Obstacle Detection and Tracking for Driverless Cars Based on Lidar

WANG Tao; ZENG Wenhao; YU Qi

doi:10.3969/j.issn.0258-2724.20200240

Volume 56 Issue 6

Dec. 2021

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Journal of Southwest Jiaotong University > 2021 > 56(6): 1346-1354.

WANG Tao, ZENG Wenhao, YU Qi. Obstacle Detection and Tracking for Driverless Cars Based on Lidar[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1346-1354. doi: 10.3969/j.issn.0258-2724.20200240

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WANG Tao, ZENG Wenhao, YU Qi. Obstacle Detection and Tracking for Driverless Cars Based on Lidar[J]. Journal of Southwest Jiaotong University, 2021, 56(6): 1346-1354. doi: 10.3969/j.issn.0258-2724.20200240

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Obstacle Detection and Tracking for Driverless Cars Based on Lidar

doi: 10.3969/j.issn.0258-2724.20200240

School of Electrical Engineering，Southwest Jiaotong University，Chengdu 610031，China

Received Date: 26 Apr 2020
Rev Recd Date: 15 Nov 2020

Available Online: 05 Jan 2021

Publish Date: 05 Jan 2021

Abstract

Abstract

To improve clustering adaptability, real-time performance, and tracking accuracy in the process of dynamic obstacle detection and tracking with lidar, an adaptive density clustering algorithm and a multi-feature data association method are developed for detection and tracking respectively. Firstly, the point cloud collected by the lidar is pre-processed such as curb detection, area-of-interest extraction and ground segmentation to remove irrelevant point clouds. Then the non-ground point clouds are clustered according to the adaptive clustering algorithm to complete the obstacle point cloud detection. Finally, a weighted multi-feature data association algorithm combined with Kalman filter is used to track dynamic obstacles. The experiments show that the entire detection process with the proposed methods can accurately and stably detect and track the 10 Hz lidar data, and shorten the clustering time by 32%.
- lidar,
- cluster,
- data association,
- Kalman filter,
- target tracking

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References(14)

References

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