Object detection device

Abstract

An object detection device (50) is provided with: a lidar (5); a data acquisition unit (111) that acquires, from the lidar (5), point cloud data including three-dimensional position information of a measurement point on the surface of the object and first velocity information indicating the relative movement velocity of the measurement point; an estimation unit (112) 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 the plurality of measurement points on the basis of the first speed information and the second speed information; a classification unit (114) that classifies the point cloud data into moving point cloud data corresponding to the measurement points at which the absolute value of the absolute movement speed is equal to or greater than a prescribed speed, 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; and a detection unit (117) that detects objects around the host vehicle on the basis of speed addition data obtained by adding the absolute movement speed corresponding to each measurement point to the two-dimensional position information of each measurement point.

Description

Technical Field

[0001] The present invention relates to an object detection device for detecting objects around a vehicle. Background Art

[0002] As such a device, there is known a device that detects a moving object using three-dimensional point cloud data acquired by a laser radar (for example, see Patent Document 1).

[0003] However, when point cloud data is directly used for the detection process of a moving object as in the device described in Patent Document 1, there is a possibility that the calculation load in the detection process will increase.

[0004] Prior art literature

[0005] Patent Literature

[0006] Patent document 1: Japanese Patent No. 7126633. Summary of the invention

[0007] An object detection device according to a technical solution of the present invention comprises: a detector mounted on a moving body, irradiating electromagnetic waves into a three-dimensional space around the moving body, and detecting the external conditions around the moving body based on the reflected waves; a data acquisition unit, which acquires point cloud data from the detector, wherein the point cloud data includes three-dimensional position information of a measurement point on the surface of the object from which the reflected waves are obtained and first speed information indicating the relative moving speed of the measurement point; a speed acquisition unit, which acquires second speed information indicating the absolute moving speed of the moving body; a calculation unit, which calculates the absolute moving 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 a classification unit, which classifies Point cloud data are classified into moving point cloud data corresponding to measurement points whose absolute moving speed calculated by a calculation unit is greater than a specified 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, wherein the speed-added data is the absolute moving speed corresponding to each measurement point calculated by the calculation unit added to the two-dimensional position information of each measurement point obtained by the conversion of the conversion unit; and an object detection unit that detects an object contained in the three-dimensional space around the moving body based on the speed-added data generated by the generation unit. BRIEF DESCRIPTION OF THE DRAWINGS

[0008] The objects, features and advantages of the present invention will be further clarified through the following description of the embodiments in conjunction with the accompanying drawings.

[0009] Figure 1 1 is a block diagram showing a main part configuration of a vehicle control device including the object detection device according to the embodiment of the present invention.

[0010] Figure 2A This is a diagram showing an example of three-dimensional objects included in the three-dimensional space around the host vehicle.

[0011] Figure 2B Observed from above Figure 2A A top view of a moving object.

[0012] Figure 3A This is a diagram showing a plurality of pedestrians traveling.

[0013] Figure 3B This is a diagram showing an example of three-dimensional data generated by the generating unit.

[0014] Figure 4A This is a diagram showing an example of a three-dimensional space observed from the perspective of a laser radar.

[0015] Figure 4B FIG. 1 is a diagram showing an example of an image of a detection result of a moving object.

[0016] Figure 5A It is a diagram for explaining the combining process of moving objects.

[0017] Figure 5B It is a diagram for explaining the combining process of moving objects.

[0018] Figure 6 It is shown by Figure 1 A flowchart of an example of a process executed by the CPU of the controller. DETAILED DESCRIPTION

[0019] The following describes the embodiments of the present invention with reference to the accompanying drawings. The object detection device of the embodiment of the present invention can be applied to a vehicle with an automatic driving function, that is, an automatic driving vehicle. It should be noted that sometimes, the vehicle to which the object detection device of the present embodiment is applied is referred to as the present vehicle, as distinguished from other vehicles. The present vehicle can 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 present vehicle can not only travel in an automatic driving mode that does not require the driver's driving operation, but also travel in a manual driving mode in which the driver performs driving operations.

[0020] When an autonomous vehicle is driving in autonomous driving mode (hereinafter referred to as automatic driving or autonomous driving), it recognizes the external conditions around the vehicle based on the detection data of 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) from the current time point to a specified time, and controls the driving actuators so that the vehicle drives along the target trajectory.

[0021] Figure 1 1 is a block diagram showing the main structure of a vehicle control device 100 including an object detection device. The vehicle control device 100 includes a controller 10, a communication unit 1, a positioning unit 2, an internal sensor group 3, a camera 4, a laser radar 5, and an actuator AC for driving. In addition, the vehicle control device 100 includes an object detection device 50 constituting a part of the vehicle control device 100. The object detection device 50 detects objects around the vehicle based on detection data of the laser radar 5.

[0022] The communication unit 1 communicates with various servers not shown in the figure through a network including a wireless communication network represented by the Internet, a mobile phone network, etc., and obtains map information, driving history information, traffic information, etc. from the server regularly or at any time. The network includes not only a public wireless communication network, but also a closed communication network set up in each specified management area, such as a wireless LAN, Wi-Fi (registered trademark), Bluetooth (registered trademark), etc. 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 sent from a positioning satellite. The positioning satellite is an artificial satellite such as a GPS satellite and a quasi-zenith satellite. The positioning unit 2 uses the positioning information received by the positioning sensor to determine the current position (latitude, longitude, altitude) of the vehicle.

[0023] The internal sensor group 3 is a general term for a plurality of sensors (internal sensors) for detecting the driving state of the vehicle. For example, the internal sensor group 3 includes a vehicle speed sensor for detecting the vehicle speed, an acceleration sensor for detecting the acceleration (lateral acceleration) of the vehicle in the front-rear direction and the left-right direction, a rotation speed sensor for detecting the rotation speed of the driving source, and a yaw rate sensor for detecting the angular velocity of the center of gravity of the vehicle around the vertical axis. Sensors for detecting the driving operation of the driver in the manual driving mode, such as the operation of the accelerator pedal, the operation of the brake pedal, and the operation of the steering wheel, are also included in the internal sensor group 3.

[0024] The camera 4 has a CCD (charge coupled device), a CMOS (complementary metal oxide semiconductor) and other imaging elements, and captures the surroundings (front, rear and side) of the vehicle. The laser radar 5 irradiates electromagnetic waves (reflected waves) into the three-dimensional space around the vehicle, and detects the external conditions around the vehicle based on the reflected waves. In more detail, the electromagnetic waves (laser, etc.) irradiated by the laser radar 5 are reflected and returned at a certain point (measurement point) on the surface of the object, thereby measuring the distance from the laser source to the point, the intensity of the reflected electromagnetic waves, the relative speed of the object at the measurement point, etc. The electromagnetic waves of the laser radar 5 installed at a predetermined position (front) of the vehicle scan the surroundings (front) of the vehicle in the horizontal and vertical directions, thereby detecting the position, shape, relative movement speed, etc. of the object in front of the vehicle (moving objects such as other vehicles, road surfaces, structures and other stationary objects). Objects detected by the laser radar 5, including people, are called objects. Therefore, moving objects include moving people (pedestrians, etc.) in addition to moving vehicles such as cars and bicycles. It should be noted that, hereinafter, the three-dimensional space is represented by an X-axis along the traveling direction of the vehicle, a Y-axis along the vehicle width direction, and a Z-axis along the height direction of the vehicle. Therefore, the three-dimensional space is sometimes referred to as an XYZ space.

[0025] The actuator AC is a driving actuator for controlling the driving of the vehicle. When the driving source is an engine, the actuator AC includes a throttle actuator for adjusting the opening of the throttle valve of the engine (throttle opening). When the driving source is a driving motor, the driving motor is included in the actuator AC. The brake actuator that operates the braking device of the vehicle and the steering actuator that drives the steering device are also included in the actuator AC.

[0026] The controller 10 includes an electronic control unit (ECU). More specifically, the controller 10 includes a computer having a computing unit 11 such as a CPU (microprocessor), a storage unit 12 such as a ROM (read-only memory) and a RAM (random access memory), and other peripheral circuits not shown in the figure, such as an I / O (input / output) interface. It should be noted that multiple ECUs with different functions, such as an engine control ECU, a travel motor control ECU, and a brake device ECU, can be separately set, but for convenience, Figure 1 The controller 10 is shown in FIG. 1 as a collection of these ECUs.

[0027] The storage unit 12 stores high-precision detailed map information (referred to as high-precision map information). The high-precision map information includes: road location information, road shape (curvature, etc.) information, road slope information, intersection and fork location information, lane number (driving lane) information, lane width and each lane location information (lane center location, lane location boundary line information), location information of landmarks (signals, signs, buildings, etc.) marked on the map, and road surface contour information such as road surface bumps. In addition, the storage unit 12 also stores various control programs, thresholds used in the programs, and setting information for vehicle-mounted detectors such as the laser radar 5.

[0028] The computing unit 11 includes 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 referred to as a detection unit) 117, and a driving control unit 118 as a functional structure. Figure 1 As shown, 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 the object detection device 50. 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 in detail later.

[0029] 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 the object detected by the object detection device 50. Specifically, the driving control unit 118 generates a target trajectory in a manner to avoid collision, contact, or following the object based on the size, position, relative moving speed, etc. of the object detected by the object detection device 50. The driving control unit 118 controls the actuator AC so that the vehicle travels along the target trajectory. Specifically, the actuator AC is controlled along the target trajectory to adjust the accelerator opening, drive the brake device, or the steering device. It should be noted that in the manual driving mode, the driving control unit 118 controls the actuator AC based on the driving instructions (steering operation, etc.) from the driver obtained by the internal sensor group 3.

[0030] 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 the laser radar 5.

[0031] The data acquisition unit 111 acquires four-dimensional data (hereinafter referred to as point cloud data) as detection data of the laser radar 5, and the four-dimensional data includes position information showing the three-dimensional position coordinates of the measurement point on the surface of the object that receives the reflected wave of the laser radar 5 and speed information showing the relative moving speed of the measurement point. The point cloud data is acquired by the laser radar 5 in units of frames, specifically, at predetermined time intervals (time intervals determined by the frame rate of the laser radar 5).

[0032] The estimation unit 112 estimates the absolute moving speed of the vehicle (speed vector of X, Y, and Z coordinates) based on the point cloud data acquired by the data acquisition unit 111. Here, the estimation of the absolute moving speed of the vehicle by the estimation unit 112 will be described.

[0033] First, the estimation unit 112 extracts point cloud data other than information of the measurement point corresponding to the three-dimensional object, that is, point cloud data corresponding to the road surface around the vehicle (hereinafter referred to as road surface point cloud data) from the point cloud data acquired by the data acquisition unit 111. The estimation unit 112 calculates the unit vector ei indicating the direction of the relative moving speed vi from the position coordinates (xi, yi, zi) included in the four-dimensional data (xi, yi, zi, vi) of the measurement point Pi (i=1, 2...n) corresponding to the road surface, that is, the road surface point cloud data, by the following formula (i).

[0034]

[0035] Next, the estimation unit 112 estimates the moving speed (absolute moving speed) Vself of the vehicle. Specifically, the estimation unit 112 sets the conversion formula for converting the relative moving speed vi of the measurement point Pi corresponding to the road surface into the absolute moving speed as the objective function L, and solves the optimization problem of optimizing the objective function L to approach zero. The measurement point Pi is a measurement point on the road surface, so the absolute speed of each of those measurement points should be zero. Therefore, by optimizing the objective function L to approach zero, the correct Vself can be estimated. Vself is shown in the following formula (ii), which is expressed by the velocity component in the XYZ axis direction. The objective function L is expressed by the following formula (iii). By solving the above-mentioned optimization problem, Vself can be obtained so that the right side of formula (iii) is zero. It should be noted that Vself can be set to zero as an initial value, or it can be set to the Vself estimated in the previous frame.

[0036] V self =(v x , v y , v z ) (ii)

[0037] L(V,f(A,V self ))=V+A·V selfT (iii)

[0038] In formula (iii), A is a matrix of unit vectors ei of n measurement points corresponding to the road surface, which is represented by formula (iv). In addition, in formula (iii), V is a 1×n matrix representing the velocity components (relative moving speed) of n measurement points Pi corresponding to the road surface, which is represented by formula (v). The estimation unit 112 obtains Vself obtained by solving the above-mentioned optimization problem as an estimated value of the absolute moving speed of the vehicle in the current frame.

[0039]

[0040] V = [v1, v2, ..., v n ] T (v)

[0041] The calculation unit 113 calculates the absolute moving speeds of all the measurement points, more specifically, the absolute moving speeds of all the measurement points including the measurement points corresponding to the three-dimensional objects, based on the absolute moving speed Vself of the host vehicle estimated by the estimation unit 112. Here, the calculated absolute moving speed is a negative value when approaching the host vehicle, and is a positive value when leaving the host vehicle.

[0042] 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 whose absolute values ​​of absolute moving speeds calculated by the calculation unit 113 are greater than or equal to a predetermined speed Th_V and stationary point cloud data corresponding to measurement points whose absolute moving speeds are less than the predetermined speed Th_V.

[0043] Figure 2A and Figure 2B This is a diagram showing an example of three-dimensional objects included in the three-dimensional space around the host vehicle. Figure 2A 2 shows a moving object (a bicycle CY and a person RD riding the bicycle CY) traveling in the traveling direction (X direction) of the vehicle in front of the vehicle. Figure 2B It shows the situation when viewed from above (Z direction) Figure 2A A top view of a moving object. Figure 2BAs shown, even without information about the height direction (Z direction) of the three-dimensional object, the maximum size (Xmax and Ymax) of the three-dimensional object in the XY direction can be identified. Therefore, the conversion unit 115 projects the above-mentioned measurement points onto a plane in a manner that removes the information about the height direction from the position information of each measurement point corresponding to the moving point cloud data, and converts the position information of each measurement point from three dimensions to two dimensions. More specifically, when the position coordinates of each measurement point are displayed in the XYZ coordinate system, the conversion unit 115 projects each measurement point corresponding to the moving point cloud data onto the XY plane, and converts the moving point cloud data into two-dimensional data represented by the XY coordinate system.

[0044] The generator 116 adds the absolute moving speed calculated by the calculator 113 to the moving point cloud data converted into two-dimensional data, and generates three-dimensional data (hereinafter referred to as XYV data or speed-added data). More specifically, the generator 116 adds the absolute moving speed corresponding to each measuring point to the position information of each measuring point included in the moving point cloud data converted into two-dimensional data by the converter 115.

[0045] The detection unit 117 detects moving objects around the vehicle based on the XYV data generated by the generation unit 116. In more detail, the detection unit 117 performs clustering processing on the XYV data, and detects a bounding box (Bounding Box) that is the circumscribed area of ​​the moving object from the XY plane. It should be noted that the clustering processing can use any method such as DBSCAN (Density-based spatial clustering of applications with noise: density-based clustering algorithm), K-means method (k-means clustering algorithm), etc. The detection unit 117 also detects the position and size of the moving object in the three-dimensional space (XYZ space) based on the position and size of the detected circumscribed area. The detection unit 117 outputs information (image information, etc.) showing the detection result of the moving object to a display device, etc., not shown in the figure.

[0046] Here, the detection accuracy of the moving object by the detection unit 117 will be described. Figure 3A This is a diagram showing a plurality of pedestrians traveling. Figure 3A , pedestrians HM32 and HM34 move (walk) in the same direction (X-axis direction), while pedestrians HM31 and HM33 move (walk) in the opposite direction. It should be noted that the absolute values ​​of the absolute moving speeds V1, V2, V3, and V4 of pedestrians HM31, HM32, HM33, and HM34 are greater than the specified speed Th_V.

[0047] Figure 3B It is a diagram showing an example of three-dimensional data (XYV data) generated by the generating unit 116 . Figure 3BIt is shown in Figure 3A XYV data is obtained by projecting the measurement point group (cluster) PC1 to PC4 corresponding to the pedestrians HM31 to HM34 on the XY plane and adding the absolute moving speeds V1 to V4 of the pedestrians HM31 to HM34. Figure 3B In FIG. 1 , the measurement point groups of each pedestrian are depicted in different colors according to the absolute moving speed of each pedestrian. It should be noted that, in order to simplify the description, it is assumed that the absolute moving speeds V1, V2, and V4 of pedestrians HM31, HM32, and HM34 are equal to each other. Figure 3B In FIG. 1 , the measurement point groups PC1 , PC2 , and PC4 corresponding to the pedestrians HM31 , HM32 , and HM34 are drawn with the same color (white), and the measurement point group PC3 corresponding to the pedestrian HM33 is drawn with a different color (black).

[0048] in addition, Figure 3B , which corresponds to the measurement point groups PC1 to PC4 detected by clustering the XYV data, are shown in FIG. Figure 3B In the example shown, the measurement point groups PC2 and PC3 are close to each other, so it is possible that the measurement point groups PC2 and PC3 are identified as one measurement point group and included in one bounding box in the clustering process. That is, pedestrian HM32 and pedestrian HM33 may be detected as one (as one pedestrian). However, by the clustering process as described above, speed information is taken into account in the classification of the measurement points, so pedestrians HM32 and HM33 moving at different absolute moving speeds are detected as separate moving objects even if they are close to each other. As a result, Figure 3B As shown, bounding boxes BB2 and BB3 are respectively allocated to measurement point groups PC2 and PC3 corresponding to pedestrians HM32 and HM33.

[0049] Figure 4A and Figure 4B 1 is a diagram for explaining the detection result of the detection unit 117 for a moving object. Here, the detection data of the laser radar installed in the vehicle is not used as an example, but the detection data of the laser radar installed in the vehicle is used as an example. Figure 4A The detection results of the moving object by the detection unit 117 are explained by taking the detection data of the laser radar in the central hall of the commercial facility shown as an example. Figure 4A FIG. 4 shows the central hall AS in a commercial setting as viewed from the perspective of a laser radar. Figure 4AAs shown in FIG. 1 , when there are multiple pedestrians in the central hall AS, the pedestrians are close to each other, so the multiple pedestrians who are close to each other may be detected as one (as one pedestrian). However, the detection unit 117 performs the clustering process taking into account the speed information as described above, thereby enabling the bounding box corresponding to each moving object to be detected with high accuracy even when the detection data of the laser radar includes moving objects that are close to each other and move.

[0050] Figure 4B In the figure, relative to Figure 4A An example of the detection result of a moving object by the detection unit 117 in the three-dimensional space. Figure 4B The thinly shaded areas in the image represent stationary objects ( Figure 4A The thickly shaded area indicates the measurement point group corresponding to the moving object (in the central hall AS). Figure 4A The square frame represents the boundary frame corresponding to each moving object (pedestrian) detected by the clustering processing of the detection unit 117. It should be noted that Figure 4B The example shows an image in which a bounding box corresponding to each moving object is superimposed on the detection data (point cloud data) of the laser radar, but the detection unit 117 can also output an image in which a bounding box corresponding to each moving object is superimposed on the image captured by the camera 4 instead of the point cloud data as the detection result of the moving object. Figure 4A In the central hall AS, the pedestrian HM42 moving toward the front side and the pedestrian HM41 moving toward the back side immediately next to the pedestrian HM42 are approaching each other, but their moving speeds are different, so they are detected as different moving objects by the detection unit 117. Figure 4B The detection result of BB41 and BB42 shows the boundary boxes BB41 and BB42 corresponding to the pedestrians HM41 and HM42, respectively. There are also a plurality of pedestrians approaching the right side of the central hall AS, but those pedestrians are also detected as different moving objects by the detection unit 117. Figure 4B In this way, by using the clustering processing as described above by the detection unit 117, even when the detection data of the laser radar 5 includes a plurality of moving objects close to each other, each moving object can be detected as a different moving object with high accuracy.

[0051] However, depending on the type of the moving object, the absolute moving speed of the main body of the moving object and the absolute moving speed of the local part attached to the main body may be different from each other. For example, a pedestrian moves while moving his limbs, so the trunk and limbs of the moving pedestrian may be different from the absolute moving speed calculated by the calculation unit 113. In such a case, when the clustering process taking into account the speed information as described above is performed, the trunk and limbs of the pedestrian may be detected as different moving objects. Therefore, in order to deal with such a problem, the detection unit 117 performs the combination process of the moving objects shown below.

[0052] Figure 5A and Figure 5B It is a diagram for explaining the combining process of moving objects. Figure 5A Schematic diagram of the pedestrian HM51 moving in the X direction and the pedestrian HM52 moving in the opposite direction to the pedestrian HM51 as viewed from the Y direction is shown in FIG. Figure 5A , the bounding boxes set by the clustering processing of the detection unit 117 are schematically shown as square boxes BD1, BD2, PT11 to PT14, PT21 to PT24. Bounding boxes BD1 and BD2 correspond to the torsos of pedestrians HM51 and HM52. Bounding boxes PT11, PT12, PT21, and PT22 correspond to the arms of pedestrians HM51 and HM52. Bounding boxes PT13, PT14, PT23, and PT24 correspond to the legs of pedestrians HM51 and HM52.

[0053] The detection unit 117 determines whether there is a bounding box (hereinafter referred to as a combined object bounding box or simply a combined object box) whose distance from other bounding boxes whose sizes are greater than or equal to a specified threshold is less than a specified length and whose size is less than a specified threshold in the bounding boxes set by the clustering process. If it is determined that a combined object box exists, the detection unit 117 combines the combined object box with the other bounding boxes (hereinafter referred to as a combined object bounding box or simply a combined object box). Figure 5A In the example, the bounding boxes PT11 to PT14 are selected as the combined object boxes, and the bounding box BD1 is selected as the combined object box corresponding to the combined object boxes PT11 to PT14. As a result, the bounding boxes PT11 to PT14 are combined with the bounding box BD1. Similarly, the bounding boxes PT21 to PT24 are combined with the bounding box BD2. In this way, it is possible to suppress the detection of the torso and limbs of the pedestrians HM51 and HM52 as separate moving objects. It should be noted that since the bounding boxes BD1 and BD2 whose sizes are greater than the specified threshold are both combined object boxes, they cannot be combined even if the distance between them is less than the specified length. In addition, since PT11 to PT14 and PT21 to PT24 whose sizes are less than the specified threshold are both combined object boxes, they are not combined even if the distance between them is less than the fixed length.

[0054] It should be noted that in Figure 5A In the description, the trunk and limbs of a pedestrian are used as an example, but even in vehicles traveling around the vehicle, the absolute moving speed calculated by the calculation unit 113 is sometimes different between the main body of the vehicle and parts such as wheels attached to the main body. Figure 5B A schematic diagram of a vehicle CA moving in the X direction as viewed from the Y direction is shown in FIG. Figure 5B In the figure, the bounding boxes set by the clustering processing of the detection unit 117 are schematically shown as square boxes 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 wheels and the rear wheels of the vehicle CA. The main body of the vehicle CA and the parts such as the wheels attached to the main body have the same moving speed in the direction of travel of the vehicle, but their relative positions with respect to the laser radar 5 are different, so different relative moving speeds are sometimes calculated separately in the calculation unit 113. In such a case, it is also possible to combine the bounding boxes PT31 and PT32 with the bounding box BD3 by performing the above-mentioned combination processing. As a result, it is possible to suppress the detection of the main body and the parts of the vehicle CA as separate moving objects.

[0055] Figure 6 It is shown Figure 1 The flowchart is a flowchart of an example of a process executed by the computing unit 11 of the controller 10 according to a predetermined program. The process shown in the flowchart is repeated at a predetermined cycle while the object detection device 50 is activated. More specifically, it is repeated at each cycle of the frame rate of the laser radar 5.

[0056] First, in step S1, the external conditions around the vehicle are detected. Specifically, an irradiation instruction is sent to the laser radar 5 to obtain point cloud data (detection data), which includes the position information and speed information of the measurement point obtained from the reflected wave of the electromagnetic wave irradiated by the laser radar 5 according to the irradiation instruction. In step S2, the point cloud data obtained in step S1 is classified into moving point cloud data and stationary point cloud data. In more detail, the point cloud data is classified into moving point cloud data corresponding to the measurement point whose absolute value of the absolute moving speed is greater than the specified speed Th_V and stationary point cloud data corresponding to the measurement points other than the above.

[0057] Next, the processing of steps S3 to S6 is executed on the moving point cloud data. It should be noted that the controller 10 also executes a predetermined processing on the stationary point cloud data, but the description thereof is omitted.

[0058] In step S3, each measurement point corresponding to the moving point cloud data is projected onto the XY plane, and the moving point cloud data is converted into two-dimensional data represented by an XY coordinate system. In step S4, clustering processing is performed. Specifically, first, the absolute moving speed corresponding to each measurement point calculated in step S2 is added to the position information of each measurement point included in the moving point cloud data converted into two-dimensional data in step S3, and three-dimensional data (XYV data) is generated. Next, clustering processing is performed on the generated XYV data.

[0059] In step S5, it is determined whether there is a combined object frame in the bounding box detected by the clustering process of step S4. When step S5 is negative (S5: No), the process proceeds to step S7. On the other hand, when step S5 is positive (S5: Yes), in step S6, after performing a combining process of combining the combined object frame with the combined object frame corresponding to the combined object frame, the process proceeds to step S7. In step S7, the position and size of the moving objects around the vehicle in the three-dimensional space (XYZ space) are detected based on the position and size of the bounding box detected by the clustering process of step S4. It should be noted that when the bounding boxes are combined by the combining process of step S6, the position and size of the moving objects in the three-dimensional space are detected based on the position and size of the combined bounding boxes.

[0060] The above-described embodiment has the following effects.

[0061] (1) The object detection device 50 comprises: a laser radar 5 mounted on the vehicle, irradiating electromagnetic waves into the three-dimensional space around the vehicle, and detecting the external conditions around the vehicle based on the reflected waves; a data acquisition unit 111, which acquires point cloud data, wherein the point cloud data includes three-dimensional position information of a measurement point on the surface of the object from which the reflected waves are obtained and first speed information indicating the relative moving speed of the measurement point; an estimation unit 112, which, as a speed acquisition unit, acquires second speed information indicating the absolute moving speed of the vehicle; a calculation unit 113, which calculates the absolute moving 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 a classification unit 114, which classifies the point cloud data into points corresponding to the points obtained by the estimation unit 112. The moving point cloud data corresponding to the measuring points whose absolute values ​​of the absolute moving speed calculated by the calculation unit 113 are above the prescribed speed Th_V and the stationary point cloud data other than the moving point cloud data; the conversion unit 115, which converts the three-dimensional position information of each measuring point corresponding to the moving point cloud data into two-dimensional position information; the generation unit 116, which generates speed-added data, which is obtained by adding the absolute moving speed corresponding to each measuring point calculated by the calculation unit 113 to the two-dimensional position information of each measuring point obtained by the conversion of the conversion unit 115; and the detection unit 117, which detects the objects contained in the three-dimensional space around the vehicle according to the speed-added data generated by the generation unit 116. The conversion unit 115 projects each measuring point corresponding to the moving point cloud data onto the XY plane, and converts the three-dimensional position information of each measuring point into two-dimensional position information. The detection unit 117 performs a prescribed clustering process on the speed-added data, detects the circumscribed area of ​​the object from the XY plane, and detects the position and size of the above-mentioned object in the three-dimensional space according to the position and size of the circumscribed area. Thus, moving objects that are close to each other, such as pedestrians passing by each other, can be detected as different moving objects. As a result, the detection accuracy of moving objects can be improved. In addition, since clustering is performed on the measurement point group corresponding to the moving object, the calculation load required for the detection of the moving object can be reduced compared to the case where clustering is also performed on the measurement point group corresponding to the stationary object.

[0062] (2) When the detection unit 117 detects a plurality of circumscribed areas corresponding to a plurality of objects from the XY plane, when the distance between a first circumscribed area whose size is less than a predetermined threshold and a second circumscribed area whose size is greater than the predetermined threshold is less than a predetermined length, the detection unit 117 combines the first circumscribed area with the second circumscribed area. The detection unit 117 detects the position and size of a single object in the three-dimensional space around the vehicle based on the position and size of the combined area obtained by combining the first circumscribed area with the second circumscribed area. More specifically, the detection unit 117 combines the first circumscribed area with the second circumscribed area based on the position and size of the combined area obtained by combining the first circumscribed area with the second circumscribed area. Figure 5B The first external area (eg, a part of the vehicle body attached to the vehicle CA) corresponds to Figure 5BThe bounding box PT31, PT32) and the second object (eg, Figure 5B The second circumscribed area (eg Figure 5B The position and size of the combined area are obtained by combining the bounding box BD3 of the first object and detecting a third object (eg, Figure 5B Thus, a moving object consisting of a main body and parts, such as a human body's trunk and limbs, a vehicle's main body and wheels, etc., can be appropriately identified as one moving object.

[0063] (3) The estimation unit 112 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 obtained by the data acquisition unit 111, and obtains the estimation result as speed information. The representative measurement point is selected from the measurement points remaining after removing the measurement point corresponding to the three-dimensional object from the plurality of measurement points. Thus, the absolute moving speed of the vehicle is estimated using the measurement point corresponding to the road surface as a reference. As a result, the absolute moving speed of the vehicle can be estimated with high accuracy. In addition, since the absolute moving speed of the vehicle (moving body) is estimated and obtained without relying on the sensor value of a vehicle speed sensor, etc., the present invention can also be applied to self-propelled robots, etc. that do not have a vehicle speed sensor, etc.

[0064] The above-mentioned embodiment can be modified into various forms. The following is an explanation of the modified example. In the above-mentioned embodiment, the laser radar 5 as a detector is mounted on a vehicle, irradiates electromagnetic waves into the three-dimensional space around the vehicle, and detects the external conditions around the vehicle based on the reflected waves. However, the detector can also be a device other than a laser radar such as a radar. In addition, the mobile body equipped with the detector can also be a mobile body other than a vehicle such as a self-propelled robot.

[0065] In addition, in the above-mentioned 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 performs clustering processing on the XYV data to detect moving objects around the vehicle. However, in the case where the accuracy of the cluster size in the three-dimensional space (XYZ space) is required, the above-mentioned clustering processing can also be performed on the XYZ space. Specifically, the generation unit can also 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). In addition, the detection unit can also perform clustering processing on the XYZV data.

[0066] In addition, in the above-mentioned embodiment, the estimation unit 112 as the speed acquisition unit selects the measurement point Pi as the representative measurement point from the measurement points remaining after removing the measurement point corresponding to the three-dimensional object from the plurality of measurement points, and 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 the second speed information. However, the speed acquisition unit may also acquire the measurement result of the absolute moving speed of the host vehicle obtained by the measuring device included in the internal sensor group 3 as the second speed information. In this case, the object detection device 50 includes at least the vehicle speed sensor in the internal sensor group 3 as the measuring device. In addition, the speed acquisition unit may also calculate and acquire the absolute moving speed of the host vehicle based on the current position of the host vehicle measured by the positioning unit 2. In this case, the object detection device 50 includes the positioning unit 2.

[0067] In addition, in the above-mentioned embodiment, the driving control unit 118 controls the driving of the vehicle to avoid collision or contact with the object detected by the detection unit 117. However, the driving control unit 118 may also serve 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. Furthermore, when the possibility of collision or contact with the moving object is greater than a predetermined level, the vehicle control device 100 may also provide a display or speaker (not shown) provided to the vehicle control device 100 to notify the occupants of the vehicle of the collision or contact with the moving object detected by the detection unit 117 (image information, sound information).

[0068] In addition, 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 system).

[0069] The above description is merely an example, and the above embodiment and modified examples do not limit the present invention unless the features of the present invention are impaired. One or more of the above embodiment and modified examples can be arbitrarily combined, and the modified examples can be combined with each other.

[0070] By adopting the present invention, it is possible to reduce the calculation load and detect moving objects with high precision.

[0071] The present invention has been described above in conjunction with preferred embodiments, but it should be understood by those skilled in the art that various modifications and changes can be made without departing from the scope of the claims.

Claims

1. An object detection device, characterized in that: have: A detector (5) mounted on a moving body, irradiating electromagnetic waves into a three-dimensional space around the moving body, and detecting external conditions around the moving body based on reflected waves; a data acquisition unit (111) for acquiring point cloud data from the detector (5), the point cloud data including three-dimensional position information of a measurement point on the surface of the object from which the reflected wave is obtained and first speed information indicating a relative moving speed of the measurement point; a speed acquisition unit that acquires second speed information indicating an absolute moving speed of the moving object; a calculation unit (113) for calculating the 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 (114) for classifying the point cloud data into moving point cloud data corresponding to the measurement point whose absolute value of the absolute moving speed calculated by the calculation unit (113) is greater than a predetermined speed and stationary point cloud data other than the moving point cloud data; a conversion unit (115) for converting the three-dimensional position information of each measurement point corresponding to the mobile point cloud data into two-dimensional position information; a generating unit (116) for generating speed added data by adding the absolute moving speed corresponding to each measuring point calculated by the calculating unit (113) to the two-dimensional position information of each measuring point obtained by the conversion of the converting unit (115); and A detection unit (117) detects the object contained in the three-dimensional space around the moving body based on the speed additional data generated by the generation unit (116).

2. The object detection device according to claim 1, characterized in that: The conversion unit (115) projects each measurement point corresponding to the mobile point cloud data onto a plane, converts the three-dimensional position information of each measurement point into the two-dimensional position information, The detection unit (117) performs a predetermined clustering process on the velocity-added data, detects a circumscribed area of ​​the object from the plane, and detects the position and size of the object in the three-dimensional space based on the position and size of the circumscribed area.

3. The object detection device according to claim 2, characterized in that: When the detection unit (117) detects multiple circumscribed areas corresponding to multiple objects from the plane, when the distance between a first circumscribed area whose size is less than a specified threshold and a second circumscribed area whose size is greater than the specified threshold is less than a specified length, the first circumscribed area is combined with the second circumscribed area.

4. The object detection device according to claim 3, characterized in that: The detection unit (117) detects the position and size of the single object in the three-dimensional space based on the position and size of a combined area obtained by combining the first circumscribed area with the second circumscribed area.

5. The object detection device according to claim 3, characterized in that: The detection unit (117) detects the position and size of a third object including the first object and the second object in the three-dimensional space based on the position and size of a combined area obtained by combining the first circumscribed area corresponding to the first object and the second circumscribed area corresponding to the second object.

6. The object detection device according to claim 1, characterized in that: The speed acquisition unit estimates the absolute moving speed of the moving body based on the position information of the representative measurement point extracted from the point cloud data acquired by the data acquisition unit and the first speed information, and acquires the estimation result as the second speed information. The representative measurement point is selected from the measurement points remaining after removing the measurement point corresponding to the three-dimensional object from the plurality of measurement points.

7. The object detection device according to claim 1, characterized in that: further comprising a measuring device for measuring the absolute moving speed of the moving body, The speed acquisition unit acquires a measurement result of the measurement device as the second speed information.

8. The object detection device according to any one of claims 1 to 7, characterized in that: The detector (5) is a laser radar.

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