External recognition device

Abstract

To appropriately control an interval between detection points irradiated with electromagnetic waves.SOLUTION: An external environment recognition apparatus 50 includes: an on-vehicle detector 5 for detecting an external environment situation around an own vehicle by electromagnetic waves; and a road surface information acquisition unit 11 for acquiring road surface information on a road on the basis of detection data of the on-vehicle detector 5. The road surface information acquisition unit 11 includes a recognition unit 111 for recognizing a road surface of the road and a three-dimensional object on the road on the basis of three-dimensional point group data including distance information for each of a plurality of detection points acquired by the on-vehicle detector 5. Further, the external environment recognition apparatus 50 includes: a determination unit 113 for determining, as a scanning angular resolution of the electromagnetic waves, an interval between the detection points of the three-dimensional point group data used for recognition by the recognition unit 111, on the basis of a size of a predetermined three-dimensional object determined in advance as a recognition target and a distance to the three-dimensional object; and an avoidance measure unit 115 for taking a predetermined avoidance measure so that at least one of the scanning angular resolution and the number of the detection points corresponding to the interval between the detection points does not exceed a limit value determined in advance.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to an external environment recognition device that recognizes the external environment of a vehicle. [Background technology]

[0002] One known device of this type is one that performs scanning by changing the angle of laser light emitted from the lidar around a first axis parallel to the height direction and a second axis parallel to the horizontal direction, and detects the outside world of the vehicle based on the position information of each detection point (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2020-149079 A Summary of the Invention [Problem to be solved by the invention]

[0004] In the above-mentioned device, there are many detection points (irradiation points of laser light) obtained by scanning, and the processing load for obtaining position information based on each detection point is large. [Means for solving the problem]

[0005] An external environment recognition device according to one embodiment of the present invention includes an on-board detector that detects the external environment around the vehicle by scanning and irradiating electromagnetic waves in a first direction and a second direction intersecting the first direction, and a road surface information acquisition unit that acquires road surface information of the road on which the vehicle is traveling based on detection data from the on-board detector. The road surface information acquisition unit includes a recognition unit that recognizes the road surface of the road and three-dimensional objects on the road as road surface information for each frame based on three-dimensional point cloud data including distance information for each of a plurality of detection points in a matrix acquired by the on-board detector for each frame, and a determination unit that determines the interval between the detection points of the three-dimensional point cloud data of the next frame to be used for recognition by the recognition unit as a scanning angle resolution of the electromagnetic waves based on the size of a predetermined three-dimensional object to be recognized and the distance from the vehicle to the three-dimensional object; and an avoidance measure unit that takes a predetermined avoidance measure so that at least one of the scanning angle resolution and the number of detection points corresponding to the interval between the detection points does not exceed a predetermined limit value. the determination unit expands an area in which the interval between detection points is set narrower than a predetermined interval when at least one of the following applies: the vehicle speed of the host vehicle is increasing, the road surface ahead of the host vehicle has an upward gradient, and the farthest distance detectable by the vehicle-mounted detector is shorter than a predetermined distance; The avoidance measure unit takes avoidance measures so that the expansion of the region by the decision unit does not lead to exceeding the limit value of the scanning angle resolution and / or the number of detection points. Effect of the Invention

[0006] According to the present invention, it is possible to appropriately control the interval between detection points to which electromagnetic waves are irradiated from an on-vehicle detector. [Brief description of the drawings]

[0007] [Figure 1A] FIG. 1 is a diagram showing a vehicle traveling on a road. [Figure 1B] FIG. 1 is a schematic diagram showing an example of detection data by a lidar. [Diagram 2] 1 is a block diagram illustrating a schematic configuration of a main part of a vehicle control device; [Figure 3A] A diagram showing the position of point cloud data in three-dimensional space using a three-dimensional coordinate system. [Figure 3B] 1A to 1C are diagrams for explaining mapping of point cloud data from a three-dimensional space to a two-dimensional XZ space. [Figure 3C] FIG. 1 is a schematic diagram showing point cloud data divided into grids. [Figure 3D] FIG. 4 is a schematic diagram showing a road surface gradient in the depth direction. [Figure 4A] Schematic diagram showing the projection angle and depth distance. [Figure 4B] Schematic diagram showing distances measured by a lidar. [Figure 5A] 5 is a schematic diagram showing an example of the relationship between the depth distance and the light projection angle in the vertical direction. [Figure 5B] FIG. 13 is a schematic diagram showing an example of the relationship between depth distance and angular resolution in the vertical direction. [Figure 6A] FIG. 2 is a schematic diagram showing an example of an illumination point when the lidar illumination light is irradiated in a raster scanning manner. [Figure 6B] 1 is a schematic diagram showing an example of irradiation points when the lidar's irradiation light is irradiated only to specific lattice points arranged in a lattice pattern within a detection area. FIG. [Figure 7] 6C is a diagram showing an example of an irradiation order when irradiating the irradiation points illustrated in FIG. 6B with irradiation light. [Figure 8] FIG. 4 is a diagram showing an example of a predicted result of a road surface gradient. [Figure 9A] 3 is a flowchart showing an example of processing executed by a CPU of the controller of FIG. 2; [Figure 9B] 3 is a flowchart showing an example of processing executed by a CPU of the controller of FIG. 2; [Figure 10A] FIG. 11 is a schematic diagram illustrating the determination process in step S90. [Figure 10B] FIG. 11 is a schematic diagram illustrating the determination process in step S90. [Figure 10C] FIG. 11 is a schematic diagram illustrating the determination process in step S90. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, an embodiment of the invention will be described with reference to the drawings. The external environment recognition device according to the embodiment of the invention can be applied to a vehicle having an automatic driving function, that is, an automatic driving vehicle. The vehicle to which the external environment recognition device according to the embodiment is applied may be called the host vehicle to distinguish it from other vehicles. The host vehicle may be any of an engine vehicle having an internal combustion engine (engine) as a driving source, an electric vehicle having a driving motor as a driving source, and 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] When driving in an automatic driving mode (hereinafter referred to as automatic driving or autonomous driving), an automatic driving vehicle recognizes the external environment around the vehicle based on detection data from an on-board detector such as a camera or LiDAR (Light Detection and Ranging). Based on the recognition results, the automatic driving vehicle generates a driving trajectory (target trajectory) for a predetermined time ahead from the current time, and controls the driving actuators so that the vehicle drives along the target trajectory.

[0010] FIG. 1A is a diagram showing a state in which a vehicle 101, which is an autonomous vehicle, travels on a road RD. FIG. 1B is a schematic diagram showing an example of detection data obtained by a lidar mounted on the vehicle 101 and directed in the traveling direction of the vehicle 101. A measurement point (which may be called a detection point) by the lidar is point information in which an irradiated laser is reflected and returned at a certain point on the surface of an object. The point information includes the distance from the laser source to the point, the intensity of the reflected and returned laser, and the relative speed between the laser source and the point. Data consisting of a plurality of detection points as shown in FIG. 1B is called point cloud data. FIG. 1B shows point cloud data based on detection points on the surface of an object included in the field of view (hereinafter referred to as FOV) of the lidar among the objects in FIG. 1A. The FOV may be, for example, 120 deg in the horizontal direction (which may be called the road width direction) of the vehicle 101 and 40 deg in the vertical direction (which may be called the up-down direction). The value of the FOV may be changed as appropriate based on the specifications of the external environment recognition device. The host vehicle 101 recognizes the external environment around the vehicle, more specifically, the road structure and objects around the vehicle, based on point cloud data such as that shown in FIG. 1B, and generates a target trajectory based on the recognition result.

[0011] Incidentally, as a method for fully recognizing the external environment around the vehicle, it is possible to increase the number of irradiation points of the electromagnetic waves emitted from an on-board detector such as a lidar (in other words, to increase the number of detection points constituting the point cloud data by increasing the density of the irradiation points of the electromagnetic waves). Increasing the number of irradiation points of the electromagnetic waves requires more precise control of the angle at which the electromagnetic waves are emitted (increasing the angular resolution). On the other hand, increasing the number of irradiation points of the electromagnetic waves (increasing the number of detection points) may increase the processing load for controlling the on-board detector, or may increase the volume of the detection data (point cloud data) obtained by the on-board detector, thereby increasing the processing load for the point cloud data. In particular, in a situation where many objects are present on the road or on the side of the road, the volume of the point cloud data will further increase. Therefore, taking the above points into consideration, the embodiment configures an external environment recognition device as follows.

[0012] <Summary> The external environment recognition device according to the embodiment intermittently irradiates irradiation light, which is an example of electromagnetic waves, from a lidar of a vehicle 101 traveling on a road RD in the traveling direction of the vehicle 101, and acquires point cloud data discretely at different positions on the road RD. The irradiation range of the irradiation light irradiated from the lidar is set so that no blank interval of data occurs in the traveling direction of the road RD between the point cloud data of the previous frame acquired by the lidar by the previous irradiation and the point cloud data of the next frame acquired by the lidar by the current irradiation. The total number of detection points used in the recognition process is reduced compared to a case where a high detection point density is set for all road surfaces within the illumination range, for example, by setting the detection point density high for road surfaces farther from the vehicle 101 and low for road surfaces closer to the vehicle 101. This makes it possible to reduce the number of detection points used in the recognition process without reducing the recognition accuracy of the position (distance from the vehicle 101) and size of objects, etc., recognized based on the point cloud data. Such an external environment recognition device will be described in further detail below.

[0013] <Configuration of the vehicle control device> 2 is a block diagram showing a main configuration of a vehicle control device 100 including an external environment recognition device. The vehicle control device 100 includes a controller 10, a communication unit 1, a positioning unit 2, an internal sensor group 3, a camera 4, a lidar 5, and an actuator AC for driving. The vehicle control device 100 also includes an external environment recognition device 50 that constitutes a part of the vehicle control device 100. The external environment recognition device 50 recognizes the external environment around the vehicle based on detection data from on-board detectors such as the camera 4 and the lidar 5.

[0014] The communication unit 1 communicates with various servers (not shown) via a network including a wireless communication network such as the Internet network or a mobile phone network, and acquires map information, driving history information, traffic information, and the like from the server periodically or at any timing. The network includes not only a public wireless communication network but also a closed communication network provided for each predetermined management area, such as a wireless LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), and the like. The acquired map information is output to the storage unit 12, and the map information is updated. The positioning unit (GNSS unit) 2 has a positioning sensor that receives a positioning signal transmitted from a positioning satellite. The positioning satellite is an artificial satellite such as a GPS satellite or a quasi-zenith satellite. The positioning unit 2 measures the current position (latitude, longitude, altitude) of the vehicle 101 using the positioning information received by the positioning sensor.

[0015] The internal sensor group 3 is a collective term for a plurality of sensors (internal sensors) that detect the traveling state of the host vehicle 101. For example, the internal sensor group 3 includes a vehicle speed sensor that detects the vehicle speed (traveling speed) of the host vehicle 101, an acceleration sensor that detects the acceleration in the forward / rearward direction and the acceleration in the left / right direction (lateral acceleration) of the host vehicle 101, a rotation speed sensor that detects the rotation speed of the traveling drive source, a yaw rate sensor that detects the rotation angular velocity around the vertical axis of the center of gravity of the host vehicle 101, and the like. The internal sensor group 3 also includes sensors that detect the driving operations of the driver in the manual driving mode, such as the operation of the accelerator pedal, the operation of the brake pedal, and the operation of the steering wheel.

[0016] The camera 4 has an imaging element such as a CCD or a CMOS, and captures images of the surroundings (front, rear, and sides) of the vehicle 101. The lidar 5 receives scattered light in response to the irradiated light, and measures the distance from the vehicle 101 to surrounding objects, the position, shape, etc. of the objects. The notification unit 6 is composed of, for example, a display device or an audio playback device, and displays or plays audio messages, etc. for the occupants of the vehicle 101.

[0017] The actuators AC are driving actuators for controlling the driving of the host vehicle 101. When the driving source for driving is an engine, the actuators AC include a throttle actuator for adjusting the opening degree of a throttle valve (throttle opening degree) of the engine. When the driving source for driving is a driving motor, the actuators AC include the driving motor. The actuators AC also include a brake actuator for operating the braking device of the host vehicle 101 and a steering actuator for driving 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 a RAM, and other peripheral circuits (not shown) such as an I / O interface. Note that although a plurality of ECUs with different functions, such as an engine control ECU, a travel motor control ECU, and a braking device ECU, can be provided separately, the controller 10 is shown in FIG. 2 as a collection of these ECUs for the sake of convenience.

[0019] The storage unit 12 can store highly accurate and 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, number of lanes (traveling lanes), lane width and position information for each lane (information on lane center position and lane position boundary), position information of landmarks (traffic lights, signs, buildings, etc.) as markers on the map, and road surface profile information such as road surface unevenness. In addition to the two-dimensional map information described later, the storage unit 12 can also store various control programs, information such as thresholds used in the programs, and setting information for vehicle-mounted detectors such as the lidar 5 (illumination point information described later, etc.). In the embodiment, since highly accurate and detailed map information is not necessarily required, detailed map information does not have to be stored in the storage unit 12 .

[0020] The calculation unit 11 has, as functional configurations, a recognition unit 111, a setting unit 112, a determination unit 113, a gradient calculation unit 114, an avoidance measure unit 115, and a driving control unit 116. As shown in FIG. 2, the recognition unit 111, the setting unit 112, the determination unit 113, the gradient calculation unit 114, and the avoidance measure unit 115 are included in the external environment recognition device 50. As described above, the external environment recognition device 50 recognizes the external environment situation around the vehicle based on detection data from an on-board detector such as the camera 4 or the lidar 5. Details of the recognition unit 111, the setting unit 112, the determination unit 113, the gradient calculation unit 114, and the avoidance measure unit 115 included in the external environment recognition device 50 will be described later.

[0021] In the automatic driving mode, the driving control unit 116 generates a target trajectory based on the external environment around the vehicle recognized by the external environment recognition device 50, and controls the actuator AC so that the host vehicle 101 travels along the target trajectory. In the manual driving mode, the driving control unit 116 controls the actuator AC in response to a driving command (such as a steering operation) from the driver acquired by the internal sensor group 3.

[0022] The rider 5 will now be described in further detail. <Detection area> The lidar 5 is mounted facing forward of the vehicle 101 so that the area to be watched while driving is included in the FOV. The lidar 5 receives light scattered by a three-dimensional object or the like onto which the illuminating light is irradiated, and therefore the FOV of the lidar 5 corresponds to the irradiation range and detection area of ​​the illuminating light. In other words, an irradiation point within the irradiation range corresponds to a detection point within the detection area. In the embodiment, the term "three-dimensional objects, etc." refers to the road surface shape including unevenness, steps, undulations, etc., three-dimensional objects located on the road RD (facilities related to the road RD (traffic lights, signs, ditches, walls, fences, guardrails, etc.)), objects on the road RD (including other vehicles and obstacles on the road surface), and dividing lines set on the road surface. The dividing lines include white lines (including lines of different colors such as yellow), curb lines, road studs, etc., and may be called lane marks. In addition, three-dimensional objects, etc. that have been set in advance as detection targets are called detection targets.

[0023] <Example of coordinate system> 3A is a diagram showing the position of point cloud data in a three-dimensional space using a three-dimensional coordinate system. In FIG. 3A, the positive x-axis direction corresponds to the traveling direction of the vehicle 101, the positive y-axis direction corresponds to the horizontal left of the vehicle 101, and the positive z-axis direction corresponds to the vertical upward direction. In addition, the x-axis component of the position of data P is called the depth distance X, the y-axis component of the position of data P is called the horizontal distance Y, and the z-axis component of the position of data P is called the height Z. If the distance measured by the LIDAR 5, in other words the distance from the LIDAR 5 to a point on the object to be detected, is D, the coordinates (X, Y, Z) indicating the position of the data P are calculated by the following formula. X = D × cosθ × cosφ (1) Y = D × sinθ × cosφ (2) Z = D × sinφ (3) The angle θ is called the horizontal projection angle, and the angle φ is called the vertical projection angle. The horizontal projection angle θ and the vertical projection angle φ are set in the LIDAR 5 by the setting unit 112.

[0024] 3B is a diagram for explaining mapping of point cloud data from a three-dimensional space to a two-dimensional XZ space. In the embodiment, in order to calculate the road surface gradient of the road RD, data P in the three-dimensional space is mapped to data P' in the XZ space for each piece of data constituting the point cloud data. This mapping converts the three-dimensional point cloud data into two-dimensional point cloud data in the XZ space. In the XZ space, information indicating the horizontal distance Y is omitted, and information on the depth distance X and height Z remains. Next, the XZ space is divided into grids of a predetermined size (for example, 50 cm square), and the number of data P' included in each grid is counted. Figure 3C is a schematic diagram showing point cloud data divided by grids. Note that the number of grids based on the actual data P' is much more than the number shown in the figure. 3C shows the position data (depth distance X) for each grid, the height Z for each grid, and the number of data P' in each grid. In this embodiment, the data of three-dimensional objects is separated and excluded in advance, so the data mainly consists of grid data of X and Z for the road surface. Therefore, by sequentially extracting grids in the depth distance X direction that have the maximum number of data P' in the grid, a row of grids showing the height Z of the road surface as shown in FIG. 3D, that is, the road surface gradient in the depth distance X direction, is obtained. Focusing on each grid, the following equation (4) is established between the vertical projection angle α with respect to the road surface point of that grid (corresponding to the irradiation point described above), the depth distance X of that road surface point, and the road surface height Z. Also, the following equation (5) is established between the distance DL from the rider 5 to that road surface point, the depth distance X of that road surface point, and the road surface height Z. tan α = Z / X (4) DL=(X 2 +Z 2 ) 1 / 2 (5)

[0025] <Projection angle and depth distance> 4A is a schematic diagram showing the vertical projection angle α (angle of the irradiated light with respect to the horizontal direction) of the lidar 5 and the depth distance X. The external environment recognition device 50 changes the projection angle α to change the irradiation direction of the irradiated light up and down, thereby moving the position of the irradiation point in the vertical direction. In FIG. 4A, for example, when the light is irradiated onto the road RD at a point where the depth distance X2 is 10 m, Projection angle The road surface is illuminated with α2. For example, when the light is illuminated on the road RD at a point where the depth distance X1 is 40 m, Projection angle The road surface is illuminated with α1. Furthermore, when the light is illuminated on the road RD at a point where the depth distance X0 is 100 m, for example, Projection angle The road surface is illuminated at α0. Generally, the larger the angle of incidence with respect to the road surface, the smaller the amount of scattered light returning from the road surface to the LIDAR 5. Therefore, in many cases, the received level of scattered light for light irradiated to a point at depth distance X0 is the lowest.

[0026] FIG. 4B is a schematic diagram showing the distance DL measured by the lidar 5. As described above with reference to FIGS. 3A to 3D, the external recognition device 50 uses the light projection angle α set for the lidar 5, the distance DL (optical path length of the irradiation light) measured by the lidar 5, and the above equations (4) and (5) to calculate the depth distance X to the road surface point irradiated with the irradiation light and the height Z of the road surface point.

[0027] When the external recognition device 50 wants to make the depth distance longer than the current value, it decreases the light projection angle α, and when it wants to make the depth distance shorter than the current value, it increases the light projection angle α. For example, when changing the depth distance from a state where the irradiation light is irradiated at a point with a depth distance of 70 m to 100 m, the external recognition device 50 decreases the light projection angle α from the current value so that the irradiation light is irradiated at a point with a depth distance of 100 m. Also, for example, when the road RD has a downward gradient or the like and the irradiation light is not irradiated on the road RD, the external recognition device 50 increases the light projection angle α from the current value so that the irradiation light is irradiated on the road RD.

[0028] FIG. 5A is a schematic diagram showing an example of the relationship between the depth distance X and the vertical light projection angle α. The horizontal axis represents the depth distance X (unit: m), and the vertical axis represents the vertical light projection angle α (unit: deg). The light projection angle α may be called the vertical direction angle. As illustrated in FIG. 5A, when the external recognition device 50 wants to shorten the depth distance X, it increases the light projection angle α to the negative side, and when it wants to lengthen the depth distance X, it decreases the light projection angle α. The sign N will be described later.

[0029] <FOV and Depth Distance> In the embodiment, the road surface condition from the depth distance corresponding to the lower end of the FOV of the lidar 5 (for example, X2 in FIG. 4A) to the depth distance corresponding to the upper end of the FOV (for example, X0 in FIG. 4A) is detected. The depth distance corresponding to the lower end of the FOV will be referred to as the first predetermined distance, and the depth distance corresponding to the upper end of the FOV will be referred to as the second predetermined distance. In general, the camera 4 is superior to the LIDAR 5 in terms of resolution at close ranges, and the LIDAR 5 is superior to the camera 4 in terms of distance measurement accuracy and relative speed measurement accuracy. Therefore, when the angle of view of the camera 4 is wider in the vertical direction than the FOV of the LIDAR 5, the external environment recognition device 50 may have the camera 4 take on the role of detecting road surface conditions below the lower end of the FOV of the LIDAR 5 (in other words, the road surface closer to the host vehicle 101 than the first predetermined distance).

[0030] <Number of irradiation points> The external environment recognition device 50 calculates the position of the irradiation point where the irradiation light of the lidar 5 is irradiated within the FOV of the lidar 5. More specifically, the external environment recognition device 50 calculates the irradiation point according to an angular resolution calculated based on a minimum size of a detection target (for example, 15 cm in both the vertical and horizontal directions) and a required depth distance (for example, 100 m) specified in advance. The required depth distance corresponds to the braking distance of the host vehicle 101, which changes depending on the vehicle speed. In the embodiment, based on the idea that the host vehicle 101 during travel should detect the road surface condition of the road in the traveling direction at least beyond the braking distance, a value obtained by adding a predetermined margin to the braking distance is called the required depth distance. The vehicle speed of the host vehicle 101 is detected by a vehicle speed sensor of the internal sensor group 3. The relationship between the vehicle speed and the required depth distance is stored in advance in the storage unit 12. The symbol N in FIG. 5A indicates the required depth distance when the vehicle speed is, for example, 100 km / h. To give an example of the angular resolution when detecting a 15 cm target at a required depth distance of 100 m, 0.05 deg is required in both the vertical and horizontal directions, as described below with reference to Fig. 5B. Note that when detecting a target smaller than 15 cm, or when detecting a 15 cm target at a depth distance X longer than 100 m, it is necessary to further increase the number of illumination points within the FOV by increasing the angular resolution.

[0031] For example, the external environment recognition device 50 calculates the positions of the illumination points so that they are arranged in a grid pattern within the FOV, and the vertical and horizontal intervals of the grid points correspond to the vertical and horizontal angular resolutions, respectively. When increasing the vertical angular resolution, the FOV is divided vertically by a number based on the angular resolution, and the vertical grid interval is narrowed to increase the number of illumination points. In other words, the intervals of the illumination points are made dense. Conversely, when decreasing the vertical angular resolution, the FOV is divided vertically by a number based on the angular resolution, and the vertical grid interval is widened to reduce the number of illumination points. In other words, the intervals of the illumination points are made coarse. The same applies to the horizontal direction. The external environment recognition device 50 generates information indicating the position of the irradiation point calculated according to the angular resolution (hereinafter referred to as irradiation point information), and stores the information in the memory unit 12 in association with position information indicating the current traveling position of the vehicle 101.

[0032] <Angular resolution and depth distance> FIG. 5B is a schematic diagram showing an example of the relationship between the depth distance X and the angular resolution in the vertical direction, and shows the angular resolution (which may be called the required angular resolution) required to recognize the detection target of the above-mentioned size (15 cm in both length and width). The horizontal axis shows the depth distance X (unit: m), and the vertical axis shows the angular resolution in the vertical direction (unit: deg). In general, the shorter the depth distance X (in other words, the closer the detection target is to the vehicle 101), the larger the viewing angle for the detection target, so that the detection target can be detected even with a low angular resolution. On the other hand, the longer the depth distance X (in other words, the farther the detection target is from the vehicle 101), the smaller the viewing angle for the detection target, so that a high angular resolution is required to detect the detection target. Therefore, as illustrated in FIG. 5B, the external environment recognition device 50 lowers the angular resolution (increases the numerical value) as the depth distance X becomes shorter, and raises the angular resolution (decreases the numerical value) as the depth distance X becomes longer. Although not shown, the same holds true for the relationship between the depth distance X and the angular resolution in the horizontal direction. The symbol N in FIG. 5B indicates the required depth distance when the vehicle speed is, for example, 100 km / h.

[0033] When the vehicle 101 is traveling in the autonomous driving mode, the external environment recognition device 50 sets a predetermined irradiation point (detection point) within the FOV and controls the lidar 5 to irradiate irradiation light. As a result, the irradiation light from the lidar 5 is irradiated toward the set irradiation point (detection point).

[0034] The light emitted by the lidar 5 may be irradiated in a raster scanning manner to all irradiation points (detection points) arranged in a grid pattern within the FOV, or may be irradiated intermittently so that the light is irradiated only to certain irradiation points (detection points), or may be irradiated in other manners.

[0035] 6A is a schematic diagram showing an example of an irradiation point when the illumination light of the lidar 5 is irradiated in a raster scanning manner. When the illumination light is irradiated from the lidar 5, the external environment recognition device 50 sets the angular resolution required at the required depth distance N for the entire area within the FOV and controls the irradiation direction of the illumination light. For example, when the required angular resolution for recognizing a detection target present at a point of the required depth distance N on the road RD is 0.05 deg in both the vertical (vertical) and horizontal (horizontal) directions, the external environment recognition device 50 controls the irradiation direction of the irradiation light to be shifted at intervals of 0.05 deg both vertically and horizontally throughout the entire FOV. That is, in FIG. 6A, each black circle of the lattice point corresponds to an irradiation point (detection point), and the intervals between the vertical and horizontal irradiation points (detection points) each correspond to an angular resolution of 0.05 deg. The number of actual illumination points within the FOV is much greater than the number of black circles shown in Fig. 6A. As a specific example, when the FOV of the lidar 5 is 120 degrees in the horizontal direction, 2400 black circles corresponding to illumination points (detection points) are arranged at intervals of 0.05 degrees in the horizontal direction. Similarly, when the FOV is 40 degrees in the vertical direction, 800 black circles corresponding to illumination points (detection points) are arranged at intervals of 0.05 degrees in the vertical direction.

[0036] The external environment recognition device 50 acquires detection data of detection points corresponding to the irradiation points in FIG. 6A every time the FOV is scanned with the irradiation light for one frame, and extracts data of detection points based on the angular resolution required for recognizing the detection target from these detection data. More specifically, for an area in the FOV where the depth distance X is shorter than the required depth distance N and where the required angular resolution is 0.1 deg rather than 0.05 deg, data is extracted so that the intervals between vertical and horizontal data are wider than 0.05 deg. Also, for an area in the FOV corresponding to the sky, where the road RD does not exist, data is extracted so that the intervals between vertical and horizontal data are wider. The intervals between the detection points extracted in this manner are the same as the intervals between the detection points indicated by black circles in FIG. 6B, which will be described later. By the external environment recognition device 50 extracting data of the detection points, it becomes possible to reduce the total number of detection data used in the recognition process.

[0037] 6B is a schematic diagram showing an example of irradiation points when the illumination light of the lidar 5 is irradiated only to predetermined irradiation points (detection points) arranged in a grid pattern within the FOV. When irradiating illumination light from the lidar 5, the external environment recognition device 50 sets the intervals of the illumination points (detection points) within the FOV to intervals according to the required angular resolution, and controls the irradiation direction of the illumination light. For example, if the required angular resolution for recognizing a detection target present at a point of required depth distance N on the road RD is 0.05 deg in both the vertical (vertical) and horizontal (horizontal) directions, the external environment recognition device 50 controls the irradiation direction of the irradiated light in an area corresponding to the required depth distance N (a strip-shaped area long in the left-right direction) so as to shift the irradiation direction of the irradiated light by 0.05 deg intervals both vertically and horizontally. In addition, in an area of ​​the FOV where the depth distance X is shorter than the required depth distance N and where a required angular resolution of 0.1 deg is sufficient, the irradiation direction of the irradiation light is controlled to widen the intervals between the vertical and horizontal detection points. Furthermore, in an area of ​​the FOV corresponding to the sky, where the road RD does not exist, the irradiation direction of the irradiation light is also controlled to widen the intervals between the vertical and horizontal detection points. Since the external environment recognition device 50 controls the intervals between the detection points, it becomes possible to reduce the total number of detection data used in the recognition process. It should be noted that the number of actual illuminated points within the FOV is much greater than the number of black circles shown in FIG. 6B.

[0038] FIG. 7 is a diagram showing an example of the irradiation order when irradiating the irradiation points illustrated in FIG. 6B with irradiation light. In FIG. 7, the irradiation direction of the irradiation light is controlled in the direction of the arrow from the upper left to the lower right irradiation point of the FOV. The letters P1 to P3 written next to the vertical arrows indicate the size of the interval between the irradiation points (detection points), where P1 indicates the interval between the irradiation points (detection points) corresponding to an angular resolution of 0.05 deg, for example. P2 indicates the interval between the irradiation points (detection points) corresponding to an angular resolution of 0.1 deg, for example. P3 indicates the interval between the irradiation points (detection points) corresponding to an angular resolution of 0.2 deg, for example. FIG. 7 shows an example in which the angular resolution is switched between three levels, but the number of levels is not limited to three, and the angular resolution may be appropriately switched between two or more levels.

[0039] <Configuration of the external recognition device> The external environment recognition device 50 will be described in detail. As described above, the external environment recognition device 50 includes the recognition unit 111, the setting unit 112, the decision unit 113, the gradient calculation unit 114, the avoidance measure unit 115, and the rider 5. <Recognition section> The recognition unit 111 generates three-dimensional point cloud data using time-series detection data detected in the FOV of the lidar 5. In addition, the recognition unit 111 recognizes the road structure in the traveling direction of the road RD on which the vehicle 101 travels and the detection object on the road RD in the traveling direction, based on the detection data measured by the LIDAR 5. The road structure refers to, for example, a straight road, a curved road, a branching road, a tunnel entrance and exit, etc. Furthermore, the recognition unit 111 detects a lane marking by, for example, performing brightness filtering processing on data showing a flat road surface. In this case, the recognition unit 111 may determine that a road surface whose brightness exceeds a predetermined threshold is a lane marking when the height of the road surface whose brightness does not exceed the predetermined threshold is substantially the same as the height of the road surface.

[0040] <Road structure recognition> An example of recognition of road structure by the recognition unit 111 will be described. The recognition unit 111 recognizes the curbs, walls, ditches, guardrails or dividing lines of the road RD ahead in the travel direction, which are included in the generated point cloud data, as boundary lines RL and RB (FIG. 1A) of the road RD, and recognizes the road structure in the travel direction indicated by the boundary lines RL and RB. As described above, the dividing lines include white lines (including lines of different colors), curb lines, road studs, etc., and the driving lanes of the road RD are defined by the markings of these dividing lines. In the embodiment, the boundary lines RL and RB of the road RD defined by the above markings are called dividing lines.

[0041] The recognition unit 111 recognizes the area between the borders RL and RB as an area corresponding to the road RD. Note that the method of recognizing the road RD is not limited to this, and other methods may be used for recognition. The recognition unit 111 also separates the generated point cloud data into point cloud data indicating a flat road surface and point cloud data indicating three-dimensional objects, etc. For example, among the three-dimensional objects, etc. on the road in the traveling direction included in the point cloud data, the recognition unit 111 recognizes road surface shapes such as unevenness, steps, and undulations that exceed 15 cm in size and objects that exceed 15 cm in length and width as detection targets. 15 cm is an example of the size of the detection target, and may be changed as appropriate.

[0042] <Settings section> The setting unit 112 sets the vertical projection angle φ of the irradiated light to the LIDAR 5. When the FOV of the LIDAR 5 is 40 deg in the vertical direction, the vertical projection angle φ is set in the range of 0 to 40 deg at intervals of 0.05 deg. Similarly, the setting unit 112 sets the horizontal projection angle θ of the irradiated light to the LIDAR 5. When the FOV of the LIDAR 5 is 120 deg in the horizontal direction, the horizontal projection angle θ is set in the range of 0 to 120 deg at intervals of 0.05 deg. The setting unit 112 sets the number of illumination points (corresponding to the number of black circles in Figs. 6A and 6B and indicating the illumination point density) within the FOV to the lidar 5 based on the angular resolution determined by the determination unit 113 as described below. As described above, the vertical and horizontal intervals of the illumination points (detection points) arranged in a grid pattern within the FOV correspond to the vertical and horizontal angular resolutions, respectively.

[0043] <Decision section> The determination unit 113 determines the scanning angle resolution set by the setting unit 112. First, the determination unit 113 calculates the vertical projection angle α at each depth distance X and the distance DL to the road surface point at each depth distance X. Specifically, as described with reference to FIG. 3D, the determination unit 113 calculates the depth distance X based on the distance DL to the road surface point measured by the LIDAR 5 and the projection angle α set in the LIDAR 5 at the time of measurement. The determination unit 113 calculates the relationship between the calculated depth distance X and the vertical angle (FIG. 5A). The determination unit 113 also calculates the relationship between the depth distance X and the distance DL. Furthermore, the determination unit 113 calculates the relationship between the depth distance X and the vertical angle resolution based on the size and depth distance X of the detection target, as illustrated in FIG. 5B. In this way, the determination unit 113 calculates the vertical angle resolution based on the size and distance DL of the detection target, and calculates the relationship between the depth distance X and the vertical angle resolution based on the distance DL and the depth distance X.

[0044] Next, the determination unit 113 determines the vertical angular resolution required to recognize the detection target of the above size. For example, in FIG. 5B, for a depth distance X where the vertical angular resolution is less than 0.1 deg, 0.05 deg, which is smaller than 0.1 deg, is determined as the required angular resolution. Also, for a depth distance X where the vertical angular resolution is 0.1 deg or more and less than 0.2 deg, 0.1 deg, which is smaller than 0.2 deg, is determined as the required angular resolution. Similarly, for a depth distance X where the vertical angular resolution is 0.2 deg or more and less than 0.3 deg, and for a depth distance X where the vertical angular resolution is 0.3 deg or more and less than 0.4 deg, the smaller 0.2 deg and 0.3 deg are determined as the required angular resolution, respectively.

[0045] The determined required angular resolution in the vertical direction can be reflected as the vertical interval between detection points when acquiring the next frame of 3D point cloud data. Furthermore, the determination unit 113 may determine the horizontal angular resolution required to recognize the detection target according to the size of the detection target and the depth distance X. The horizontal angular resolution required can also be reflected as the horizontal interval of the detection points when acquiring the 3D point cloud data of the next frame. The required angular resolution in the horizontal direction may be set to be equal to the required angular resolution in the vertical direction previously determined. In other words, on the same horizontal line as the detection point for which the required angular resolution in the vertical direction has been determined to be 0.05 deg, the required angular resolution in the horizontal direction is determined to be 0.05 deg. Similarly, on the same horizontal line as the detection point for which the required angular resolution in the vertical direction has been determined to be 0.1 deg, the required angular resolution in the horizontal direction is determined to be 0.1 deg. Furthermore, for the other required angular resolutions, on the same horizontal line as the detection point for which the required angular resolution in the vertical direction has been determined, the required angular resolution in the horizontal direction is determined to be the same value as the required angular resolution in the vertical direction.

[0046] The determination unit 113 is configured to be able to expand the area within the FOV where the irradiation spot density is set high (in other words, the depth distance width (which may also be called the depth distance width) where a high angular resolution is set for the lidar 5) in the following three cases: (1) When the required depth distance N is extended as the vehicle speed increases (2) When the farthest depth distance X that can be detected by the lidar 5 is shorter than the required depth distance N (3) When the road ahead is on an upward slope In the above cases of (1) and (3), the depth distance range for which the required angular resolution is set higher is expanded so that detection targets at greater distances can be recognized. In the above case of (2), the required angular resolution is set higher based on the idea of ​​irradiating more precisely for safety in areas that cannot be detected. The above (1) to (3) are referred to as enlargement requirements for enlarging the area where the irradiation spot density is set high, and will be described in detail later with reference to FIG. 9B.

[0047] <Gradient calculation section> When the road RD on which the vehicle 101 is traveling is downhill and the reflection angle from the road surface is small, when the road surface is flat but has a low reflectivity, or when the vehicle speed is fast and the required depth distance N is large, the LIDAR 5 may not be able to receive scattered light up to the required depth distance N. In this case, the farthest depth distance X that can be detected by the LIDAR 5 is called the maximum depth distance L. The maximum depth distance may also be called the maximum road surface detection distance.

[0048] When the necessary depth distance N calculated from the vehicle speed of the vehicle 101 exceeds the maximum depth distance L (for example, when the necessary depth distance N is 108 m and the maximum depth distance L=92 m), the gradient calculation unit 114 predicts the height Z (gradient) of the road surface farther than the maximum depth distance L, using the measurement data actually acquired by the lidar 5 from the bottom end of the FOV (first predetermined distance) to the maximum depth distance L. For gradient prediction, for example, an AR model or an ARIMA model, which is a time series prediction method, can be used. FIG. 8 shows an example of a result of prediction using the ARIMA model. The horizontal axis indicates the depth distance X (unit: m), and the vertical axis indicates the height Z (unit: m) of the road surface. As a result of prediction, an average value of the predicted height Z, an upper and lower limit value of the predicted height Z (for example, a reliability of 99%), etc. are obtained. In the embodiment, a predicted value corresponding to a curve with a reliability of 99% upper limit value in which the range of the vertical angular resolution becomes wider is adopted as the "upper limit value of the predicted height". By configuring in this way, when the road RD in the FOV is an uphill road, the possibility of missing data when acquiring the 3D point cloud data of the next frame can be reduced by setting the vertical angular resolution high in the upper vertical region. On the other hand, when the road RD is a downhill road, the irradiation light from the lidar 5 is not irradiated on the road surface in the first place, so that the range cannot be expanded downward in the vertical direction. Therefore, in order to avoid unnecessary processing, a predicted value corresponding to a curve with a reliability of 99% lower limit value is not adopted.

[0049] As described above, the gradient calculation unit 114 predicts data of the road surface gradient from the maximum depth distance L to the necessary depth distance N using the "upper limit value of predicted height" according to an ARIMA model or the like.

[0050] <Avoidance Measures Division> The avoidance measures unit 115 takes a predetermined measure to avoid settings that exceed the specifications of the lidar 5 (limits of illumination settings that can be performed) or the specifications of the calculation unit 11 (limits of computational settings that can be performed) when, for example, the decision unit 113 has decided to increase the number of illumination points to be set within the FOV (widening the depth distance range for setting a high angular resolution to the lidar 5), causing the required angular resolution in the vertical and horizontal directions to exceed the angular resolution that can be set to the lidar 5, or when the number of illumination points that can be illuminated by the lidar 5 in one frame time is exceeded, or when the number of points that can be computed by the calculation unit 11 in one frame time is exceeded. As an example, the avoidance measure unit 115 causes the traveling control unit 116 to limit the vehicle speed when traveling in the autonomous driving mode. The vehicle speed is limited by setting an upper limit on the vehicle speed so that the extension of the necessary depth distance N is kept within a predetermined range, or by prohibiting an increase in the vehicle speed so as to maintain the current necessary depth distance N. In addition, the avoidance measure unit 115 notifies the occupant through the notification unit 6 by a warning display, a warning sound, or the like that the vehicle speed is limited to avoid a setting that exceeds the setting limit of the rider 5 or the calculation unit 11.

[0051] As another example, the avoidance measures unit 115 may notify the occupant via the notification unit 6 of the vehicle speed required to avoid a setting that exceeds the set limit of the rider 5 or the calculation unit 11 before causing the travel control unit 116 to limit the vehicle speed. By encouraging the occupant to perform a deceleration operation such as a brake operation, it becomes possible to avoid a setting that exceeds the set limit of the rider 5 or the calculation unit 11.

[0052] As another example, the evasive action unit 115 may set the frame rate at which the LIDAR 5 acquires 3D point cloud data to be lower than the current frame rate, thereby making the limit value that can be set by the LIDAR 5 or the calculation unit 11 appear more lenient. For example, by reducing the frame rate to half the current frame rate, the angular resolution that can be set by the LIDAR 5 is doubled (in other words, the number of irradiation points that can be set by the LIDAR 5 is doubled). Before changing the frame rate for the LIDAR 5, the evasive action unit 115 may notify the occupant via the notification unit 6 that the setting will be changed to avoid a setting that exceeds the setting limit of the LIDAR 5 or the calculation unit 11.

[0053] Furthermore, as another example, the evasive measures unit 115 does not perform a setting that exceeds the setting limit of the rider 5 or the calculation unit 11 (maintains the current setting), but instead causes the driving control unit 116 to degrade the autonomous driving level of the autonomous driving mode to a level lower than the current level when driving in the autonomous driving mode. Before changing the autonomous driving level of the autonomous driving mode, the evasive measures unit 115 may notify the occupant via the notification unit 6 that the autonomous driving level is being changed to avoid a setting that exceeds the setting limit of the rider 5 or the calculation unit 11.

[0054] <Generating location data> The external environment recognition device 50 can generate continuous position data by mapping data indicating the position of the detection target detected based on the time-series point cloud data measured in real time by the LIDAR 5 onto, for example, a two-dimensional XY map. In the XY space, information indicating the height Z is omitted, and information on the depth distance X and horizontal distance Y remains. The recognition unit 111 acquires position information of a three-dimensional object, etc. on a two-dimensional map stored in the storage unit 12, and calculates the relative position of the above three-dimensional object, etc. from the moving speed and moving direction (e.g., azimuth angle) of the vehicle 101 by performing coordinate conversion centered on the position of the vehicle 101. Every time point cloud data is acquired by the LIDAR 5 through measurement, the recognition unit 111 performs coordinate conversion centered on the position of the vehicle 101 for the relative position of the three-dimensional object, etc. based on the acquired point cloud data, and records it on the two-dimensional map.

[0055] <Explanation of the flow chart> 9A and 9B are flowcharts showing an example of processing executed by the calculation unit 11 of the controller 10 in Fig. 2 in accordance with a predetermined program. The calculation unit 11 executes the processing repeatedly at predetermined intervals while the host vehicle 101 is traveling in the autonomous driving mode, for example.

[0056] First, in step S10, the calculation unit 11 causes the LIDAR 5 to acquire three-dimensional point cloud data, and the process proceeds to step S20. In step S20, the calculation unit 11 performs a process of separating road surface data from the point cloud data, and proceeds to step S30. More specifically, data on three-dimensional objects on the road RD is detected and separated from the point cloud data to obtain point cloud data indicating a flat road surface and point cloud data indicating three-dimensional objects, etc. The three-dimensional objects, etc. include obstacles on the road, curbs, walls, ditches, guardrails, etc. provided on the left and right ends of the road RD, as well as other vehicles such as motorcycles that are traveling.

[0057] An example of the separation process will be described. The calculation unit 11 converts the relative positions of the point cloud data into a position centered on the position of the vehicle 101, and displays the road RD on a two-dimensional map of XY corresponding to the depth direction and road width direction, for example, as seen from above, and grids the two-dimensional map to a predetermined size. When the difference between the maximum value and the minimum value of the data in each grid is smaller than a predetermined threshold, the calculation unit 11 determines that the data of that grid indicates a flat road surface. On the other hand, when the difference between the maximum value and the minimum value of the data in a grid is larger than a predetermined threshold, the calculation unit 11 determines that the data of that grid indicates a three-dimensional object or the like. It should be noted that other methods may be used to determine whether the point cloud data corresponds to data of a road surface or to a three-dimensional object or the like.

[0058] In step S30, the calculation unit 11 calculates the road surface gradient in the travel direction of the road RD by the gradient calculation unit 114 based on the data of the grid separated as road surface data, and proceeds to step S40. An example of the calculation process of the road surface gradient is as described with reference to Figs. 3A to 3D. It should be noted that other methods may be used to calculate the road surface gradient.

[0059] In step S40, the calculation unit 11 obtains the maximum depth distance L by the gradient calculation unit 114, and proceeds to step S50. As described above, the maximum depth distance L is the farthest depth distance detectable by the LIDAR 5. The gradient calculation unit 114 of the calculation unit 11 acquires, as the maximum depth distance L, the depth distance corresponding to the data of the farthest grid from the position of the host vehicle 101 among the grids extracted during the calculation process of the road surface gradient.

[0060] In step S50, the calculation unit 11 calculates the vertical projection angle α and the distance DL to the road surface point for each depth distance X, and proceeds to step S60. The relationship between the vertical angle and the depth distance X is as shown in FIG 5A. The relationship between the depth distance X, the height Z of the road surface, and the distance DL to the road surface is as shown in FIG 4B. When the maximum depth distance L is shorter than the required depth distance N, the gradient calculation unit 114 of the calculation unit 11 predicts the road surface gradient farther than the maximum depth distance L. The prediction result of the road surface gradient is as shown in Fig. 8. The calculation unit 11 calculates the vertical light projection angle α and the distance DL to the road surface point for each depth distance X based on the measurement data up to the maximum depth distance L actually acquired by the LIDAR 5 and the prediction result of the road surface gradient predicted by the gradient calculation unit 114.

[0061] In step S60, the calculation unit 11 calculates the required angular resolution for each depth distance X, and proceeds to step S70 in Fig. 9B. The required angular resolution is an angular resolution required to detect a detection target of a pre-specified size. The relationship between the depth distance X and the angular resolution is as shown in Fig. 5B.

[0062] 9B, the calculation unit 11 determines whether the enlargement requirement for enlarging the region where the above-mentioned irradiation spot density is set high is satisfied. In the embodiment, as described above for the determination unit 113, the enlargement requirement for enlarging the region where the irradiation spot density is set high (for example, widening the depth distance width where the lidar 5 is set to an angular resolution of 0.1 deg or less) is determined in advance. If at least one of the above three requirements (1) to (3) is met, the calculation unit 11 makes an affirmative decision in step S70 and proceeds to step S80. If none of the above three requirements (1) to (3) is met, the calculation unit 11 makes a negative decision in step S70 and proceeds to step S90.

[0063] In step S80, the calculation unit 11 sends an instruction to the determination unit 113 to expand the high resolution region within the FOV (in other words, to expand the region within the FOV where the irradiation spot density is set high), and the process proceeds to step S90.

[0064] In step S90, the calculation unit 11 judges whether or not the resolution exceeds a predetermined resolution threshold. As an example, the judgment is made based on the relationship between the depth distance X and the vertical angular resolution shown in FIG. 5B. As a result, if the necessary angular resolution required to recognize the detection target becomes smaller than the set limit of the angular resolution for the LIDAR 5 (for example, 0.05 deg) by widening the high-resolution region in the FOV, the calculation unit 11 judges step S90 as positive and proceeds to step S95. If the necessary angular resolution does not exceed the set limit even if the high-resolution region is widened in the FOV, the calculation unit 11 judges step S90 as negative and proceeds to step S100. The set limit refers to the angular resolution at which the operation of the LIDAR 5 is guaranteed. Fig. 10A is a schematic diagram for explaining the determination process of step S90 described above. The horizontal axis is the time axis, and corresponds to the elapsed time since the start of autonomous driving. The vertical axis indicates the required angular resolution, which indicates the minimum value of the angular resolution required within the FOV. In other words, the curve shown in Fig. 10A indicates the change over time in the minimum value of the required angular resolution. The part of the curve that exceeds the resolution threshold (angular resolution that can be set in the LIDAR 5) indicated by the dashed line corresponds to the case where a positive determination is made in step S90.

[0065] FIG. 10B is a schematic diagram for explaining the judgment process of step S100 described above. The horizontal axis is the time axis, and corresponds to the elapsed time since the start of automatic driving. The vertical axis indicates the number of irradiation points (the number of measurement points), which indicates the total number of irradiation points set within the FOV. In other words, the curve shown in FIG. 10B indicates the change in the number of irradiation points within the FOV over time. The point where the curve exceeds the irradiation point number threshold (the smaller of the number of irradiation points that can be set on the lidar 5 or the number of measurement points that can be calculated by the calculation unit 11) indicated by the dashed line corresponds to the case where a positive judgment is made in step S100. The setting limit in the case of FIG. 10B refers to the number of irradiation points for which the operation of the lidar 5 and the operation of the calculation unit 11 are guaranteed.

[0066] Fig. 10C is another schematic diagram for explaining the determination process of steps S90 and S100, and illustrates a case where the curve in Fig. 10A or Fig. 10B exceeds the threshold (resolution threshold or irradiation point number threshold) shown by the dashed line. Note that, in reality, the curve does not exceed the set limit because the avoidance measure unit 115 takes the excess avoidance measures of steps S95 and S105.

[0067] In step S95, the calculation unit 11, by the avoidance measure unit 115, takes excess avoidance measures to avoid a setting exceeding the set limit of the rider 5, and proceeds to step S100. The avoidance measures are as described above. In step S105, the calculation unit 11 takes excess avoidance measures by the avoidance measures unit 115 to avoid settings that exceed the set limits of the rider 5 and the calculation unit 11, and then proceeds to step S110. The avoidance measures are as described above.

[0068] In step S110, the calculation unit 11 determines the angular resolution in the vertical direction as the required angular resolution by the determination unit 113, and proceeds to step S120. In the embodiment, the angular resolution in the vertical direction is determined before the angular resolution in the horizontal direction.

[0069] In step S120, the determination unit 113 of the calculation unit 11 determines the horizontal angular resolution as the required angular resolution, and proceeds to step S130. By determining the horizontal angular resolution after the vertical angular resolution, it becomes easy to match the horizontal angular resolution with the vertical angular resolution.

[0070] In step S130, the calculation unit 11 determines the coordinates of the detection point. More specifically, the calculation unit 11 determines the coordinates indicating the position of the detection point as exemplified by a black circle in Fig. 6B. The recognition unit 111 recognizes a three-dimensional object or the like in the traveling direction of the road RD on which the host vehicle 101 is traveling, based on the detection data detected at the position of the detection point determined in step S130.

[0071] Each time point cloud data is acquired in step S10, the calculation unit 11 generates two-dimensional continuous position data by mapping the relative positions of the solid objects, etc. based on the point cloud data onto a two-dimensional XY map. Then, the relative positions of the solid objects, etc. based on the point cloud data can be converted into coordinates with the position of the vehicle 101 as the center and recorded on the two-dimensional map.

[0072] In step S140, the calculation unit 11 judges whether or not to end the process. When the host vehicle 101 continues to travel in the autonomous driving mode, the calculation unit 11 judges step S140 as negative, returns to step S10 in FIG. 9A, and repeats the above-mentioned process. By returning to step S10 in FIG. 9A, the determination of the coordinates of the detection points based on the point cloud data while the host vehicle 101 is traveling is periodically and repeatedly performed. On the other hand, when the host vehicle 101 has ended traveling in the autonomous driving mode, the calculation unit 11 judges step S140 as positive, and ends the process in FIG. 9B.

[0073] According to the embodiment described above, the following advantageous effects are achieved. (1) The external environment recognition device 50 includes a LIDAR 5 as an on-board detector that detects the external environment around the vehicle 101 by scanning and irradiating light as electromagnetic waves in a horizontal direction as a first direction and a vertical direction as a second direction intersecting the first direction, and a calculation unit 11 as a road surface information acquisition unit that acquires road surface information of the road RD on which the vehicle 101 is traveling based on the detection data of the LIDAR 5. The calculation unit 11 includes a recognition unit 111 that recognizes the road surface of the road RD and three-dimensional objects on the road for each frame as road surface information based on three-dimensional point cloud data including distance information for each of a matrix of multiple detection points acquired by the lidar 5 for each frame, and is equipped with a determination unit 113 that determines the interval between the detection points of the three-dimensional point cloud data of the next frame to be used for recognition by the recognition unit 111 as the scanning angle resolution of the irradiated light based on the size (e.g., 15 cm in both the vertical and horizontal directions) of a predetermined three-dimensional object to be recognized and the distance (e.g., 100 m) from the vehicle 101 to the three-dimensional object, and an avoidance measure unit 115 that takes predetermined avoidance measures so that at least one of the scanning angle resolution and the number of detection points corresponding to the interval between the detection points does not exceed a set limit as a predetermined limit value. By configuring in this manner, it is possible to avoid setting a small scanning angle resolution for the LIDAR 5 that exceeds the setting limit, or setting a number of detection points that exceeds the setting limit of the LIDAR 5 and a number of detection points that exceeds the processing limit of the calculation unit 11. In addition, by controlling the interval between the detection points based on the size of a predetermined three-dimensional object previously determined as a recognition target and the distance from the vehicle 101 to the three-dimensional object, it is possible to reduce the total amount of detection data used in the recognition process, compared to when the interval between the detection points is narrower than necessary. In other words, it is possible to reduce the processing load of the calculation unit 11 without reducing the recognition accuracy of the position and size of the object, etc., to be detected by the external environment recognition device 50. Furthermore, in the embodiment, even when the vehicle 101 travels on a road RD that is not included in the high-precision map information, on a road RD that is traveled on for the first time without the provision of high-precision map information, or on a road RD that has changed in a manner different from that in the high-precision map information due to construction or the like, it is possible to obtain the depth distance X to the road surface of the road RD in the travel direction for each detection point using the lidar 5, and determine the scanning angle resolution of the irradiated light corresponding to the interval between detection points required for the recognition unit 111 to recognize the above-mentioned three-dimensional object at each depth distance X.

[0074] (2) In the external environment recognition device of (1) above, when at least one of the following conditions is met: the vehicle speed of the vehicle 101 is increasing, the road surface ahead of the vehicle 101 is on an uphill slope, and the farthest maximum depth distance L detectable by the LIDAR 5 is shorter than a predetermined distance (for example, a required depth distance N), the decision unit 113 sets the interval between detection points narrower than a predetermined interval (for example, 0.1 deg) (in other words, sets the detection point density higher) to expand the area, and the avoidance measure unit 115 takes avoidance measures so that the area expansion by the decision unit 113 does not exceed the limit value of the scanning angle resolution and / or the number of detection points. With this configuration, while the determination unit 113 determines in real time the interval between the detection points of the 3D point cloud data used for recognition by the recognition unit 111, even if the driving condition (vehicle speed) of the vehicle 101 and the condition (gradient, road surface reflectivity, etc.) of the road RD on which it is traveling change, it is possible to appropriately control the interval between the detection points without setting a small scanning angle resolution that exceeds the setting limit for the LIDAR 5, or setting a number of detection points that exceeds the setting limit of the LIDAR 5 or a number of detection points that exceeds the processing limit of the calculation unit 11.

[0075] (3) In the external environment recognition device of (1) or (2) above, the avoidance measures unit 115 limits the vehicle speed of the host vehicle 101. With this configuration, while the determination unit 113 determines in real time the interval between the detection points of the 3D point cloud data used for recognition by the recognition unit 111, even if the condition of the road RD on which the vehicle 101 is traveling (gradient, road surface reflectivity, etc.) changes, the vehicle speed during autonomous driving is automatically limited, and the interval between the detection points can be appropriately controlled without setting a small scanning angle resolution that exceeds the setting limit for the LIDAR 5, or setting a number of detection points that exceeds the setting limit of the LIDAR 5 or a number of detection points that exceeds the processing limit of the calculation unit 11.

[0076] (4) In the external environment recognition device of (1) or (2) above, the avoidance measures unit 115 prompts the occupants of the host vehicle 101 to take avoidance measures. With this configuration, it is possible to notify the occupant, for example, via the notification unit 6, of the risk that a small scanning angle resolution exceeding the set limit will be set for the LIDAR 5, or that a number of detection points exceeding the set limit of the LIDAR 5 and a number of detection points exceeding the processing limit of the calculation unit 11 will be set, due to a change in either the running state (vehicle speed) of the vehicle 101 or the state of the road RD on which the vehicle is traveling (gradient, road surface reflectance, etc.). By the occupant manually performing an avoidance operation, for example, by slowing down the vehicle speed of the vehicle 101, it becomes possible to avoid settings that exceed the set limits of the LIDAR 5 and the calculation unit 11.

[0077] (5) In the external environment recognition device of (4) above, the avoidance measures unit 115 informs the occupant of the vehicle speed for keeping the scanning angle resolution or the number of detection points within a limit value. With this configuration, for example, the rider can be informed of how much the vehicle speed should be decelerated to via the notification unit 6. By the rider performing the deceleration operation to the relevant vehicle speed, it becomes possible to avoid settings that exceed the set limits of the rider 5 and the calculation unit 11.

[0078] (6) In the external environment recognition device of (1) or (2) above, the avoidance measure unit 115 relaxes the limit value by lowering the frame rate at which the LIDAR 5 acquires 3D point cloud data. With this configuration, the limit values ​​that can be set by the rider 5 and the limit values ​​that can be calculated by the calculation unit 11 appear to be gentler, making it possible to avoid settings that exceed the setting limits of the rider 5 and the calculation unit 11.

[0079] (7) In the external environment recognition device of (1) or (2) above, the avoidance measure unit 115 degrades the autonomous driving level of the host vehicle 101. With this configuration, by avoiding settings that exceed the setting limits of the lidar 5 and the calculation unit 11, it becomes possible to take appropriate measures, such as lowering the autonomous driving level, if this would hinder the maintenance of the autonomous driving level.

[0080] The above embodiment can be modified in various ways, and the modifications will be described below. (Variation 1) In the above-described embodiment, an example has been described in which the external environment recognition device 50 causes the LIDAR 5 to detect the road surface conditions in the traveling direction of the host vehicle 101. Instead of this, for example, a configuration may be adopted in which the LIDAR 5 has an FOV capable of detecting the surroundings of the host vehicle 101 at 360 degrees, and the LIDAR 5 detects the road surface conditions all around the host vehicle 101.

[0081] (Variation 2) The above-mentioned required depth distance N (100 m, 108 m, etc.), the angular resolution set in the lidar 5 (0.05, 0.1, 0.2, 0.3, 0.4), and other values ​​are merely examples and may be changed as appropriate.

[0082] The above description is merely an example, and the present invention is not limited to the above-mentioned embodiment and modifications, as long as the characteristics of the present invention are not impaired. One or more of the above-mentioned embodiment and modifications can be arbitrarily combined, and modifications can also be combined with each other. [Explanation of symbols]

[0083] REFERENCE SIGNS LIST 1 communication unit, 2 positioning unit, 3 internal sensor group, 4 camera, 5 lidar, 6 notification unit, 10 controller, 11 calculation unit, 12 memory unit, 50 external environment recognition device, 100 vehicle control device, 101 host vehicle, 111 recognition unit, 112 setting unit, 113 decision unit, 114 gradient calculation unit, 115 avoidance measure unit, 116 driving control unit, AC actuator

Claims

1. An external environment recognition device including: an on-vehicle detector that detects an external environment around a vehicle by scanning and irradiating electromagnetic waves in a first direction and a second direction intersecting the first direction; and a road surface information acquisition unit that acquires road surface information of a road on which the vehicle is traveling based on detection data of the on-vehicle detector, the road surface information acquisition unit includes a recognition unit that recognizes the road surface and three-dimensional objects on the road for each frame as the road surface information based on three-dimensional point cloud data including distance information for each of a plurality of detection points in a matrix acquired by the vehicle-mounted detector for each frame, a determination unit that determines an interval between detection points of the three-dimensional point cloud data of a next frame to be used for recognition by the recognition unit as a scanning angle resolution of the electromagnetic wave, based on a size of a predetermined three-dimensional object previously determined as a recognition target and a distance from the vehicle to the three-dimensional object; an avoidance measure unit that takes a predetermined avoidance measure so that at least one of the scanning angle resolution and the number of the detection points corresponding to the interval between the detection points does not exceed a predetermined limit value; Equipped with the determination unit expands an area in which the intervals between the detection points are set narrower than a predetermined interval when at least one of the following conditions is met: a vehicle speed of the host vehicle is increasing; a road surface ahead of the host vehicle has an upward gradient; and a farthest distance detectable by the vehicle-mounted detector is shorter than a predetermined distance; and The avoidance measure unit takes the avoidance measure so that the expansion of the area by the decision unit does not lead to the scanning angle resolution and / or the number of detection points exceeding the limit value.

2. In the external environment recognition device according to claim 1, The external environment recognition device, wherein the avoidance measure unit limits the vehicle speed of the host vehicle.

3. In the external environment recognition device according to claim 1, The external environment recognition device, wherein the avoidance measure unit prompts an occupant of the vehicle to take the avoidance measure.

4. In the external environment recognition device according to claim 3, The external environment recognition device is characterized in that the avoidance measure unit informs the occupant of a vehicle speed for keeping the scanning angle resolution or the number of detection points within the limit value.

5. In the external environment recognition device according to claim 1, The external environment recognition device, wherein the avoidance measure unit relaxes the limit value by lowering a frame rate at which the vehicle-mounted detector acquires the three-dimensional point cloud data.

6. In the external environment recognition device according to claim 1, The external environment recognition device, wherein the avoidance measure unit degrades the autonomous driving level of the vehicle.

7. In the external environment recognition device according to claim 1, The external environment recognition device is characterized in that the on-board detector is a lidar.

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