unit 175 are included in, for example, the map generation unit 17 in Fig. 1. The storage unit 12 includes a map storage unit 121 and a trajectory storage unit 122 .

[0036] The information acquisition unit 141 acquires information used to control the traveling operation of the vehicle from the storage unit 12 (map storage unit 121). More specifically, the information acquisition unit 141 reads landmark information included in the environmental map from the map storage unit 121, and further acquires, from the landmark information, information indicating the positions of lane markings on the road on which the vehicle is traveling and the extension direction of those lane markings (hereinafter referred to as lane marking information). When the lane marking information does not include information indicating the direction in which the lane marks extend, the information acquisition unit 141 may calculate the direction in which the lane marks extend based on the positions of the lane marks. Alternatively, the information acquisition unit 141 may acquire information indicating the positions and directions in which the lane marks of the road on which the host vehicle is traveling from road map information or a white line map (information indicating the positions of white, yellow, and other lane markings) stored in the map storage unit 121.

[0037] The extraction unit 171 extracts edges that indicate the contour of an object from the camera image IM (illustrated in FIG. 3A) acquired by the camera 1a, and extracts feature points using the edge information. As described above, feature points are, for example, intersections of edges. FIG. 3B is a diagram illustrating feature points extracted by the extraction unit 171 based on the camera image IM of FIG. 3A. The black circles in the diagram represent feature points.

[0038] The selection unit 172 selects feature points for calculating three-dimensional positions from the feature points extracted by the extraction unit 171. In this embodiment, feature points included in features other than predetermined features (e.g., road dividing lines, traffic lights, traffic signs, etc.) are selected as unique feature points that are easily distinguishable from other feature points. FIG. 3C is a diagram illustrating feature points selected by the selection unit 172 based on FIG. 3B. Black circles in the diagram represent feature points. The illustrated predetermined features are merely examples, and at least one may be excluded.

[0039] The calculation unit 173 estimates the position and orientation of the camera 1a so that the same feature points converge to one point across multiple frames of camera images IM, and calculates the three-dimensional positions of the feature points. The calculation unit 173 calculates the three-dimensional positions of each of the multiple different feature points selected by the selection unit 172.

[0040] The generating unit 174 uses the three-dimensional positions of the different feature points calculated by the calculating unit 173 to generate an environmental map made up of three-dimensional point cloud data including information on each three-dimensional position.

[0041] The determination unit 175 determines whether the environmental map generated by the generation unit 174 has been completed. Details of the determination will be described later, but the determination unit 175 determines whether the map generated by the generation unit 174 has been completed based on the difference between the position of a new feature point of the predetermined feature extracted based on the camera image IM newly acquired by the camera 1a and the position of a point added to the environmental map stored in the map storage unit 121.

[0042] The vehicle position recognition unit 13 estimates the vehicle position on the environmental map based on the environmental map stored in the map storage unit 121. First, the vehicle position recognition unit 13 estimates the position of the host vehicle in the vehicle width direction. Specifically, the vehicle position recognition unit 13 uses machine learning (DNN (Deep Neural Network) or the like) technology to recognize road lane lines included in the camera image IM newly acquired by the camera 1a. The vehicle position recognition unit 13 recognizes the positions and extension directions of the lane lines included in the camera image IM on the environmental map based on lane line information acquired from landmark information included in the environmental map stored in the map storage unit 121. The vehicle position recognition unit 13 then estimates the relative positional relationship between the host vehicle and the lane lines in the vehicle width direction (positional relationship on the environmental map) based on the positions and extension directions of the lane lines on the environmental map. In this way, the position of the host vehicle in the vehicle width direction on the environmental map is estimated.

[0043] Next, the vehicle position recognition unit 13 estimates the position of the vehicle in the traveling direction. In detail, the vehicle position recognition unit 13 recognizes a landmark (e.g., building BL1) from a camera image IM (FIG. 3A) newly acquired by the camera 1a by processing such as pattern matching, and recognizes a feature point on the landmark from among the feature points extracted by the extraction unit 171. Furthermore, the vehicle position recognition unit 13 estimates the distance from the vehicle to the landmark in the traveling direction based on the position of the feature point of the landmark captured in the camera image IM. Note that the distance from the vehicle to the landmark may be calculated based on detection values ​​from the radar 1b and the lidar 1c.

[0044] The vehicle position recognition unit 13 searches for feature points corresponding to the landmarks in the environmental map stored in the map storage unit 121. In other words, feature points that match the feature points of the landmarks recognized from the newly acquired camera image IM are recognized from among the multiple feature points (point cloud data) that make up the environmental map. Next, the vehicle position recognition unit 13 estimates the position of the vehicle on the environmental map in the direction of travel based on the position of the feature point on the environmental map that corresponds to the feature point of the landmark and the distance in the direction of travel from the vehicle to the landmark. As described above, the vehicle position recognition unit 13 recognizes the position of the vehicle on the environmental map based on the estimated position of the vehicle on the environmental map in the vehicle width direction and the traveling direction.

[0045] The map storage unit 121 stores information about the environmental map generated by the generation unit 174. The trajectory storage unit 122 stores information indicating the travel trajectory of the host vehicle. The travel trajectory is represented as the host vehicle position on an environmental map, for example, recognized by the host vehicle position recognition unit 13 while the host vehicle is traveling.

[0046] <Explanation of the flowchart> An example of processing executed by the controller 10 of Fig. 2 according to a predetermined program will be described with reference to the flowcharts of Fig. 4A and Fig. 4B. Fig. 4A shows processing before an environmental map is created, which is started, for example, in manual driving mode and repeated at a predetermined cycle. Fig. 4B shows processing performed in parallel with the map creation processing of Fig. 4A. Fig. 4B also shows processing after an environmental map is created, which is started, for example, in automatic driving mode and repeated at a predetermined cycle.

[0047] In step S10 of FIG. 4A, the controller 10 acquires a camera image IM as detection information from the camera 1a, and the process proceeds to step S20.

[0048] In step S20, the controller 10 extracts feature points from the camera image IM using the extraction unit 171, and then the process proceeds to step S30.

[0049] In step S30, the controller 10 selects a feature point using the selection unit 172, and then proceeds to step S40. As described above, by selecting a feature point that is included in a feature other than road dividing lines, traffic lights, traffic signs, etc., it becomes possible to select a unique feature point that is easily distinguishable from other feature points.

[0050] In step S40, the controller 10 causes the calculation unit 173 to calculate the three-dimensional positions of the plurality of different feature points, and then proceeds to step S50.

[0051] In step S50, the controller 10 generates, by the generation unit 174, an environmental map made up of three-dimensional point cloud data including information on the three-dimensional positions of a plurality of different feature points, and then proceeds to step S60.

[0052] In step S60, the controller 10 acquires position information (distance from the vehicle to the feature) of a feature having a feature point that was not selected in step S30 among the feature points extracted in step S20, in other words, the predetermined feature (road dividing line, traffic light, traffic sign, etc.), and proceeds to step S70. This position information is obtained by estimating the distance from the vehicle to the feature based on the position of the feature point of the feature captured in the camera image IM. Note that the distance from the vehicle to the feature may also be acquired based on the detection value of the radar 1b or the lidar 1c.

[0053] In step S70, the controller 10 adds information about points corresponding to the feature points of the above-mentioned features to the point cloud data of the environmental map, and then proceeds to step S80. With this configuration, information about features such as lane lines is embedded in the environmental map. By adding information about lane lines, traffic lights, and traffic signs to the environmental map, it becomes possible to provide information about the positions of lane lines, traffic lights, and traffic signs that are visible from the host vehicle's position estimated based on the information about the environmental map to the host vehicle, based on the information about the environmental map.

[0054] In step S80, if the controller 10 recognizes that the position where the vehicle is traveling is on the past traveling trajectory, it corrects the three-dimensional position information contained in the environmental map by the loop closing process described above, and proceeds to step S90.

[0055] In step S90, the controller 10 records the information of the environmental map in the map storage unit 121 of the storage unit 12, and ends the processing in FIG. 4A.

[0056] In step S210 of FIG. 4B, the controller 10 acquires a camera image IM as detection information from the camera 1a, and the process proceeds to step S220.

[0057] In step S220, the controller 10 extracts new feature points from the camera image IM using the extraction unit 171, and proceeds to step S230. Note that the feature points extracted in the processing of Fig. 4B are called new feature points even if they are points on the same object as the feature points extracted in the processing of Fig. 4A.

[0058] In step S230, the controller 10 selects new feature points using the selection unit 172, and proceeds to step S240. In step S230, new feature points based on edge information of predetermined features (road dividing lines, signs, traffic lights, etc.) and new feature points based on edge information of buildings and the like that are not predetermined features are selected.

[0059] In step S240, the controller 10 recognizes (estimates) the vehicle position based on the environmental map using the vehicle position recognition unit 13, and then the process proceeds to step S250.

[0060] In step S250, the controller 10 calculates the position difference and proceeds to step S260. The position difference is the difference between the position of the new feature point of the predetermined feature selected in step S230 and the position of the point corresponding to the feature point of the predetermined feature that was added to the environmental map in step S70. The position information of the new feature point of the predetermined feature is obtained by estimating the distance from the vehicle to the lane marking, etc., based on the position of the lane marking, etc., captured in the camera image IM, for example. Note that the distance from the vehicle to the lane marking, etc., may also be obtained based on the detection value of the radar 1b or the lidar 1c.

[0061] In step S260, the controller 10 determines whether the environmental map is complete. If the position difference is equal to or less than a predetermined tolerance, the controller 10 makes a positive determination in step S260 and proceeds to step S270. In this case, the environmental map is considered to be complete for the area traveled during the processing of FIG. 4B, and the environmental map can be used for vehicle control in autonomous driving in this area. On the other hand, if the position difference exceeds the predetermined allowable value, the controller 10 makes a negative judgment in step S260 and proceeds to step S280. In this case, it is determined that the environmental map is incomplete for the area traveled during the processing of Fig. 4B, and the environmental map cannot be used for vehicle control in autonomous driving in this area.

[0062] In step S280, the controller 10 deletes the information added to the environmental map in step S70, and adds the position information of the new feature point of the predetermined feature selected in step S230 back to the environmental map, and then proceeds to step S270.

[0063] According to the embodiment described above, the following effects can be obtained. (1) The map generating device 60 includes an extraction unit 171 that extracts feature points from a camera image IM as detection information detected by a camera 1a that serves as an on-board detector that detects the situation around the vehicle; a selection unit 172 that selects a feature point to be used for calculation by a calculation unit 173 from the multiple feature points extracted by the extraction unit 171; a calculation unit 173 that calculates the three-dimensional position of the same feature point included in the camera image IM of multiple frames for each of multiple different feature points selected by the selection unit 172 based on the camera image IM of multiple frames and using the position and attitude of the camera 1a; and a generation unit 174 that generates an environmental map including information on each of the three-dimensional positions using the three-dimensional positions of the multiple different feature points calculated by the calculation unit 173, where the selection unit 172 selects feature points excluding feature points of specified features, and the generation unit 174 adds information on points corresponding to the feature points not selected by the selection unit 172 to the generated environmental map. With this configuration, it is possible to prioritize the selection of unique feature points that are easy to track across multiple frames of camera images IM (for example, feature points based on edge information of building window frames, etc.), while avoiding the selection of feature points that are difficult to track across multiple frames of camera images IM (for example, feature points based on edge information of specified features such as road dividing lines, signs, and traffic lights), thereby reducing the number of feature points used to generate the environmental map while including information such as dividing lines that is useful for recognizing (estimating) the vehicle's position in the environmental map. In this way, it becomes possible to appropriately generate an environmental map necessary for safe vehicle control.

[0064] (2) The map generating device 60 described in (1) above further includes a map memory unit 121 that stores the generated environmental map, a vehicle position recognition unit 13 as a position estimation unit that estimates the position of the vehicle by comparing new feature points extracted by the extraction unit 171 from a camera image IM newly detected by the camera 1a with feature points in the environmental map stored in the map memory unit 121, and a trajectory memory unit 122 that stores the past driving trajectory of the vehicle, and when the position at which the vehicle is driving is on the driving trajectory stored in the trajectory memory unit 122, the generation unit 174 corrects the information of the environmental map so as to match the position of the vehicle estimated by the vehicle position recognition unit 13 using the new feature points with the position of the vehicle estimated by the vehicle position recognition unit 13 during past driving. This configuration allows the loop closing process to be performed appropriately to correct the information contained in the environmental map, thereby enabling the appropriate generation of an environmental map necessary for safe vehicle control.

[0065] (3) The map generating device 60 of (2) above further includes a determination unit 175 that determines whether the environmental map is complete based on the difference between the positions of new feature points such as road dividing lines, signs, and traffic lights as predetermined features acquired based on the camera image IM newly detected by the camera 1a and the positions of points corresponding to feature points (such as dividing lines, signs, and traffic lights) that have been added to the environmental map stored in the map memory unit 121 and that have not been selected by the selection unit 172. This configuration allows the controller 10 to appropriately determine whether the environmental map is complete or not. The reason for this will be explained with reference to Figures 5A and 5B.

[0066] Fig. 5A is a schematic diagram illustrating information contained in the environmental map when the processing of step S70 is completed, and Fig. 5B is a schematic diagram illustrating information contained in the environmental map when the processing of step S80 is completed. In Fig. 5A, the circles indicated by symbols FP1 to FP12 indicate feature points that make up the environmental map, and the figures indicated by symbols T1 to T8 indicate points that were added to the environmental map in the processing of step S70 (points that correspond to the division lines in the camera image IM).

[0067] As a result of the correction process in step S80, it is assumed that the feature points constituting the environmental map, which are indicated by the symbols FP5 to FP7 in Fig. 5A, have been moved to the positions indicated by the symbols FP5 to FP7 in Fig. 5B, respectively. Points T3 and T4, which correspond to the demarcation lines and which were added using the positions of the nearest feature points FP5 to FP7 as reference positions, move to the positions indicated by the symbols T3 and T4 in Fig. 5B, respectively, in accordance with the movement of the positions of feature points FP5 to FP7. If the positions of points T3 and T4 corresponding to the lane lines have moved beyond a predetermined allowable range, the determination unit 175 determines that the environmental map generated by the generation unit 174 is incomplete. In this case, it is necessary to add points T3' and T4' corresponding to the lane lines newly acquired based on the camera image IM again, using the positions of feature points FP5 to FP7 after the movement as reference positions. On the other hand, if the positions of points T3 and T4 corresponding to the lane markings have moved to positions within a predetermined allowable range, the determination unit 175 determines that the environmental map generated by the generation unit 174 is complete. In this case, it is not necessary to add points T3' and T4' corresponding to the lane markings newly acquired based on the camera image IM again, using the positions of feature points FP5 to FP7 after the movement as reference positions. As described above, the controller 10 can appropriately determine whether or not the environmental map is complete.

[0068] (4) In the map generating device 60 described in (3) above, when the judgment unit 175 judges that the environmental map is not complete (step S260 is negative), the generation unit 174 adds information on points (T3' and T4') corresponding to new feature points such as road dividing lines, signs, and traffic lights as specified features to the revised map, instead of information on points (T3 and T4) corresponding to feature points (such as dividing lines, signs, and traffic lights) not selected by the selection unit 172. With this configuration, if points T3 and T4 corresponding to lane lines move beyond their respective allowable ranges due to the movement of feature points FP5 to FP7 caused by the correction of the environmental map information, points T3' and T4' corresponding to lane lines newly acquired based on camera image IM can be added to the environmental map anew. This makes it possible to provide information on the positions of lane lines, traffic lights, and traffic signs visible from the vehicle's position estimated based on the corrected environmental map information to the vehicle based on the corrected environmental map information.

[0069] (5) In the map generating device 60 described above in (1) to (4), the selection unit 172 does not select at least one feature point from among road dividing lines, traffic lights, and traffic signs. With this configuration, it is possible to prioritize the selection of unique feature points that are easy to track across multiple frames of camera images IM (for example, feature points based on edge information such as window frames of buildings), while avoiding the selection of feature points based on edge information such as road markings, traffic signs, and traffic lights that are difficult to track across multiple frames of camera images IM. This makes it possible to include information such as markings that are useful for recognizing (estimating) the vehicle's position in the environmental map while reducing the number of feature points used to generate the environmental map.

[0070] The above embodiment can be modified in various ways, and modifications will be described below. (Variation 1) Road dividing lines, traffic signs, and traffic lights have been given as examples of specified features for which the selection unit 172 will not select feature points based on the camera images IM, but the configuration may also be such that feature points are not selected for other features as well, provided that the object is difficult to track across multiple frames of camera images IM.

[0071] (Variation 2) In the embodiment, for ease of explanation, the processing shown in Fig. 4A has been described as processing performed before the environmental map is created. However, even after the environmental map is created (after it is determined that the environmental map is complete), the processing shown in Fig. 4A may be performed in parallel with the vehicle position recognition processing shown in Fig. 4B. By performing the processing even after the environmental map is completed, for example, if there is a change in the road environment, it becomes possible to appropriately add that information to the environmental map.

[0072] The above description is merely an example, and the present invention is not limited to the above-described embodiment and modifications as long as the features of the present invention are not impaired. One or more of the above-described embodiment and modifications can be arbitrarily combined, and modifications can also be combined with each other. [Explanation of symbols]

[0073] 1a camera, 1b radar, 1c lidar, 10 controller, 11 calculation unit, 12 memory unit, 13 vehicle position recognition unit, 14 external environment recognition unit, 17 map generation unit, 60 map generation device, 121 map memory unit, 122 trajectory memory unit, 171 extraction unit, 172 selection unit, 173 calculation unit, 174 generation unit, 175 judgment unit, BL1 to BL3 buildings, FP1 to FP12 feature points, IM camera image, OL roadway outer line, SG traffic light, SL lane boundary line, T1 to T8, T3', T4' points, TS1, TS2 traffic sign, V1, V2 other vehicles

Claims

1. an extraction unit that extracts feature points from detection information detected by an on-board detector that detects the surrounding conditions of the host vehicle; a selection unit that selects feature points to be used for calculation by a calculation unit described later from the plurality of feature points extracted by the extraction unit; a calculation unit that calculates, based on the plurality of pieces of detection information and using the positions and orientations of the on-vehicle detector, a three-dimensional position of the same feature point included in the plurality of pieces of detection information for each of the plurality of different feature points selected by the selection unit; a generation unit that generates a map including information on each of the three-dimensional positions of the feature points using the three-dimensional positions of the different feature points calculated by the calculation unit; Equipped with the selection unit is configured to select the feature points excluding the feature points of a predetermined feature, and the generation unit is configured to add information on points corresponding to the feature points not selected by the selection unit to the generated map; a map storage unit that stores the generated map; a position estimation unit that estimates a position of the vehicle by comparing new feature points extracted by the extraction unit from detection information newly detected by the on-board detector with the feature points in the map stored in the map storage unit; and a locus storage unit that stores a past travel locus of the host vehicle; a determination unit that determines that the map is complete when a difference between a position of a new feature point of the predetermined feature acquired based on detection information newly detected by the on-board detector and a position of a point that has been added to the map stored in the map storage unit and corresponds to the feature point not selected by the selection unit is equal to or less than a predetermined tolerance, and that determines that the map is incomplete when the difference exceeds the predetermined tolerance, the generation unit corrects the map information so as to match the position of the vehicle estimated by the position estimation unit using the new feature point with the position of the vehicle estimated by the position estimation unit during past travel, when the position of the vehicle is on the travel trajectory stored in the trajectory storage unit; and when the determination unit determines that the map is not complete, adds information on points corresponding to new feature points of the specified feature to the corrected map, instead of information on points corresponding to the feature points not selected by the selection unit.

2. 2. The map generating device according to claim 1, the generation unit, when the position at which the vehicle is traveling is on the traveling trajectory stored in the trajectory storage unit, corrects the map information by performing a loop closing process in which the position of the vehicle estimated by the position estimation unit using the new feature points and the position of the vehicle estimated by the position estimation unit during past traveling have the same coordinates.

3. In the map generating device according to claim 1, The map generating device is characterized in that the selection unit does not select at least one of the feature points among road dividing lines, traffic lights, and traffic signs.

Images