Speed and Accuracy Tradeoff for LiDAR Data Based Road Boundary Detection

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.