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A LiDAR Based Control Solution to Achieve High Precision in Autonomous Parking

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PRICAI 2022: Trends in Artificial Intelligence (PRICAI 2022)

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Abstract

Autonomous driving has been quite promising in recent years. While autonomous driving vehicles certainly have a bright future, we have to admit that it is still challenging in complex interactive scenarios. On the other hand, while humans are good at interactive tasks, they are often less competent for tasks with strict precision demands. In this paper, we introduced a real-world, industrial scenario in which autonomous driving system provides a solution to a parking task that human drivers are not capable. This task required ego vehicle to keep a strict lateral distance (i.e. 3\(\sigma \) \(\le \) 5 cm) to a reference. To address this challenge, we redesigned the control module from Baidu Apollo open-source autonomous driving system. A specific (3\(\sigma \) \(\le \) 2 cm) Error Feedback System was first built to enhance the original localization module. Then we investigated the control module thoroughly and added an extra real-time calibration algorithm to guarantee precision. After all those efforts, the results are encouraging, showing that a lateral precision with 3\(\sigma \) \(\le \) 5 cm has been achieved, better than any specially trained and highly experienced human test drivers and original Apollo solution.

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Acknowledgements

The vast majority of this paper was performed in Baidu/Baidu USA. The authors acknowledge the resources, i.e. the Apollo Open Source team, the Quality Assurance team, the Safety team, and the Operation team, etc., provided by Intelligent Driving Group, Baidu/Baidu USA.

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Authors and Affiliations

  1. Intelligence Research Department, Hozon Auto Ltd., Shanghai, China

    Xin Xu

  2. ECARX Ltd., Shanghai, China

    Fan Zhu

  3. Intelligent Driving Group, Baidu Ltd., Beijing, China

    Xin Xu & Yu Dong

  4. Intelligent Driving Group, Baidu USA Ltd., California, USA

    Fan Zhu

Authors
  1. Xin Xu
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  2. Yu Dong
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  3. Fan Zhu
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Corresponding author

Correspondence to Fan Zhu .

Editor information

Editors and Affiliations

  1. CSIRO Australian e-Health Research Centre, Brisbane, QLD, Australia

    Sankalp Khanna

  2. Shanghai Jiao Tong University, Shanghai, China

    Jian Cao

  3. University of Tasmania, Hobart, TAS, Australia

    Quan Bai

  4. University of Technology Sydney, Sydney, NSW, Australia

    Guandong Xu

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Xu, X., Dong, Y., Zhu, F. (2022). A LiDAR Based Control Solution to Achieve High Precision in Autonomous Parking. In: Khanna, S., Cao, J., Bai, Q., Xu, G. (eds) PRICAI 2022: Trends in Artificial Intelligence. PRICAI 2022. Lecture Notes in Computer Science, vol 13629. Springer, Cham. https://doi.org/10.1007/978-3-031-20862-1_10

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  • DOI: https://doi.org/10.1007/978-3-031-20862-1_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20861-4

  • Online ISBN: 978-3-031-20862-1

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