Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11–13, 2017, Proceedings

Research Article

An Effective Method for Self-driving Car Navigation based on Lidar

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  • @INPROCEEDINGS{10.1007/978-3-030-00916-8_65,
        author={Meng Liu and Yu Liu and Jianwei Niu and Yu Du and Yanchen Wan},
        title={An Effective Method for Self-driving Car Navigation based on Lidar},
        proceedings={Collaborative Computing: Networking, Applications and Worksharing. 13th International Conference, CollaborateCom 2017, Edinburgh, UK, December 11--13, 2017, Proceedings},
        proceedings_a={COLLABORATECOM},
        year={2018},
        month={10},
        keywords={Lidar Self-driving car LNA Navigation Linear regression},
        doi={10.1007/978-3-030-00916-8_65}
    }
    
  • Meng Liu
    Yu Liu
    Jianwei Niu
    Yu Du
    Yanchen Wan
    Year: 2018
    An Effective Method for Self-driving Car Navigation based on Lidar
    COLLABORATECOM
    Springer
    DOI: 10.1007/978-3-030-00916-8_65
Meng Liu1,*, Yu Liu1, Jianwei Niu1, Yu Du2, Yanchen Wan1
  • 1: Beihang University
  • 2: Beijing Union University
*Contact email: liumeng_scse@buaa.edu.cn

Abstract

Existing navigation methods are generally based on GPS or cameras and these methods have limitations in terms of signal strength and brightness. To overcome drawbacks of navigation methods above, we propose a Lidar-based Navigation Approach (LNA) to predict movement trajectory of self-driving vehicles through road edges information, and this approach is a fitting and real-time regression method. By combining regression model with vehicle coordinate system, navigation trajectory is accurately generated. Experiments on common road scenarios demonstrate that our approach is effective to improve navigation techniques.