Reliability Analysis of Autonomous Driving Lane Detection on Mountainous Freeways

Abstract:

Autonomous vehicles need to perceive constantly changing road alignments， section characteristics， and environmental factors when driving on mountainous freeways， and analyze safe driving boundaries. This paper focuses on testing the lane detection performance of autonomous vehicles based on LiDAR in mountainous freeway environments and analyzes the influencing factors of lane perception failure. Using the Road and Traffic Holographic Data Acquisition System of Tongji University， the on-road vehicle test was conducted on the Guidu freeway in Guizhou Province， with multiple round trips totaling 3 200 kilometers. The impact of 11 feature variables on perceived failure was explained based on the CatBoost model and Shapley Additive exPlanations （SHAP）. The research results indicate that weather factors have the most significant impact on the perception failure of lane markings， followed by the influence of road alignments. Specifically， a higher probability of perceptual failure will be detected when there are the following characteristics： larger maximum curvature， larger grade change rate， exit， and entrance sections， longitudinal deceleration markings， lateral deceleration markings， higher driving speed， etc. The results of this paper can not only provide measures more suitable for the safe operation of autonomous driving on mountainous highways， adapting better to autonomous vehicles， but can also serve as a reference for establishing the operational design domain for autonomous driving.