Object detection device

Abstract

To accurately detect a mobile object, while reducing a calculation load.SOLUTION: An object detection device 50 includes: a rider 5; a data acquisition part 111 for acquiring point group data including three-dimensional positional information of measurement points of the surface of an object and first speed information indicating relative movement speed of the measurement points, from the rider 5; an estimation part 112 for acquiring second speed information indicating absolute movement speed of own vehicle; a calculation part 113 for calculating absolute movement speed of each of the plurality of measurement points on the basis of the first speed information and the second speed information; a classification part 114 for classifying the point group data into movement point group data corresponding to the measurement points in which the absolute value of the absolute movement speed is equal to or higher predetermined speed, and quiescent point group data other than the movement point group data; a conversion part 115 for converting the three-dimensional positional information of the respective measurement points corresponding to the movement point group data into two-dimensional positional information; and a detection part 117 for detecting the object around the own vehicle, on the basis of speed addition data in which the absolute movement speed corresponding to the respective measurement points is added to the two-dimensional positional information of the respective measurement points.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to an object detection device that detects objects around a vehicle. [Background technology]

[0002] As this type of device, a device that detects moving objects by using three-dimensional point cloud data acquired by a lidar is known (see, for example, Patent Document 1). [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Patent No. 7126633 Summary of the Invention [Problem to be solved by the invention]

[0004] However, if point cloud data is used as is for moving object detection processing, as in the device described in Patent Document 1, there is a risk that the calculation load in the detection processing will increase. [Means for solving the problem]

[0005] An object detection device according to one aspect of the present invention includes a detector mounted on a moving body, which irradiates electromagnetic waves into a three-dimensional space around the moving body and detects an external environment around the moving body based on the reflected waves, a data acquisition unit which acquires from the detector point cloud data including three-dimensional position information of measurement points on the surface of the object from which the reflected waves are obtained and first velocity information indicating the relative movement speed of the measurement points, a velocity acquisition unit which acquires second velocity information indicating the absolute movement speed of the moving body, a calculation unit which calculates the absolute movement speed of each of a plurality of measurement points corresponding to the point cloud data based on the first velocity information and the second velocity information, and the moving point cloud data corresponding to the measurement points for which the absolute moving speed calculated by the calculation unit is equal to or greater than a predetermined speed, and stationary point cloud data other than the moving point cloud data; a conversion unit that converts three-dimensional position information of each measurement point corresponding to the moving point cloud data into two-dimensional position information; a generation unit that generates speed-added data by adding the absolute moving speed calculated by the calculation unit and corresponding to each measurement point to the two-dimensional position information of each measurement point obtained by the conversion by the conversion unit; and an object detection unit that detects objects included in the three-dimensional space around the moving body based on the speed-added data generated by the generation unit. Effect of the Invention

[0006] According to the present invention, it is possible to accurately detect a moving object while reducing the calculation load. [Brief description of the drawings]

[0007] [Figure 1] 1 is a block diagram showing a configuration of a main part of a vehicle control device including an object detection device according to an embodiment of the present invention. [Figure 2A] FIG. 2 is a diagram showing an example of a three-dimensional object included in a three-dimensional space around the host vehicle. [Figure 2B] FIG. 2B is a plan view of the moving object of FIG. 2A seen from above. [Figure 3A] A diagram showing multiple pedestrians passing by. [Figure 3B] FIG. 4 is a diagram showing an example of three-dimensional data generated by a generating unit. [Figure 4A] FIG. 1 is a diagram showing an example of a three-dimensional space seen from a lidar's viewpoint. [Figure 4B] FIG. 13 is a diagram showing an example of an image showing a detection result of a moving object. [Figure 5A] FIG. 11 is a diagram for explaining a process of combining moving objects. [Figure 5B] FIG. 11 is a diagram for explaining a process of combining moving objects. [Figure 6] 4 is a flowchart showing an example of processing executed by a CPU of the controller of FIG. 1; DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. An object detection device according to an embodiment of the present invention can be applied to a vehicle having an automatic driving function, that is, an automatic driving vehicle. The vehicle to which the object detection 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 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 an autonomous vehicle is traveling in an autonomous driving mode (hereinafter referred to as autonomous driving or self-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 traveling trajectory (target trajectory) for a predetermined time ahead from the current time, and controls the traveling actuators so that the vehicle travels along the target trajectory.

[0010] 1 is a block diagram showing the main configuration of a vehicle control device 100 including an object detection 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 object detection device 50 that constitutes a part of the vehicle control device 100. The object detection device 50 detects objects around the vehicle based on detection data from the lidar 5.

[0011] 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 using the positioning information received by the positioning sensor.

[0012] The internal sensor group 3 is a collective term for a plurality of 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 acceleration in the forward and backward directions and the acceleration in the left and right directions (lateral acceleration) of the vehicle, a rotation speed sensor that detects the rotation speed of the driving source, a yaw rate sensor that detects the rotation angular velocity around the vertical axis of the center of gravity of the vehicle, etc. 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, the operation of the steering wheel, etc.

[0013] The camera 4 has an imaging element such as a CCD or a CMOS, and captures the surroundings (front, rear, and sides) of the vehicle. The lidar 5 irradiates electromagnetic waves (reflected waves) into a three-dimensional space around the vehicle, and detects the external situation around the vehicle based on the reflected waves. More specifically, the electromagnetic waves (laser light, etc.) irradiated by the lidar 5 are reflected at a certain point (measurement point) on the surface of the object and return, thereby measuring the distance from the laser source to that point, the intensity of the reflected electromagnetic waves, the relative speed of the object located at that measurement point, and the like. The electromagnetic waves of the lidar 5 attached to a predetermined position (front) of the vehicle are scanned in the horizontal and vertical directions around the vehicle (forward), thereby detecting the position, shape, relative movement speed, and the like of an object in front of the vehicle (moving objects such as other vehicles and stationary objects such as road surfaces and structures). The objects detected by the lidar 5 are called objects, including people. Therefore, moving objects include moving people (pedestrians, etc.) in addition to moving vehicles such as automobiles and bicycles. In the following, the above three-dimensional space is represented by an X-axis along the traveling direction of the host vehicle, a Y-axis along the width direction of the host vehicle, and a Z-axis along the height direction of the host vehicle. Therefore, the above three-dimensional space may be called an XYZ space.

[0014] The actuators AC are driving actuators for controlling the driving of the host vehicle. When the driving source for driving is an engine, the actuators AC include a throttle actuator that adjusts the opening of a throttle valve (throttle opening) 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 that operates the braking device of the host vehicle and a steering actuator that drives the steering device.

[0015] The controller 10 is composed of an electronic control unit (ECU). More specifically, the controller 10 includes a computer having a calculation unit 11 such as a CPU (microprocessor), a storage unit 12 such as a ROM, 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. 1 as a collection of these ECUs for the sake of convenience.

[0016] The memory unit 12 stores highly accurate, detailed map information (referred to as high-accuracy map information). The high-accuracy map information includes road position information, road shape (curvature, etc.) information, road gradient information, intersection and branch point position information, number of lanes (driving lanes) information, lane width and position information for each lane (information on lane center positions and lane position boundary lines), 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.

[0017] The calculation unit 11 has, as functional components, a data acquisition unit 111, an estimation unit 112, a calculation unit 113, a classification unit 114, a conversion unit 115, a generation unit 116, an object detection unit (hereinafter simply referred to as a detection unit) 117, and a driving control unit 118. As shown in Fig. 1, the data acquisition unit 111, the estimation unit 112, the calculation unit 113, the classification unit 114, the conversion unit 115, the generation unit 116, and the detection unit 117 are included in an object detection device 50. Details of the data acquisition unit 111, the estimation unit 112, the calculation unit 113, the classification unit 114, the conversion unit 115, the generation unit 116, and the detection unit 117 included in the object detection device 50 will be described later.

[0018] In the automatic driving mode, the driving control unit 118 generates a target trajectory based on the external conditions around the vehicle including the size, position, relative moving speed, etc. of an object detected by the object detection device 50. Specifically, the driving control unit 118 generates a target trajectory based on the size, position, relative moving speed, etc. of the object detected by the object detection device 50 so as to avoid collision or contact with the object or to follow the object. The driving control unit 118 controls the actuator AC so that the host vehicle travels along the target trajectory. Specifically, the actuator AC is controlled along the target trajectory to adjust the accelerator opening and drive the braking device and the steering device. In the manual driving mode, the driving control unit 118 controls the actuator AC according to a driving command (such as a steering operation) from the driver acquired by the internal sensor group 3.

[0019] The object detection device 50 will be described in detail. As described above, the object detection device 50 includes the data acquisition unit 111, the estimation unit 112, the calculation unit 113, the classification unit 114, the conversion unit 115, the generation unit 116, and the detection unit 117. The object detection device 50 further includes a LIDAR 5.

[0020] The data acquisition unit 111 acquires four-dimensional data (hereinafter referred to as point cloud data) including position information indicating three-dimensional position coordinates of measurement points on the surface of an object from which reflected waves of the LIDAR 5 are obtained, and speed information indicating the relative movement speed of the measurement points, as detection data of the LIDAR 5. The point cloud data is acquired by the LIDAR 5 on a frame-by-frame basis, specifically, at predetermined time intervals (time intervals determined by the frame rate of the LIDAR 5).

[0021] The estimation unit 112 estimates the absolute moving speed of the host vehicle based on the point cloud data acquired by the data acquisition unit 111. Here, the estimation of the absolute moving speed of the host vehicle by the estimation unit 112 will be described.

[0022] First, the estimation unit 112 extracts point cloud data from the point cloud data acquired by the data acquisition unit 111, excluding information on measurement points corresponding to three-dimensional objects, that is, point cloud data corresponding to the road surface around the vehicle (hereinafter referred to as road surface point cloud data). The estimation unit 112 extracts the road surface point cloud data, that is, the measurement points P i 4-dimensional data (x i , y i ,z i ,v i ) i , y i ,z i ) based on the relative movement speed v i The unit vector e indicating the direction of i Calculate.

[0023]

number

[0024] Next, the estimation unit 112 estimates the vehicle's moving speed (absolute moving speed) V self Specifically, the estimation unit 112 estimates the measurement point P i The relative movement speed v i The transformation equation for converting the measured point P into the absolute moving speed is set as the objective function L, and the optimization problem is solved to optimize the objective function L so that it approaches zero. i are measurement points on the road surface, the absolute speed of each measurement point should be zero. Therefore, by optimizing the objective function L to approach zero, the correct V self can be estimated. V self is expressed by the velocity components in the XYZ axis directions as shown in the following equation (ii). The objective function L is expressed by the following equation (iii). By solving the above optimization problem, V self is searched. Note that V self The initial value of may be set to zero, or it may be set to the estimated value of V self may be set.

[0025]

number

number

[0026] In equation (iii), A is the unit vector e of n measurement points corresponding to the road surface. i In addition, in the equation (iii), V is a matrix of n measurement points P i is a 1×n matrix representing the velocity components (relative movement velocities) of the vectors V, V, and V are expressed by the formula (v). The estimation unit 112 estimates V obtained by solving the optimization problem. self is obtained as an estimate of the absolute moving speed of the host vehicle in the current frame.

[0027]

number

number

[0028] The calculation unit 113 calculates the absolute moving speed V of the vehicle estimated by the estimation unit 112. self Based on the above, the absolute moving speed of all the measurement points, more specifically, all the measurement points including the measurement points corresponding to the three-dimensional object, is calculated. Here, the calculated absolute moving speed has a negative value when approaching the host vehicle, and has a positive value when moving away.

[0029] The classification unit 114 classifies the point cloud data acquired by the data acquisition unit 111 into moving point cloud data corresponding to measurement points where the absolute value of the absolute moving speed calculated by the calculation unit 113 is equal to or greater than a predetermined speed Th_V, and still point cloud data corresponding to measurement points where the absolute value is less than the predetermined speed Th_V.

[0030] 2A and 2B are diagrams showing an example of a solid object included in a three-dimensional space around the vehicle. FIG. 2A shows a moving object (a bicycle CY and a person RD riding the bicycle CY) traveling in front of the vehicle in the traveling direction (X direction) of the vehicle. FIG. 2B shows a plan view of the moving object in FIG. 2A seen from above (Z direction). As shown in FIG. 2B, the maximum size (Xmax and Ymax) of the solid object in the XY direction can be recognized even without information on the height direction (Z direction) of the solid object. Therefore, the conversion unit 115 projects each of the measurement points corresponding to the moving point cloud data onto a plane so as to remove information on the height direction from the position information of each measurement point, thereby converting the position information of each measurement point from three dimensions to two dimensions. Specifically, when the position coordinates of each measurement point are expressed in the XYZ coordinate system, the conversion unit 115 projects each measurement point corresponding to the moving point cloud data onto the XY plane, thereby converting the moving point cloud data into two-dimensional data expressed in the XY coordinate system.

[0031] The generating unit 116 adds the absolute moving speed calculated by the calculating unit 113 to the moving point cloud data converted into two-dimensional data to generate three-dimensional data (hereinafter referred to as XYV data or speed-added data). More specifically, the generating unit 116 adds the absolute moving speed corresponding to each measurement point to each of the position information of each measurement point included in the moving point cloud data converted into two dimensions by the converting unit 115.

[0032] The detection unit 117 detects moving objects around the vehicle based on the XYV data generated by the generation unit 116. More specifically, the detection unit 117 executes a clustering process on the XYV data to detect a bounding box, which is a circumscribing area of ​​the moving object, from the XY plane. Any method such as DBSCAN (Density-based spatial clustering of applications with noise) or the K-means method may be used for the clustering process. The detection unit 117 further detects the position and size of the moving object in a three-dimensional space (XYZ space) based on the position and size of the detected circumscribing area. The detection unit 117 outputs information (image information, etc.) indicating the detection result of the moving object to a display device (not shown) or the like.

[0033] Here, the detection accuracy of the moving object by the detection unit 117 will be described. Fig. 3A is a diagram showing a state where a plurality of pedestrians are passing by. Fig. 3A shows a state where pedestrians HM32 and HM34 are moving (walking) in the same direction (X-axis direction), and pedestrians HM31 and HM33 are moving (walking) in the opposite direction. The absolute values ​​of the absolute movement speeds V1, V2, V3, and V4 of the pedestrians HM31, HM32, HM33, and HM34 are equal to or greater than a predetermined speed Th_V.

[0034] FIG. 3B is a diagram showing an example of three-dimensional data (XYV data) generated by the generating unit 116. FIG. 3B shows XYV data obtained by adding absolute movement speeds V1 to V4 of the pedestrians HM31 to HM34 to two-dimensional data obtained by projecting the measurement point clouds (clusters) PC1 to PC4 corresponding to the pedestrians HM31 to HM34 in FIG. 3A onto the XY plane. In FIG. 3B, the measurement point clouds of each pedestrian are drawn in a color according to the absolute movement speed of each pedestrian. For the sake of simplicity of explanation, it is assumed that the absolute movement speeds V1, V2, and V4 of the pedestrians HM31, HM32, and HM34 are equal to each other. Therefore, in FIG. 3B, the measurement point clouds PC1, PC2, and PC4 corresponding to the pedestrians HM31, HM32, and HM34 are drawn in the same color (white), and the measurement point cloud PC3 corresponding to the pedestrian HM33 is drawn in a different color (black).

[0035] FIG. 3B also shows bounding boxes BB1 to BB4 corresponding to the measurement point groups PC1 to PC4 detected by performing clustering processing on the XYV data. In the example shown in FIG. 3B, since the measurement point groups PC2 and PC3 are close to each other, the measurement point groups PC2 and PC3 may be recognized as one measurement point group in the clustering processing and may be included in one bounding box. That is, there is a risk that the pedestrian HM32 and the pedestrian HM33 may be detected as one (as one pedestrian). However, since the speed information is taken into consideration in classifying the measurement points by the above-mentioned clustering processing, the pedestrians HM32 and HM33 moving at different absolute movement speeds are detected as different moving objects even if they are close to each other. As a result, as shown in FIG. 3B, the bounding boxes BB2 and BB3 are assigned to the measurement point groups PC2 and PC3 corresponding to the pedestrians HM32 and HM33, respectively.

[0036] 4A and 4B are diagrams for explaining the detection result of a moving object by the detection unit 117. Here, the detection result of a moving object by the detection unit 117 is explained using the detection data of a lidar installed in a concourse in a commercial facility as shown in FIG. 4A as an example, instead of the detection data of a lidar mounted on a vehicle. FIG. 4A shows the state of a concourse AS in a commercial facility as seen from the viewpoint of a lidar. As shown in FIG. 4A, when there are many pedestrians in the concourse AS, the pedestrians approach each other, so that there is a risk that the approaching multiple pedestrians are detected as a single unit (as one pedestrian). However, by performing the clustering process in which the speed information as described above is taken into consideration in the detection unit 117, even if the detection data of the lidar includes moving objects moving while being close to each other, the bounding box corresponding to each moving object can be detected with high accuracy.

[0037] FIG. 4B shows an example of a detection result of a moving object by the detection unit 117 for the three-dimensional space of FIG. 4A. The lightly shaded area in FIG. 4B represents a measurement point cloud corresponding to a stationary object (wall WL of the concourse AS in FIG. 4A). The darkly shaded area represents a measurement point cloud corresponding to a moving object (pedestrian moving in the concourse AS in FIG. 4A). The rectangular frame represents a bounding box corresponding to each moving object (pedestrian) detected by the clustering process of the detection unit 117. Note that, although FIG. 4B shows an example of an image in which a bounding box corresponding to each moving object is superimposed on the detection data (point cloud data) of the lidar, the detection unit 117 may output an image in which a bounding box corresponding to each moving object is superimposed on an image captured by the camera 4 instead of the point cloud data as a detection result of the moving object. In FIG. 4A, a pedestrian HM42 moving toward the front side of the concourse AS and a pedestrian HM41 moving toward the rear side just to the side of the pedestrian HM42 are approaching each other, but since their moving speeds are different, they are detected as different moving objects by the detection unit 117. As a result, the detection result in FIG. 4B displays bounding boxes BB41 and BB42 corresponding to the pedestrians HM41 and HM42, respectively. There are also multiple pedestrians close to each other on the right rear side of the concourse AS, but these pedestrians are also detected as different moving objects by the detection unit 117, so the detection result in FIG. 4B displays bounding boxes corresponding to each of them. In this way, according to the above-mentioned clustering process by the detection unit 117, even if the detection data of the rider 5 includes many moving objects approaching each other, each moving object can be accurately detected as a different moving object.

[0038] However, depending on the type of moving object, the absolute moving speed of the main body of the moving object may differ from the absolute moving speed of the parts attached to the main body. For example, since a pedestrian moves while moving his / her limbs, the absolute moving speeds calculated by the calculation unit 113 may differ between the torso and the limbs of the moving pedestrian. In such a case, when a clustering process is performed in consideration of the speed information as described above, the torso and the limbs of the pedestrian may be detected as separate moving objects. Therefore, in order to address such a problem, the detection unit 117 executes a combining process of moving objects as shown below.

[0039] 5A and 5B are diagrams for explaining the combining process of moving objects. FIG. 5A shows a schematic diagram of a pedestrian HM51 moving in the X direction and a pedestrian HM52 moving opposite to the pedestrian HM51, as viewed from the Y direction. In FIG. 5A, bounding boxes set by the clustering process by the detection unit 117 are shown as rectangular frames BD1, BD2, PT11 to PT14, and PT21 to PT24. The bounding boxes BD1 and BD2 correspond to the torsos of the pedestrians HM51 and HM52. The bounding boxes PT11, PT12, PT21, and PT22 correspond to the arms of the pedestrians HM51 and HM52. The bounding boxes PT13, PT14, PT23, and PT24 correspond to the legs of the pedestrians HM51 and HM52.

[0040] The detection unit 117 judges whether or not there is a bounding box (hereinafter referred to as a bounding box to be joined, or simply referred to as a bounding box to be joined) that is less than a predetermined distance from other bounding boxes whose sizes are equal to or larger than a predetermined threshold, and whose own size is less than a predetermined threshold, among the bounding boxes set by the clustering process. If it is judged that a bounding box to be joined exists, the detection unit 117 joins the bounding box to the other bounding box (hereinafter referred to as a bounding box to be joined, or simply referred to as a bounding box to be joined). In the example of FIG. 5A, the bounding boxes PT11 to PT14 are selected as the bounding boxes to be joined, and the bounding box BD1 is selected as the bounding box to be joined corresponding to the bounding boxes PT11 to PT14. As a result, the bounding boxes PT11 to PT14 are joined to the bounding box BD1. Similarly, the bounding boxes PT21 to PT24 are joined to the bounding box BD2. This makes it possible to prevent the torsos and limbs of pedestrians HM51 and HM52 from being detected as separate moving objects. Note that bounding boxes BD1 and BD2, whose sizes are equal to or larger than a predetermined threshold, are boxes to be joined, and therefore are not joined even if the distance between them is less than a predetermined length. Also, PT11-PT14 and PT21-PT24, whose sizes are less than a predetermined threshold, are boxes to be joined, and therefore are not joined even if the distance between them is less than a predetermined length.

[0041] In FIG. 5A, the torso and limbs of a pedestrian are described as an example. However, even in a vehicle traveling around the host vehicle, the absolute moving speeds calculated by the calculation unit 113 may differ between the main body of the vehicle and parts such as wheels attached to the main body. FIG. 5B shows a schematic diagram of a vehicle CA moving in the X direction as viewed from the Y direction. In FIG. 5B, bounding boxes set by the clustering process by the detection unit 117 are shown as rectangular frames BD3, PT31, and PT32. The bounding box BD3 corresponds to the main body of the vehicle CA. The bounding boxes PT31 and PT32 correspond to the front and rear wheels of the vehicle CA. The main body of the vehicle CA and parts such as wheels attached to the main body have the same moving speed in the traveling direction of the vehicle, but since their relative positions with respect to the rider 5 are different, the calculation unit 113 may calculate different relative moving speeds. Even in such a case, the bounding boxes PT31 and PT32 can be combined with the bounding box BD3 by performing the combining process as described above. As a result, it is possible to prevent the main body and parts of the vehicle CA from being detected as separate moving objects.

[0042] 6 is a flowchart showing an example of a process executed by the calculation unit 11 of the controller 10 in FIG. 1 in accordance with a predetermined program. The process shown in this flowchart is repeated at a predetermined cycle while the object detection device 50 is running. More specifically, the process is repeated at a cycle according to the frame rate of the LIDAR 5.

[0043] First, in step S1, the external environment surrounding the vehicle is detected. Specifically, an irradiation command is sent to the LIDAR 5, and point cloud data (detection data) including position information and speed information of measurement points at which reflected waves of electromagnetic waves irradiated from the LIDAR 5 in response to the irradiation command are obtained is acquired. In step S2, the point cloud data acquired in step S1 is classified into moving point cloud data and stationary point cloud data. More specifically, the point cloud data is classified into moving point cloud data corresponding to measurement points whose absolute values ​​of absolute moving speeds are equal to or greater than a predetermined speed Th_V, and stationary point cloud data corresponding to other measurement points.

[0044] Next, the processing of steps S3 to S6 is executed for the moving point cloud data. Note that the controller 10 also executes a predetermined processing for the stationary point cloud data, but the description thereof will be omitted.

[0045] In step S3, each measurement point corresponding to the moving point cloud data is projected onto an XY plane, and the moving point cloud data is converted into two-dimensional data represented in an XY coordinate system. In step S4, a clustering process is executed. Specifically, first, the absolute moving speed corresponding to each measurement point calculated in step S2 is added to each piece of position information of each measurement point included in the moving point cloud data converted into two-dimensional data in step S3, to generate three-dimensional data (XYV data). Next, a clustering process is executed on the generated XYV data.

[0046] In step S5, it is determined whether or not there is a to-be-combined box among the bounding boxes detected by the clustering process in step S4. If the result in step S5 is negative, the process proceeds to step S7. On the other hand, if the result in step S5 is positive, in step S6, a combining process is executed to combine the to-be-combined box with the corresponding to-be-combined box, and then the process proceeds to step S7. In step S7, the positions and sizes of the moving objects around the host vehicle in a three-dimensional space (XYZ space) are detected based on the positions and sizes of the bounding boxes detected by the clustering process in step S4. Note that when the bounding boxes are combined in the combining process in step S6, the positions and sizes of the moving objects in a three-dimensional space are detected based on the positions and sizes of the bounding boxes after the combination.

[0047] According to the embodiment described above, the following advantageous effects are achieved. (1) The object detection device 50 includes a lidar 5 mounted on a host vehicle and configured to irradiate electromagnetic waves into a three-dimensional space around the host vehicle and detect an external environment around the host vehicle body based on the reflected waves, a data acquisition unit 111 that acquires point cloud data including three-dimensional position information of measurement points on the surface of an object from which reflected waves are obtained and first speed information indicating the relative movement speed of the measurement points, an estimation unit 112 serving as a speed acquisition unit that acquires second speed information indicating the absolute movement speed of the host vehicle, a calculation unit 113 that calculates the absolute movement speed of each of a plurality of measurement points corresponding to the point cloud data based on the first speed information and the second speed information, and an estimation unit 112 that estimates the absolute movement speed of the measurement points based on the absolute movement speed calculated by the calculation unit 113. The apparatus includes a classification unit 114 that classifies the moving point cloud data into moving point cloud data corresponding to measurement points whose absolute value of the moving speed is equal to or greater than a predetermined speed Th_V, and stationary point cloud data other than the moving point cloud data, a conversion unit 115 that converts three-dimensional position information of each measurement point corresponding to the moving point cloud data into two-dimensional position information, a generation unit 116 that generates speed-added data by adding the absolute moving speed corresponding to each measurement point calculated by the calculation unit 113 to the two-dimensional position information of each measurement point obtained by the conversion by the conversion unit 115, and a detection unit 117 that detects an object included in the three-dimensional space around the vehicle based on the speed-added data generated by the generation unit 116. The conversion unit 115 projects each measurement point corresponding to the moving point cloud data onto an XY plane, and converts the three-dimensional position information of each measurement point into two-dimensional position information. The detection unit 117 executes a predetermined clustering process on the speed-added data to detect a circumscribing area of ​​the object from the XY plane, and detects the position and size of the object in the three-dimensional space based on the position and size of the circumscribing area. This allows moving objects that are close to each other, such as pedestrians passing each other, to be detected as separate moving objects. As a result, the accuracy of detecting moving objects can be improved. In addition, because the clustering process is performed on the measurement point clouds corresponding to moving objects, the calculation load required for detecting moving objects can be reduced compared to when the clustering process is performed on the measurement point clouds corresponding to stationary objects as well.

[0048] (2) When detecting multiple circumscribing areas corresponding to multiple objects from the XY plane, if the distance between a first circumscribing area whose size is less than a predetermined threshold and a second circumscribing area whose size is equal to or greater than the predetermined threshold is less than a predetermined length, the detection unit 117 combines the first circumscribing area with the second circumscribing area. This allows a moving object consisting of a body and parts, such as the torso and limbs of a person or the body and wheels of a vehicle, to be properly recognized as a single moving object.

[0049] (3) The estimation unit 112 estimates the absolute moving speed of the host vehicle based on the position information and speed information of the representative measurement point extracted from the point cloud data acquired by the data acquisition unit 111, and acquires the estimation result as speed information. The representative measurement point is selected from the remaining measurement points excluding the measurement points corresponding to the three-dimensional object from the multiple measurement points. In this way, the absolute moving speed of the host vehicle is estimated based on the measurement point corresponding to the road surface. As a result, the absolute moving speed of the host vehicle can be estimated with high accuracy. In addition, since the absolute moving speed of the host vehicle (moving body) is estimated and acquired without relying on sensor values ​​such as a vehicle speed sensor, the present invention can also be applied to a self-propelled robot that does not have a vehicle speed sensor, etc.

[0050] The above embodiment can be modified into various forms. Modifications will be described below. In the above embodiment, the LIDAR 5 as a detector is mounted on a vehicle, and the LIDAR 5 irradiates electromagnetic waves into a three-dimensional space around the vehicle and detects the external environment around the vehicle based on the reflected waves. However, the detector may be something other than a LIDAR, such as a radar. In addition, the moving body on which the detector is mounted may be something other than a vehicle, such as a self-propelled robot.

[0051] In the above embodiment, the conversion unit 115 converts the moving point cloud data obtained by the classification unit 114 into two-dimensional data, the generation unit 116 adds the absolute moving speed calculated by the calculation unit 113 to the moving point cloud data converted into two-dimensional data to generate three-dimensional speed-added data (XYV data), and the detection unit 117 executes clustering processing on the XYV data to detect moving objects around the vehicle. However, when accuracy of the cluster size in three-dimensional space (XYZ space) is required, the above-mentioned clustering processing may be executed on the XYZ space. Specifically, the generation unit may add the absolute moving speed calculated by the calculation unit 113 to the moving point cloud data obtained by the classification unit 114 to generate four-dimensional speed-added data (hereinafter referred to as XYZV data). Then, the detection unit may execute clustering processing on the XYZV data.

[0052] In the above embodiment, the estimation unit 112 as a speed acquisition unit selects a representative measurement point P from the remaining measurement points after removing the measurement points corresponding to the three-dimensional object from the plurality of measurement points. i The speed acquisition unit 111 selects a representative measurement point and estimates the absolute moving speed of the vehicle based on the position information and speed information of the representative measurement point extracted from the point cloud data acquired by the data acquisition unit 111, and acquires the estimation result as the second speed information. However, the speed acquisition unit may acquire the measurement result of the absolute moving speed of the vehicle acquired by a measuring instrument included in the internal sensor group 3 as the second speed information. In this case, the object detection device 50 includes at least a vehicle speed sensor of the internal sensor group 3 as a measuring instrument. In addition, the speed acquisition unit may calculate and acquire the absolute moving speed of the vehicle based on the current position of the vehicle measured by the positioning unit 2. In this case, the object detection device 50 includes the positioning unit 2.

[0053] In the above embodiment, the driving control unit 118 controls the driving of the vehicle so as to avoid collision or contact with the object detected by the detection unit 117. However, the driving control unit 118 may function as a notification unit to predict the possibility of collision or contact with the moving object based on the size, position, and moving speed of the moving object detected by the detection unit 117. When the possibility of collision or contact with the moving object is equal to or greater than a predetermined level, the driving control unit 118 may notify the occupants of the vehicle of attention-calling information (video information or audio information) regarding the collision or contact with the moving object detected by the detection unit 117 via a display and a speaker (not shown) provided in the vehicle control device 100.

[0054] Furthermore, in the above embodiment, the object detection device 50 is applied to an autonomous vehicle, but the object detection device 50 can also be applied to vehicles other than autonomous vehicles. For example, the object detection device 50 can also be applied to a manually driven vehicle equipped with an ADAS (Advanced Driver Assistance Systems).

[0055] 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 features 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. [Explanation of symbols]

[0056] 5 Lidar, 10 Controller, 11 Calculation unit, 12 Memory unit, 111 Data acquisition unit, 112 Estimation unit, 113 Calculation unit, 114 Classification unit, 115 Conversion unit, 116 Generation unit, 117 Detection unit, 118 Travel control unit, 50 Object detection device, 100 Vehicle control device, AC actuator

Claims

1. a detector mounted on the moving body, irradiating electromagnetic waves into a three-dimensional space around the moving body and detecting an external environment around the moving body based on reflected waves; a data acquisition unit that acquires from the detector point cloud data including three-dimensional position information of measurement points on a surface of an object from which the reflected waves are obtained and first velocity information indicating relative movement velocities of the measurement points; a speed acquisition unit that acquires second speed information indicating an absolute moving speed of the moving object; a calculation unit that calculates an absolute moving speed of each of the plurality of measurement points corresponding to the point cloud data based on the first speed information and the second speed information; a classification unit that classifies the point cloud data into moving point cloud data corresponding to the measurement points where the absolute value of the absolute moving speed calculated by the calculation unit is equal to or greater than a predetermined speed, and stationary point cloud data other than the moving point cloud data; a conversion unit that converts the three-dimensional position information of each measurement point corresponding to the moving point cloud data into two-dimensional position information; a generating unit that generates speed-added data by adding the absolute movement speed corresponding to each of the measurement points calculated by the calculating unit to the two-dimensional position information of each of the measurement points obtained by the conversion by the converting unit; an object detection unit that detects the object included in the three-dimensional space around the moving object based on the speed-added data generated by the generation unit.

2. 2. The object detection device according to claim 1, the conversion unit projects each of the measurement points corresponding to the moving point cloud data onto a plane and converts the three-dimensional position information of each of the measurement points into the two-dimensional position information; The object detection device is characterized in that the detection unit performs a predetermined clustering process on the velocity-added data to detect a circumscribing area of ​​the object from the plane, and detects a position and a size of the object in the three-dimensional space based on the position and size of the circumscribing area.

3. 3. The object detection device according to claim 2, The object detection device is characterized in that, when the detection unit detects multiple circumscribing areas corresponding to multiple objects from the plane, if the distance between a first circumscribing area whose size is less than a predetermined threshold and a second circumscribing area whose size is equal to or greater than the predetermined threshold is less than a predetermined length, the detection unit combines the first circumscribing area with the second circumscribing area.

4. 2. The object detection device according to claim 1, the speed acquisition unit estimates an absolute moving speed of the moving object based on the position information of the representative measurement point and the first speed information extracted from the point cloud data acquired by the data acquisition unit, and acquires the estimation result as the second speed information; An object detection device, characterized in that the representative measurement point is selected from among the remaining measurement points excluding the measurement point corresponding to a three-dimensional object from the plurality of measurement points.

5. 2. The object detection device according to claim 1, a measuring device for measuring the absolute moving speed of the moving object, The object detection device, wherein the speed acquisition unit acquires the measurement result of the measuring instrument as the second speed information.

6. The object detection device according to any one of claims 1 to 5, The object detection device, characterized in that the detector is a lidar.

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