JP7467562B1 - External Recognition Device - Google Patents

Abstract

[Problem] To accurately recognize the external environment around a vehicle while reducing the processing load. [Solution] An external environment recognition device 50 includes a lidar 5 that irradiates electromagnetic waves around a host vehicle having a driving control unit 115 that controls the operation of an actuator AC and detects the external environment around the host vehicle based on the reflected waves, a data acquisition unit 111 that acquires point cloud data including position information of measurement points on the surface of the object from which the reflected waves are obtained, and a determination unit 113 that recognizes an object ahead of the host vehicle based on the point cloud data acquired by the data acquisition unit 111 and determines a focus point to be used by the driving control unit 115 from among the measurement points corresponding to the object based on the positional relationship between the host vehicle and the object. The data acquisition unit 111 acquires point cloud data such that the density of the point cloud data around the focus point determined by the determination unit 113 is increased. [Selected Figure] FIG. 2

Description

The present invention relates to an external environment recognition device that recognizes the external environment of a vehicle.

One such device known in the art is one that dynamically steers the lidar's laser light to generate a laser pulse pattern of non-uniform density in a specific area within the field of view (see, for example, Patent Document 1). The device described in Patent Document 1 generates a high-density laser pulse pattern in an area where an object is estimated to exist, so as to increase the accuracy of detecting the object's boundary line.

Japanese Patent No. 6860656

However, controlling the density of laser pulses on a region-by-region basis, as in the device described in Patent Document 1, may result in an unnecessarily large number of measurement points.

The external environment recognition device according to one aspect of the present invention includes an on-board detector that irradiates electromagnetic waves around a host vehicle having a driving control unit that controls the operation of a driving actuator and detects the external environment around the host vehicle based on the reflected waves, a data acquisition unit that acquires point cloud data including position information of measurement points on the surface of the object from which the reflected waves are obtained, and a determination unit that recognizes an object in front of the host vehicle based on the point cloud data acquired by the data acquisition unit and determines a focus point to be used by the driving control unit from among the measurement points corresponding to the object based on the positional relationship between the host vehicle and the object. The determination unit determines, as the focus points, a first measurement point that is the shortest distance from the host vehicle and a second measurement point that is the furthest from the host vehicle in the traveling direction among the measurement points corresponding to the object. The data acquisition unit acquires the point cloud data such that the density of the point cloud data around the focus point determined by the determination unit is increased.

The present invention makes it possible to accurately recognize the external environment around the vehicle while reducing the processing load.

FIG. 2 is a diagram showing an example of an external environment ahead of a host vehicle; FIG. 1 is a diagram for explaining the angular resolution of a lidar. FIG. 2 is a block diagram showing the configuration of a main part of the vehicle control device. 3 is a flowchart showing an example of a process executed by a CPU of the controller of FIG. 2 . FIG. FIG. 13 is a diagram showing an example of clustered point cloud data. FIG. 4 is a diagram for explaining a method of extracting a nearest neighbor point. FIG. 4 is a diagram for explaining a method of extracting a nearest neighbor point. FIG. 13 is a diagram for explaining a method for determining a target cluster. FIG. 13 is a diagram for explaining the angle of a nearest point with respect to a center line of travel. FIG. 11A and 11B are diagrams for explaining a method of calculating an additional irradiation position. 11A and 11B are diagrams for explaining a method of calculating an additional irradiation position.

The following describes an embodiment of the present invention with reference to the drawings. The external environment recognition device according to the 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 external environment recognition device according to the present embodiment is applied may be called 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 be driven 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.

When an autonomous vehicle is driving in autonomous mode (hereafter referred to as autonomous driving or autonomous driving), it recognizes the external environment around the vehicle based on detection data from on-board detectors such as cameras and LiDAR (Light Detection and Ranging). Based on the recognition results, the autonomous 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.

Figure 1A is a diagram showing an example of the external situation in front of the vehicle, which is an autonomous driving vehicle. The vehicle is traveling on lane LN2 of road RD, which has two lanes on each side (lanes LN1 and LN2) with left-hand traffic, and other vehicles 102 to 106 are traveling in front of the vehicle. The other vehicles 103 and 106 are traveling on lane LN1, and the other vehicles 102, 104, and 105 are traveling on lane LN2. The other vehicles 102, 104, and 105 are two-wheeled vehicles. Electromagnetic waves (such as laser light) emitted by a lidar mounted on the vehicle are reflected and returned from a point (measurement point) on the surface of the object, and the distance from the laser source to that point, the intensity of the reflected electromagnetic waves, the relative speed of the object located at the measurement point, etc. are measured. By scanning the laser horizontally and vertically with respect to the road RD on which the vehicle is traveling, the vehicle can recognize the positions and relative speeds of other vehicles 102-106 ahead of the vehicle. FIG. 1B is a diagram for explaining the angular resolution of the lidar. The circles IP in FIG. 1B represent the illumination positions of the lidar, with the horizontal interval between the illumination positions corresponding to the horizontal angular resolution and the vertical interval between the illumination positions corresponding to the vertical angular resolution. The vehicle 101 recognizes the external environment around the vehicle, more specifically, objects such as traffic participants and structures ahead of the vehicle, based on the information (point cloud data) of the measurement points acquired by the lidar at a predetermined time interval (a time interval determined by the frame rate of the lidar), and performs driving control based on the recognition results.

In FIG. 1A, when the vehicle 101 passes another vehicle (e.g., another vehicle 102) traveling ahead, it is necessary to accurately measure the relative position (distance and angle) of the other vehicle in order to avoid collisions or scrapes with the other vehicle or other traffic participants (vehicles, people, bicycles, etc.) that appear from behind the other vehicle. As a method for accurately recognizing the relative position of a three-dimensional object such as another 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 density of the electromagnetic wave irradiation points to increase the density of the point cloud data). On the other hand, if the number of irradiation points is increased, the processing load for controlling the on-board detector may increase, and the volume of detection data obtained by the on-board detector may increase, thereby increasing the processing load of the detection data. In addition, there is a risk of increasing the scale of the device. Therefore, taking these points into consideration, in the embodiment, the external environment recognition device is configured as follows.

The external environment recognition device according to this embodiment intermittently irradiates electromagnetic waves with a predetermined angular resolution in the traveling direction of the vehicle traveling on the road RD from the lidar of the vehicle, and acquires point cloud data discretely at different positions on the road RD. This type of external environment recognition device will be described in more detail below.

Figure 2 is a block diagram showing the main components of a vehicle control device 100 including an external environment recognition device. The vehicle control device 100 has 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 has an external environment recognition device 50 that constitutes 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.

The communication unit 1 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. The networks include not only public wireless communication networks, but also closed communication networks provided for each specified management area, such as wireless LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), and the like. The acquired map information is output to the memory unit 12, and the map information is updated. The positioning unit (GNSS unit) 2 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 2 measures the current position (latitude, longitude, altitude) of the vehicle using the positioning information received by the positioning sensor.

The internal sensor group 3 is a collective term for multiple sensors (internal sensors) that detect the driving state of the vehicle. For example, the internal sensor group 3 includes a vehicle speed sensor that detects the vehicle speed of the vehicle, an acceleration sensor that detects the longitudinal acceleration and the lateral acceleration (lateral acceleration) of the vehicle, a rotation speed sensor that detects the rotation speed of the driving source, and a yaw rate sensor that detects the rotation angular velocity around the vertical axis of the center of gravity of the vehicle. The internal sensor group 3 also includes sensors that detect the driving operations of the driver in manual driving mode, such as the operation of the accelerator pedal, the operation of the brake pedal, and the operation of the steering wheel.

The camera 4 has an imaging element such as a CCD or CMOS and captures images of the surroundings (front, rear and sides) of the vehicle. The lidar 5 intermittently irradiates electromagnetic waves with a predetermined angular resolution in the direction of travel of the vehicle, receives electromagnetic waves reflected from the surface of objects (reflected waves) and measures the distance from the vehicle to surrounding objects, as well as the object's position, shape, etc.

Actuator AC is a driving actuator for controlling the driving of the host vehicle. When the driving source for driving is an engine, actuator AC includes a throttle actuator that adjusts the opening of the throttle valve (throttle opening) of the engine. When the driving source for driving is a driving motor, actuator AC includes the driving motor. Actuator AC also includes a brake actuator that operates the braking device of the host vehicle and a steering actuator that drives the steering device.

The controller 10 is composed of an electronic control unit (ECU). More specifically, the controller 10 is composed of a computer having an arithmetic unit 11 such as a CPU (microprocessor), a memory unit 12 such as a ROM or RAM, and other peripheral circuits (not shown) such as an I/O interface. Note that it is possible to provide multiple ECUs with different functions separately, such as an ECU for engine control, an ECU for driving motor control, and an ECU for the braking system, but for the sake of convenience, in FIG. 2, the controller 10 is shown as a collection of these ECUs.

The memory unit 12 stores highly accurate, detailed map information (referred to as high-precision map information). The high-precision map information includes road position information, road shape (curvature, etc.) information, road gradient information, intersection and branch point position information, number of lanes (travel lanes), lane width and position information for each lane (lane center position and lane position boundary information), position information of landmarks (traffic lights, signs, buildings, etc.) as markers on the map, and road surface profile information such as road surface unevenness. The memory unit 12 also stores various control programs, information such as thresholds used in the programs, and setting information for vehicle-mounted detectors such as the LIDAR 5 (illumination position information, etc., described later).

The calculation unit 11 has, as functional components, a data acquisition unit 111, a recognition unit 112, a decision unit 113, a setting unit 114, and a driving control unit 115. As shown in FIG. 2, the data acquisition unit 111, the recognition unit 112, the decision unit 113, and the setting unit 114 are included in the external environment recognition device 50. As described above, the external environment recognition device 50 recognizes the external environment around the vehicle based on the detection data of the on-board detector of the lidar 5. The data acquisition unit 111, the recognition unit 112, the decision unit 113, and the setting unit 114 included in the external environment recognition device 50 will be described in detail later.

In the autonomous driving mode, the driving control unit 115 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 vehicle 101 travels along the target trajectory. In the manual driving mode, the driving control unit 115 controls the actuator AC in response to driving commands (such as steering operations) from the driver acquired by the internal sensor group 3.

The details of the external environment recognition device 50 will be described. As described above, the external environment recognition device 50 includes a data acquisition unit 111, a recognition unit 112, a determination unit 113, and a setting unit 114. The external environment recognition device 50 includes a rider 5.

The data acquisition unit 111 acquires point cloud data (detection data) including position information of measurement points on the surface of an object from which reflected waves from the lidar 5 are obtained.

The recognition unit 112 recognizes the external environment around the vehicle based on the point cloud data acquired by the data acquisition unit 111. Specifically, the recognition unit 112 recognizes objects such as other vehicles ahead of the vehicle based on the point cloud data acquired by the data acquisition unit 111.

Based on the positional relationship between the vehicle and the object recognized by the recognition unit 112, the determination unit 113 determines a focus point to be used by the driving control unit 115 from among the measurement points (a set of measurement points) corresponding to the object. Hereinafter, a set of measurement points corresponding to each object is referred to as a cluster. The determination unit 113 determines the focus point so that the vehicle does not collide with or come into contact with the object when overtaking or running alongside the object in front. Specifically, the determination unit 113 determines, as the focus points, the point that is the shortest distance from the vehicle (hereinafter referred to as the nearest point) and the point that is the furthest from the vehicle in the traveling direction (hereinafter referred to as the farthest point) among the measurement points corresponding to the object.

The setting unit 114 sets the irradiation position (irradiation point) of the rider 5. More specifically, the setting unit 114 updates the irradiation position information stored in the storage unit 12 so that the density of the point cloud data around the point of interest determined by the determination unit 113 increases. As a result, the irradiation light from the rider 5 is irradiated toward the set irradiation position at the next measurement of the rider 5 (when the next frame is acquired). As a result, the point cloud data around the point of interest is acquired with high resolution by the data acquisition unit 111 at the next measurement of the rider 5. The driving control unit 115 controls the driving operation of the vehicle so as to avoid collision or contact with the object recognized by the recognition unit 112 based on the information (position information, etc.) around the point of interest acquired with high resolution in this way. More specifically, the driving control unit 115 controls the actuator AC to adjust the accelerator opening and drive the braking device and steering device. The light emitted by the lidar 5 may be emitted in a raster scanning manner, or may be emitted intermittently so that the electromagnetic waves are emitted only at the irradiation points set by the setting unit 114, or may be emitted in other manners.

Figure 3 is a flowchart showing an example of processing executed by the calculation unit 11 of the controller 10 in Figure 2 according to a predetermined program. The processing shown in the flowchart in Figure 3 is repeated, for example, at predetermined intervals while the vehicle 101 is traveling in the autonomous driving mode, more specifically, at intervals according to the frame rate of the rider 5.

First, in step S11, an irradiation command is sent to the lidar 5, and point cloud data (detection data) including position information of measurement points where reflected waves of electromagnetic waves irradiated from the lidar 5 in response to the irradiation command are obtained. In step S12, based on the point cloud data acquired in step S11, it is determined whether an object, more specifically, another traffic participant (another vehicle, bicycle, or pedestrian) has been recognized in front of the vehicle. If the result in step S12 is negative, the process ends. If the result in step S12 is positive, in step S13, the point cloud data acquired in step S11 is clustered. Specifically, for each object recognized in step S12, a corresponding measurement point (cluster) is recognized from the point cloud data acquired in step S11. In step S14, based on the positional relationship between the vehicle and the object recognized in step S12, the nearest point is extracted from the cluster corresponding to the object. The method of extracting the nearest point will be described later. When multiple objects are recognized in step S12, the nearest point is extracted from the measurement points that constitute the cluster for each cluster corresponding to each object. In step S15, a target cluster is determined based on the nearest points of each cluster extracted in step S14. The target cluster is a cluster corresponding to an object that is more likely to collide with or come into contact with the vehicle than other objects among the objects recognized in step S12. The method of determining the target cluster will be described later. In step S16, the farthest point is extracted from the measurement points constituting the target cluster determined in step S15. The method of extracting the farthest point will be described later. In step S17, a position at which to add an illumination point (hereinafter referred to as an additional illumination position) is calculated based on the nearest point extracted in step S14 and the farthest point extracted in step S16. The method of calculating the additional illumination position will be described later.

In step S18, the setting information (irradiation position information) of the lidar 5 is updated so that electromagnetic waves are irradiated to the additional irradiation position calculated in step S17 the next time the lidar 5 measures (the next frame is acquired).

The operation of the external environment recognition device 50 will be described in more detail with reference to Figures 4A to 9B. When the vehicle is traveling on lane LN2 of road RD in Figure 1A, electromagnetic waves are emitted from the light-emitting unit (not shown) of the lidar 5, and the electromagnetic waves (reflected waves) reflected on the surface of an object in front of the vehicle are detected by the light-receiving unit (not shown) of the lidar 5. This allows information on the measurement points (point cloud data) as shown in Figure 4A to be acquired (S11). Figure 4A is a diagram showing the measurement points in schematic form. The circles in Figure 4A represent the measurement points, and the circles DP drawn in bold represent the measurement points corresponding to the other vehicles 102 to 105, i.e., the positions at which the electromagnetic waves (reflected waves) reflected on the surfaces of the other vehicles 102 to 105 are detected. Note that electromagnetic waves (reflected waves) reflected by the surfaces of the curbs, medians, roadside trees, etc. of the road RD are also detected by the light receiving unit of the lidar 5, but for the sake of simplicity of explanation and drawings, it is assumed that the reflected waves corresponding to these are not detected.

Next, the recognition unit 112 recognizes an object in front of the vehicle based on the point cloud data acquired by the data acquisition unit 111 (S12). For object recognition, RANSAC (RANdom SAmple Consensus) may be used for removing the road surface, or other methods may be used. The determination unit 113 clusters the point cloud data (measurement points DP) acquired by the data acquisition unit 111 based on the recognition result of the recognition unit 112 (S13). Any method such as DBSCAN (Density-based spatial clustering of applications with noise) or K-means method may be used for clustering. FIG. 4B is a diagram showing an example of clustered point cloud data. As shown in FIG. 4B, the determination unit 113 performs clustering on two-dimensional data (two-dimensional point cloud data) obtained by plotting the point cloud data (three-dimensional point cloud data) acquired by the data acquisition unit 111 on an XY plane (a plane extending in the traveling direction (X direction) and the road width direction (Y direction)). Clusters CL1 to CL5 in FIG. 4B correspond to other vehicles 102 to 106, respectively. The dashed rectangles in FIG. 4B simply represent other vehicles 102 to 106 corresponding to clusters CL1 to CL5, respectively. Note that the determination unit 113 may perform clustering on the three-dimensional point cloud data without converting the three-dimensional point cloud data into two-dimensional point cloud data.

Next, the determination unit 113 extracts the nearest point from each of the clusters CL1 to CL5 (S14). Here, the method of extracting the nearest point will be described. FIGS. 5A and 5B are diagrams for explaining the method of extracting the nearest point. FIG. 5A shows the nearest point MN1 extracted from the cluster CL1 in FIG. 4B. As shown in FIG. 5A, the nearest point MN1 is the measurement point that, among the measurement points constituting the cluster CL1, has the shortest distance Dxy on the XY plane from the light receiving unit (not shown) of the lidar 5. Similarly, the determination unit 113 extracts the nearest points from the clusters CL2 to CL5. The measurement points MN1 to MN5 in FIG. 5B are the nearest points extracted from the clusters CL1 to CL5 corresponding to the other vehicles 102 to 106, respectively.

Next, the determination unit 113 determines the target cluster (S15). Here, the method of determining the target cluster will be described. FIG. 6 is a diagram for explaining the method of determining the target cluster. As shown in FIG. 6, the determination unit 113 determines, as the target cluster, from among the clusters CL1 to CL5, a cluster in which the distance between the host vehicle 101 and the nearest point in the traveling direction (the vertical direction in FIG. 6) is less than a threshold value THy, and the distance between the host vehicle 101 and the nearest point in the road width direction (the horizontal direction in FIG. 6) is less than a threshold value THx. In the example shown in FIG. 6, the clusters CL1 to CL3 are determined as the target clusters. The determination unit 113 excludes, from among the clusters CL1 to CL3, clusters that do not satisfy a predetermined condition from the target cluster. Hereinafter, the cluster excluded from the target cluster will be referred to as an excluded cluster. Also, an object corresponding to the excluded cluster will be referred to as an excluded object.

Here, a method for determining an excluded cluster will be described. First, the determination unit 13 calculates the angle θ of the nearest point of the target cluster with respect to the traveling center line ML. More specifically, the determination unit 13 calculates the angle θ between the traveling center line ML and a straight line connecting the light receiving unit of the rider 5 and the nearest point of the target cluster. The traveling center line ML is the center line of the lane in which the host vehicle 101 is traveling. Note that the traveling center line ML may be a straight line extending from the light receiving unit of the rider 5 in the traveling direction of the host vehicle 101, or may be a straight line extending from the center of the vehicle width of the host vehicle 101 in the traveling direction. FIG. 7 is a diagram for explaining the angle θ of the nearest point of the target cluster with respect to the traveling center line ML. As shown in FIG. 7, the determination unit 113 calculates the angles θ (θ1 to θ3) of each of the target clusters CL1 to CL3. The determination unit 113 compares the target clusters located to the left of the moving center line ML, and determines the target cluster whose nearest point is farther away than the other target clusters and whose angle θ (absolute value) is larger than the other target clusters as the excluded cluster. Similarly, the determination unit 113 determines the excluded cluster from the target clusters located to the right of the moving center line. In the example shown in FIG. 7, the target clusters CL1 and CL2 are located to the left of the moving center line ML, the nearest point MN2 is farther away than the nearest point MN1, and the absolute value of the angle θ2 is larger than the absolute value of the angle θ1, so the target cluster CL2 is determined to be the excluded cluster. On the other hand, only the target cluster CL3 is located to the right of the moving center line ML, so the excluded cluster is not determined.

When the determination unit 113 determines the excluded cluster, it extracts the farthest point from the target clusters CL1 and CL3 other than the excluded cluster (S16). Here, the method of extracting the farthest point will be described. FIG. 8 is a diagram showing an example of the farthest point. FIG. 8 shows the farthest point MF1 extracted from the target cluster CL1. As shown in FIG. 8, the farthest point MF1 is the measurement point that has the longest distance Dy in the traveling direction from the light receiving unit (not shown) of the lidar 5 among the measurement points constituting the target cluster CL1, and is a measurement point that is located closer to the traveling center line ML than the nearest point MN1. The determination unit 113 similarly extracts the farthest point for the target cluster CL3. However, in the example of FIG. 4B, the target cluster CL3 does not include any measurement points other than the nearest point MN3, so the farthest point is not extracted.

The determination unit 113 determines the nearest point and the farthest point extracted in the above manner as the focus points to be used by the driving control unit 115. The setting unit 114 calculates the additional illumination position of the lidar 5 based on the focus points determined by the determination unit 113 (S17).

Here, the calculation of the additional illumination position of the lidar 5 by the setting unit 114 will be described. The setting unit 114 calculates the additional illumination position of the lidar 5 so as to increase the density around the point of interest (nearest point and farthest point) of the point cloud data acquired by the data acquisition unit 111, that is, to reduce the spacing in the horizontal direction (X direction and Y direction) and vertical direction (Z direction) of the illumination points around the point of interest.

9A and 9B are diagrams for explaining a method of calculating an additional irradiation position. Point AP shown in FIG. 9A represents an additional irradiation position in the horizontal direction (X direction and Y direction). Point AP is set from the point of interest in the horizontal direction (X direction and Y direction) by a specified number of points with a specified angular resolution. More specifically, point AP is set from the nearest point in the X direction by a specified number of points with a specified angular resolution. Also, point AP is set from the farthest point in the Y direction by a specified number of points with a specified angular resolution. The specified number of points may be determined based on the length between the point of interest and a measurement point (adjacent measurement point) adjacent to the point of interest and the specified angular resolution, or may be determined in advance by a user or the like. Note that when adding an irradiation position in the X direction at the nearest point, the setting unit 114 does not add an irradiation position at a position farther away from the center line of travel than the nearest point. The reason is that the possibility of collision or contact between the vehicle 101 and the other vehicle 103 at a position farther away from the nearest point in the X direction is extremely low. For a three-dimensional object with a small number of measurement points constituting a cluster (for example, a few or less), an illumination position may be added to a position away from the center line of travel so that point cloud data is acquired with high resolution around the nearest point. On the other hand, when adding an illumination position in the Y direction at the farthest point, the setting unit 114 does not add an illumination position on the host vehicle 101 side of the farthest point. The addition of illumination positions around the farthest point is performed for the purpose of more accurately detecting an object (traffic participant, etc.) TP that jumps out from the rear side of the other vehicle 103 as shown in FIG. 9A. Therefore, an illumination position is added on the rear side of the farthest point (upper side in FIG. 9A).

When the setting unit 114 calculates the additional horizontal irradiation position for the point of interest, it calculates the additional vertical irradiation position at the point of interest. First, the setting unit 114 calculates the height of the target cluster at the point of interest based on the position information of the measurement points that make up the target cluster. Specifically, the setting unit 114 acquires the maximum and minimum values in the vertical direction (Z direction) of the measurement points at the point of interest from the position information of the measurement points that make up the target cluster. The setting unit 114 calculates the height of the target cluster at the point of interest (the difference between the maximum and minimum values) based on the acquired maximum and minimum values. Next, when the calculated height is equal to or less than a predetermined height, the setting unit 114 adds an irradiation position to the point of interest in the vertical range indicated by the minimum and maximum values with a specified angular resolution. On the other hand, when the calculated height is greater than the predetermined height, the setting unit 114 adds an irradiation position to the point of interest in the range from the minimum value to the predetermined height with a specified angular resolution. The setting unit 114 calculates the additional vertical irradiation position as described above for each point of interest of the target cluster. The processes in steps S14 to S17 are performed for each of the XY planes corresponding to each position in the Z direction defined based on the angular resolution in the vertical direction (Z direction). As a result, as shown by point AP in FIG. 9B, an illumination position of the lidar 5 is added in the horizontal and vertical directions. FIG. 9B shows an example in which an illumination position is added to double the angular resolution.

The setting unit 114 updates the irradiation position information of the lidar 5 stored in the storage unit 12 based on the calculated additional irradiation positions in the horizontal and vertical directions (S18). As a result, the electromagnetic waves are irradiated to the additional irradiation positions during the next measurement of the lidar 5 (when the next frame is acquired).

According to the embodiment described above, the following advantageous effects are achieved.
(1) The external environment recognition device 50 includes a lidar 5 that detects the external environment around the vehicle 101 based on the reflected waves by irradiating electromagnetic waves around the vehicle 101 having a driving control unit 115 that controls the operation of the actuator AC for driving, a data acquisition unit 111 that acquires point cloud data including position information of measurement points on the surface of the object from which the reflected waves are obtained, and a determination unit 113 that recognizes an object in front of the vehicle 101 based on the point cloud data acquired by the data acquisition unit 111 and determines a point of interest to be used by the driving control unit 115 from among the measurement points corresponding to the object based on the positional relationship between the vehicle 101 and the object. The data acquisition unit 111 acquires point cloud data such that the density of the point cloud data around the point of interest determined by the determination unit 113 is increased. In addition, the data acquisition unit 111 determines, as points of interest, a first measurement point (nearest point) that is the shortest distance from the vehicle 101 among the measurement points corresponding to the object and a second measurement point (farthest point) that is the furthest from the vehicle in the traveling direction. In this way, by limiting the high resolution region to the outer edge of a forward object (another vehicle, etc.) with which the vehicle may collide or come into contact, rather than the entire field of view (FOV) of the lidar 5, it is possible to accurately recognize the forward object while reducing the amount of data in the point cloud data. In addition, since the coarseness and density control of the point cloud data is performed on a point-by-point basis, the amount of data in the point cloud data can be reduced more reliably than in the conventional method in which the coarseness and density control of the point cloud data is performed on a region-by-region basis.

(2) When multiple objects (other vehicles 102 to 106 in FIG. 6) are recognized in front of the vehicle 101 based on the point cloud data acquired by the data acquisition unit 111, the determination unit 113 determines the object (other vehicles 105, 106 in FIG. 6) whose nearest point is at least a first predetermined distance (threshold THy in FIG. 6) away from the vehicle 101 in the traveling direction and at least a second predetermined distance (threshold THx in FIG. 6) away from the vehicle 101 in the vehicle width direction as an object to be excluded, and determines the nearest point and the farthest point of the object (other vehicles 102 to 104 in FIG. 6) excluding the object to be excluded from the multiple objects as the focus points. In this way, by excluding objects that can sufficiently avoid collision or contact with the vehicle from the targets of the high resolution range, the amount of data of the point cloud data can be reduced without reducing the safety of the vehicle while traveling, and the amount of data calculation in the calculation unit 11 can be reduced.

(3) The determination unit 113 further determines the first object (other vehicle 103) as the object to be excluded when the objects (other vehicles 102-104 in FIG. 7) remaining after removing the object to be excluded from the multiple objects include a first object (other vehicle 103) and a second object (other vehicle 102), and the nearest point (measurement point MN2 in FIG. 7) corresponding to the first object (other vehicle 103) is farther in the traveling direction and vehicle width direction than the nearest point (measurement point MN1 in FIG. 7) of the second object (other vehicle 102). This makes it possible to exclude objects with an extremely low risk of collision or contact with the vehicle from the targets in the high resolution range, and to further reduce the amount of data in the point cloud data without reducing the safety of the vehicle while it is traveling.

(4) The external environment recognition device 50 further includes a setting unit 114 that sets the illumination position of the lidar 5 so that the density of the point cloud data around the point of interest is increased. In this way, by controlling the density of the point cloud data on a point-by-point basis, it is possible to accurately recognize the details of objects, such as the handlebars of a motorcycle in front of the vehicle and the arms of the driver of the motorcycle, and to improve safety when overtaking, passing, offsetting, etc., motorcycles, etc.

The above embodiment can be modified in various ways. Modifications will be described below. In the above embodiment, the vehicle-mounted detector is a lidar capable of varying the sparse and dense areas of the point cloud data. However, the above embodiment can also be applied to a lidar that cannot vary the sparse and dense areas of the point cloud data. In this case, the data acquisition unit 111 functions as a first data acquisition unit that acquires point cloud data (first point cloud data) including position information of measurement points on the surface of the object from which the reflected waves of the electromagnetic waves irradiated by the lidar 5 are obtained, and also functions as a second data processing unit that thins out the data of the first point cloud data to acquire second point cloud data having a lower data density than the first point cloud data. The determination unit 113 recognizes an object in front of the vehicle 101 based on the second point cloud data acquired by the data acquisition unit 111, and determines a point of interest from among the measurement points corresponding to the object based on the positional relationship between the vehicle 101 and the object. The driving control unit 115 uses the first point cloud data around the point of interest determined by the determination unit 113 and the second point cloud data to control the operation of the driving actuator AC. This makes it possible to reduce the amount of data calculations without reducing the accuracy of recognizing objects ahead, even when the vehicle-mounted detector is a lidar that cannot change the density of point cloud data.

In addition, in the above embodiment, the addition of illumination positions (S16 to S18) is performed only for the target cluster determined in step S15, that is, only for the cluster corresponding to an object that may collide or come into contact with the vehicle 101. However, the processing of steps S16 to S18 may be performed for the clusters corresponding to all objects recognized in step S12.

In the above embodiment, when multiple objects are recognized ahead of the host vehicle 101, the determination unit 113 determines, as an object to be excluded, an object whose nearest point is distant from the host vehicle 101 in the traveling direction by more than the threshold value THy and whose nearest point is distant from the host vehicle 101 in the vehicle width direction by more than the threshold value THx. However, the threshold value THy and the threshold value THx may be changed according to the traveling speed of the host vehicle 101. For example, the threshold value THy may be increased as the traveling speed of the host vehicle 101 increases.

In the above embodiment, the driving control unit 115 controls the driving of the vehicle to avoid collision or contact with the object recognized by the recognition unit 112, based on the information about the surroundings of the point of interest acquired with high resolution. However, the calculation unit may notify the occupants of the vehicle of warning information (video information or audio information) about collision or contact with the object recognized by the recognition unit 112 via a display or speaker (not shown) provided in the vehicle control device 100, based on the information about the surroundings of the point of interest acquired with high resolution.

In addition, in the above embodiment, the external environment recognition device 50 is applied to an autonomous vehicle, but the external environment recognition device 50 can also be applied to vehicles other than autonomous vehicles. For example, the external environment recognition device 50 can also be applied to manually driven vehicles equipped with ADAS (Advanced Driver Assistance Systems).

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. It is also possible to arbitrarily combine one or more of the above-mentioned embodiment and modifications, and it is also possible to combine modifications together.

1 Communication unit, 2 Positioning unit, 3 Internal sensor group, 4 Camera, 5 Lidar, 10 Controller, 11 Calculation unit, 12 Memory unit, 111 Data acquisition unit, 112 Recognition unit, 113 Decision unit, 114 Setting unit, 50 External recognition device, 100 Vehicle control device, 115 Travel control unit, AC actuator

Claims (6)

an on-board detector that irradiates electromagnetic waves around a vehicle and detects an external environment around the vehicle based on reflected waves, the on-board detector having a driving control unit that controls an operation of a driving actuator;
a data acquisition unit that acquires point cloud data including position information of measurement points on a surface of an object from which the reflected waves are obtained;
a determination unit that recognizes the object ahead of the host vehicle based on the point cloud data acquired by the data acquisition unit, and determines a focus point to be used by the driving control unit from among the measurement points corresponding to the object based on a positional relationship between the host vehicle and the object,
the determination unit determines, as the focus point, a first measurement point that is the shortest distance from the host vehicle among the measurement points corresponding to the object and a second measurement point that is the furthest from the host vehicle in a traveling direction;
The data acquisition unit acquires the point cloud data so that a density of the point cloud data around the point of interest determined by the determination unit is increased. The external environment recognition device according to claim 1 ,
The determination unit, when a plurality of objects are recognized in front of the host vehicle based on the point cloud data acquired by the data acquisition unit, determines, as an object to be excluded, an object whose first measurement point is away from the host vehicle by a first predetermined distance or more in a traveling direction and whose first measurement point is away from the host vehicle by a second predetermined distance or more in a vehicle width direction, and determines, as the point of interest, the first measurement point and the second measurement point of the object excluding the object to be excluded from the plurality of objects. The external environment recognition device according to claim 2 ,
The determination unit further determines the first object as the excluded object when the objects excluding the excluded object from the plurality of objects include a first object and a second object, and the first measurement point of the first object is farther away in the traveling direction and the vehicle width direction than the first measurement point of the second object. The external environment recognition device characterized by: In the external environment recognition device according to any one of claims 1 to 3 ,
The external environment recognition device is characterized in that the on-board detector is a lidar. The external environment recognition device according to claim 4 ,
The external environment recognition device further comprises a setting unit that sets an irradiation position of the vehicle-mounted detector so that the density of the point cloud data around the point of interest is increased. The external environment recognition device according to claim 4 ,
the data acquisition unit is a first data acquisition unit,
the point cloud data is first point cloud data,
a second data processing unit that thins out the first point cloud data acquired by the first data acquisition unit to acquire second point cloud data having a lower data density than the first point cloud data,
the determination unit recognizes the object ahead of the host vehicle based on the second point cloud data acquired by the second data processing unit, and determines the point of interest from among the measurement points corresponding to the object based on a positional relationship between the host vehicle and the object;
The driving control unit controls the operation of the driving actuator using the second point cloud data and the first point cloud data around the point of interest determined by the determination unit.

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