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Low-Cost LiDAR-Based Vehicle Detection for Self-driving Container Trucks at Seaport

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Collaborative Computing: Networking, Applications and Worksharing (CollaborateCom 2021)

Abstract

The self-driving technology has been developed rapidly in the past decades, due to new sensors, and car manufacturers have become more open. However, fully self-driving vehicles for the public still has a long way to go. Most studies try to focus on self-driving in special scenes, such as park sightseeing car, express logistics vehicle, sweeper, indoor service robot, and special vehicles in the mining area or seaport area. One of the critical issues is that the cost of a self-driving vehicle should strictly be controlled for commercial uses. This paper presents a low-cost LiDAR-based moving obstacle detection and tracking for self-driving container trucks in the low-speed seaport area. We build a CNN model for obstacle detection with the bird’s eye view (BEV) map generated from two low density LiDARs equipped at the head of a container truck. A boosting tracker is used to achieve real-time processing speed on the embedded module of Tx2. Simulation on the collected data shows that our Strided-Yolo model can achieve the highest mAP on the BEV projection map than other models.

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Acknowledgment

This work has been supported by China Postdoctoral Science Foundation (2020M681798), Qianjiang Excellent Post-Doctoral Program (2020Y4A001) and 2020 Zhejiang Postdoctoral Research Project (ZJ2020011). JITRI Suzhou Automotive Research Institute Project (CEC20190404). Chongqing Autonomous Unmanned System Development Foundation and Key Technology Strategic Research Project (2020-XZ-CQ-3). The authors would like to thank Plusgo for their cooperation during data collection.

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Authors and Affiliations

  1. Hangzhou Innovation Institute, Hangzhou, 31000, China

    Zhenchao Ouyang, Lu Ren & Yu Liu

  2. State Key Laboratory of Software Development Environment, BeiHang University, Beijing, 100191, China

    Changjie Zhang, Lu Ren & Yu Liu

  3. Zhongfa Aviation University, Hangzhou, 31000, Zhejiang, China

    Zhenchao Ouyang

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  1. Changjie Zhang
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  2. Zhenchao Ouyang
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  3. Lu Ren
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  4. Yu Liu
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Correspondence to Zhenchao Ouyang .

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Editors and Affiliations

  1. Shanghai University, Shanghai, China

    Honghao Gao

  2. Xi’an Jiaotong-Liverpool University, Suzhou, China

    Xinheng Wang

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Zhang, C., Ouyang, Z., Ren, L., Liu, Y. (2021). Low-Cost LiDAR-Based Vehicle Detection for Self-driving Container Trucks at Seaport. In: Gao, H., Wang, X. (eds) Collaborative Computing: Networking, Applications and Worksharing. CollaborateCom 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 407. Springer, Cham. https://doi.org/10.1007/978-3-030-92638-0_27

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  • DOI: https://doi.org/10.1007/978-3-030-92638-0_27

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  • Print ISBN: 978-3-030-92637-3

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