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Volume 8 Issue 6
Jun.  2021

IEEE/CAA Journal of Automatica Sinica

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Article Navigation >  IEEE/CAA Journal of Automatica Sinica  > 2021  >  8(6): 1210-1220
G. J. Wang, J. Wu, R. He, and B. Tian, "Speed and Accuracy Tradeoff for LiDAR Data Based Road Boundary Detection," IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1210-1220, Jun. 2021. doi: 10.1109/JAS.2020.1003414
Citation: G. J. Wang, J. Wu, R. He, and B. Tian, "Speed and Accuracy Tradeoff for LiDAR Data Based Road Boundary Detection," IEEE/CAA J. Autom. Sinica, vol. 8, no. 6, pp. 1210-1220, Jun. 2021. doi: 10.1109/JAS.2020.1003414
shu

Speed and Accuracy Tradeoff for LiDAR Data Based Road Boundary Detection

doi: 10.1109/JAS.2020.1003414
  • 1.

    State Key Laboratory of Automotive Simulation and Control, Jilin University, Changchun 130022, China

  • 2.

    State Key Laboratory of Management and Control for Complex Systems, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China

  • 3.

    Qingdao Academy of Intelligent Industries, Shandong, China

Funds:  This work was supported by the Research on Construction and Simulation Technology of Hardware in Loop Testing Scenario for Self-Driving Electric Vehicle in China (2018YFB0105103J)
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    Abstract
  • Road boundary detection is essential for autonomous vehicle localization and decision-making, especially under GPS signal loss and lane discontinuities. For road boundary detection in structural environments, obstacle occlusions and large road curvature are two significant challenges. However, an effective and fast solution for these problems has remained elusive. To solve these problems, a speed and accuracy tradeoff method for LiDAR-based road boundary detection in structured environments is proposed. The proposed method consists of three main stages: 1) a multi-feature based method is applied to extract feature points; 2) a road-segmentation-line-based method is proposed for classifying left and right feature points; 3) an iterative Gaussian Process Regression (GPR) is employed for filtering out false points and extracting boundary points. To demonstrate the effectiveness of the proposed method, KITTI datasets is used for comprehensive experiments, and the performance of our approach is tested under different road conditions. Comprehensive experiments show the road-segmentation-line-based method can classify left, and right feature points on structured curved roads, and the proposed iterative Gaussian Process Regression can extract road boundary points on varied road shapes and traffic conditions. Meanwhile, the proposed road boundary detection method can achieve real-time performance with an average of 70.5 ms per frame.

     

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    Highlights

    • This paper presents a road-segmentation-line-based classification method for classifying feature points. The road segmentation line is determined by a beam band model and an improved peak-finding algorithm. The method can classify left and right feature points accurately on curved roads up to 70 meters away.
    • This paper proposes a distance filter and random sample consensus filter for candidate point extraction and seed point extraction. The candidate points and seed points would be used for the subsequent feature points filtering based on an iterative Gaussian process.
    • This paper proposes an iterative Gaussian Process Regression (GPR) based feature points filtering method. The GPR algorithm can be applied to various road shapes without assuming that road boundaries are parametric models. At this same time, the algorithm can effectively remove false points caused by obstacle occlusions. Because GPR is a nonparametric model, it significantly enhances the adaptability to various road shapes and the robustness for obstacle occlusions.

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