Improving Lateral Autonomous Driving in Snow-Wet Environments Based on Road-Mark Reconstruction Using Principal Component Analysis

Lateral localization is a very sensible factor for evaluating the performance of autonomous driving. This paper addresses the general reasons of the lateral drifting by analyzing the performance of an accurate localization system at level of 10∼20 cm. The performance drops into 0.4∼3 m in winter because of the snow and wet road surface representations in the LIDAR data. Accordingly, we prove that enhancing the quality of online LIDAR data before calculating the matching score with the map is a key-solution to improve the lateral accuracy. This can be achieved by filtering-out snow, regenerating the road edges at correct positions and sharping-up the lane lines. To achieve these objectives, we propose a machine learning based road-mark reconstruction framework. The map images are converted into edge profiles to represent the road-marks in a series of peaks. Principal Component Analysis (PCA) is used to model the relationships between these peaks and extract the dominant distribution patterns. Based on the leading eigenvectors (eigenroads), the LIDAR edge profiles are safely and efficiently reconstructed during the autonomous driving. The reliability of the proposed framework has been tested using map data generated in 2015 and LIDAR data collected in 2016 and 2017 in snowy and rainy weather conditions. The experimental results have verified the robustness and truthfulness of the proposed framework to significantly improve the lateral accuracy in the LIDAR based localization systems. The localization accuracy has been enhanced to be 96.4% for 15 cm average error in very critical situations of changing the road patterns and the environmental conditions. The reliability of the proposed framework has been tested using map data generated in 2015 and LIDAR data collected in 2016 and 2017 in snowy and rainy weather conditions. The experimental results have verified the robustness and truthfulness of the proposed framework to significantly improve the lateral accuracy in the LIDAR based localization systems. The localization accuracy has been enhanced to be 96.4% for 15 cm average error in very critical situations of changing the road patterns and the environmental conditions.