Search

JP-7856733-B1 - Object detection device

JP7856733B1JP 7856733 B1JP7856733 B1JP 7856733B1JP-7856733-B1

Abstract

[Challenge] To detect distant moving objects and small moving objects at an early stage. [Solution] The object detection device 50 includes a classification unit 114 that classifies point cloud data acquired by the lidar 5 into moving point cloud data whose absolute value of absolute movement speed is equal to or greater than a predetermined speed, and stationary point cloud data other than moving point cloud data; a storage unit 12 that stores the moving point cloud data in frame units; an offset processing that offsets the position of each measurement point in the moving point cloud data included in past frames stored in the storage unit 12 based on the movement speed and direction of movement of each measurement point estimated based on the velocity information of each measurement point; an overlay unit 117 that overlays the offset-processed moving point cloud data of past frames onto the newly acquired point cloud data from the lidar 5; and a detection unit 118 that detects a moving object based on the moving point cloud data after the overlay processing. [Selection Diagram] Figure 1

Inventors

  • 佐伯 大地
  • 小西 俊介
  • チョン ザオ シャン
  • 益岡 修平

Assignees

  • 本田技研工業株式会社

Dates

Publication Date
20260511
Application Date
20241206

Claims (4)

  1. A detector that acquires point cloud data including three-dimensional position information and velocity information indicating relative movement speed at measurement points on the surface of an object included in the three-dimensional space by irradiating electromagnetic waves into a three-dimensional space and receiving the reflected waves, A calculation unit that calculates the absolute moving speed of each of the multiple measurement points corresponding to the point cloud data based on the speed information, When the point cloud data is acquired by the detector, a classification unit classifies the point cloud data into moving point cloud data 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 storage unit that stores the moving point cloud data classified by the classification unit for each point cloud frame containing the moving point cloud data at the same time, The system includes a processing unit that performs processing to detect a moving object moving within the three-dimensional space, The aforementioned processing unit, An offset process is performed to offset the position of each measurement point in the moving point cloud data included in the past point cloud frame stored in the storage unit, based on the moving speed and direction of each measurement point estimated based on the velocity information of each measurement point. When the moving point cloud data newly acquired by the detector is classified by the classification unit, an overlay process is performed on the moving point cloud data, overlaying the moving point cloud data of the previous point cloud frame that has been offset. Based on the moving point cloud data after the superposition process, the moving object is detected . The aforementioned processing unit further, When the moving point cloud data newly acquired by the detector is classified by the classification unit, a first clustering process is performed on the moving point cloud data, in which the minimum number of points in the cluster to be detected is set to a first predetermined number. In the offset process, the positions of each measurement point belonging to the cluster included in the moving point cloud data of the past point cloud frame are offset based on the moving speed and direction of the cluster estimated based on the velocity information of each measurement point. In the superposition process, the data of each measurement point belonging to the cluster from the moving point cloud data of the past point cloud frame that has been offset is superimposed on the moving point cloud data classified by the classification unit. A second clustering process is performed on the moving point cloud data after the superposition process, in which the minimum number of points is set to a second predetermined number that is greater than the first predetermined number. An object detection device characterized by detecting the position and size of the moving object in the three-dimensional space based on the position and size of the clusters detected by the second clustering process .
  2. In the object detection device according to claim 1, The aforementioned detector is an object detection device characterized by being mounted on a moving object.
  3. In the object detection device according to claim 2, The speed information mentioned above is first speed information, The system further includes a speed acquisition unit that acquires second speed information indicating the absolute speed of the moving body, The calculation unit calculates the absolute movement speed of each of the multiple measurement points corresponding to the point cloud data based on the first speed information and the second speed information. The object detection device is characterized in that the classification unit classifies the point cloud data into the moving point cloud data, specifically the measurement points whose absolute value of the absolute moving velocity calculated by the calculation unit is equal to or greater than the predetermined velocity.
  4. In the object detection device according to any one of claims 1 to 3, An object detection device characterized in that the detector is a lidar or radar.

Description

This invention relates to an object detection device for detecting objects around a vehicle. As an example of this type of device, one that detects moving objects using three-dimensional point cloud data acquired by a lidar is known (see, for example, Patent Document 1). Patent No. 7126633 A block diagram showing the main components of a vehicle control device including an object detection device according to an embodiment of the present invention.A diagram showing an example of a three-dimensional object contained in the three-dimensional space surrounding the vehicle.A plan view of the moving object in Figure 2A, seen from above.A diagram showing multiple pedestrians passing each other.A diagram showing an example of XYV data.A diagram showing an example of the three-dimensional space surrounding a vehicle.Figure 4A shows an example of XYV data corresponding to the three-dimensional space.Figure 4A shows an example of XYV data corresponding to the three-dimensional space.This figure shows an example of a measurement point cloud from past frames superimposed on the XYV data of the current frame without offset processing.A diagram showing an example of superimposed XYV data.A diagram illustrating the detection of moving objects.A diagram illustrating the calculation of the motion vector of a moving object.A flowchart showing an example of the processing performed by the CPU of the controller in Figure 1. The embodiments of the present invention will be described below with reference to the drawings. The object detection device according to the embodiments of the present invention can be applied to vehicles with an autonomous driving function, i.e., autonomous vehicles. The vehicle to which the object detection device according to this embodiment is applied may be referred to as "the vehicle itself" to distinguish it from other vehicles. The vehicle itself may be an engine-powered vehicle with an internal combustion engine as its driving source, an electric vehicle with a drive motor as its driving source, or a hybrid vehicle with both an engine and a drive motor as its driving sources. The vehicle itself can operate not only in an autonomous driving mode that does not require driver operation, but also in a manual driving mode with driver operation. When an autonomous vehicle is operating in autonomous driving mode (hereinafter referred to as autonomous driving or self-driving), it recognizes the external environment around it based on detection data from on-board detectors such as LiDAR (Light Detection and Ranging). Based on this recognition, the autonomous vehicle generates a target trajectory (driving path) for a predetermined time period from the current moment, and controls the driving actuators to ensure the vehicle travels along the target trajectory. Figure 1 is a block diagram showing the main components of a vehicle control device 100, including an object detection device. This vehicle control device 100 comprises a controller 10, a communication unit 1, a positioning unit 2, an internal sensor group 3, a camera 4, a lidar 5, and a driving actuator AC. The vehicle control device 100 also includes an object detection device 50, which constitutes 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. The communication unit 1 communicates with various servers (not shown) via a network including wireless communication networks such as the Internet and mobile phone networks, and acquires map information, driving history information, and traffic information from the servers periodically or at arbitrary times. The network includes not only public wireless communication networks but also closed communication networks established for each designated management area, such as wireless LANs, Wi-Fi®, and Bluetooth®. 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 positioning signals transmitted from positioning satellites. Positioning satellites are artificial satellites such as GPS satellites and quasi-zenith satellites. The positioning unit 2 uses the positioning information received by the positioning sensor to measure the current position (latitude, longitude, and altitude) of its own vehicle. The internal sensor group 3 is a collective term for multiple sensors (internal sensors) that detect the vehicle's driving state. For example, the internal sensor group 3 includes a vehicle speed sensor that detects the vehicle's speed, acceleration sensors that detect the vehicle's longitudinal and lateral acceleration (lateral acceleration), a rotation speed sensor that detects the rotation speed of the drive source, and a yaw rate sensor that detects the rotational angular velocity of the vehicle's center of gravity around its vertical axis. Sensors that detect driver operations in manual driving mo