Lane changing identification and prediction method, system and device for extracting vehicle trajectory by using roadside laser radar data, and storage medium

A technology of laser radar and vehicle trajectory, applied in the field of traffic engineering

Pending Publication Date: 2021-09-03
山东高速建设管理集团有限公司 +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, since there are only a small number of connected vehicles, high-resolution microsensors can usually only collect sample data provided by these vehicles, while CV technology requires data from all road users

Method used

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  • Lane changing identification and prediction method, system and device for extracting vehicle trajectory by using roadside laser radar data, and storage medium
  • Lane changing identification and prediction method, system and device for extracting vehicle trajectory by using roadside laser radar data, and storage medium
  • Lane changing identification and prediction method, system and device for extracting vehicle trajectory by using roadside laser radar data, and storage medium

Examples

Experimental program
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Effect test

Embodiment 1

[0133] A lane change recognition and prediction method using roadside lidar data to extract vehicle trajectories, such as figure 1 shown, including the following steps:

[0134] (1) Background filtering and target clustering: use the background filtering technology to filter the background of the point cloud image obtained by the roadside lidar, and use the DBSCAN algorithm to cluster the point cloud in the point cloud image after the background filtering process , get the point cloud cluster;

[0135] (2) Object classification: distinguish pedestrians from vehicles, remove the point cloud of pedestrians, and only keep the point cloud of vehicles;

[0136] (3) Target tracking: generate lane boundary lines and match vehicles in transit to corresponding lanes;

[0137] (4) Lane-changing behavior prediction: Use the preset CDNLB and CCDNLB to predict the lane-changing behavior of the vehicle in real time.

Embodiment 2

[0139] According to a method of lane change recognition and prediction using roadside lidar data to extract vehicle trajectories described in Embodiment 1, the difference is that:

[0140] In step (1), background filtering refers to: filter out the background point cloud when no target in transit passes through from the point cloud image when the target in transit passes, and obtain the background point cloud of the target in transit; Figure 4 is the schematic diagram of point cloud data before background filtering; Figure 5 Is the schematic diagram of point cloud data after background filtering.

[0141] In step (1), target clustering includes the following steps:

[0142] A. Scan the entire point cloud image obtained after the background filtering process, find any core point, and expand the core point, that is, find all the density-connected data points starting from the core point;

[0143] B. Traverse all core points in the ε neighborhood of the core point (note: the ...

Embodiment 3

[0213] A lane change recognition and prediction system that uses roadside lidar data to extract vehicle trajectories, such as Figure 11 As shown, the lane change recognition and prediction method for extracting vehicle trajectories using roadside lidar data includes: a target point cloud collection module, a data processing module, and a prediction module; the target point cloud collection module is used to: collect in-transit targets, Background point cloud data; the data processing module is used to: filter out background point cloud data, filter out pedestrian point cloud data, cluster vehicle point clouds, track vehicle targets in different frames, divide lanes and match vehicle targets to Go in the corresponding lane; the prediction module is used to: predict whether the vehicle on the way will change lanes. The data processing module integrates a variety of point cloud processing algorithms: the DBSCAN algorithm is used for the clustering of target point clouds in trans...

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Abstract

The invention relates to a lane changing identification and prediction method, system and device for extracting a vehicle trajectory based on roadside laser radar data, and a storage medium. The method comprises the following steps: (1) carrying out background filtering on a point cloud picture scanned by a laser radar; (2) clustering the point cloud data of the in-transit target; (3) classifying vehicles and people by adopting an artificial neural network; (4) tracking the same target in continuous frames; (5) predicting the lane changing behavior of the vehicle according to the threshold value of the selected evaluation index. According to the method, the lane changing or turning behaviors of the vehicle can be predicted in real time with relatively high precision, and a related vehicle lane changing early warning system can be developed by utilizing the lane changing information, so the development of intelligent traffic engineering is increased.

Description

technical field [0001] The invention belongs to the technical field of traffic engineering, and in particular relates to a method, system, device and storage medium for lane change recognition and prediction based on roadside lidar extraction of vehicle trajectories. Background technique [0002] Intelligent networked vehicle technology (Connected-Vehicle, CV) has become an important part of the future intelligent transportation system. In an ideal CV network, all road users can communicate with each other through various wireless communication technologies. Connected vehicles have many advantages, including reducing congestion, improving traffic safety, and reducing fuel consumption. Lane change recognition and prediction is an important part of the functionality of CV networks, which is considered to be one of the most challenging driving maneuvers. Recognition and prediction of lane change is of great significance for collision avoidance. In order to provide effective ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G08G1/01G08G1/04G06N3/08G06K9/62G06Q10/04G06Q50/30
CPCG08G1/0125G08G1/0137G08G1/04G06N3/08G06Q10/04G06Q50/30G06F18/23
Inventor 吴建清张营超宋修广张涵侯福金李利平王凯马兆有杨梓梁李辉吕斌冉斌霍光
Owner 山东高速建设管理集团有限公司
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