Readiness Evaluation of Freeways for Lane-Detection Performance of Lidar-Based Automated Vehicles: A Field Test Analysis

Advanced driver-assistance systems with lane-detection functions are increasingly deployed in automated vehicles (AVs), but current road infrastructures may not accommodate to AVs’ safe operations. Freeways, where reliable perception is crucial for safe navigation, present higher safety risks. Lidar’s ability to provide precise depth information and function effectively in challenging scenarios becomes essential. However, fewer studies have explored roadway readiness for lidar-based performance. This study identified the effects of freeway design and condition on lane-detection performance of lidar-based AVs through a field test on two typical freeways in Shanghai, China, and the freeway readiness was evaluated. The Tongji University Road and Traffic Data Acquisition System was used for data collection. The lane-detection failure was treated as the label. Ten variables of five feature types were considered as the parameters, including road geometry, road segment, road marking, vehicle operation, and environment. The XGBoost ensemble machine learning algorithm and the SHapley Additive exPlanations (SHAP) were used for modeling and interpretation, respectively. Results show: 1) all features except for light condition were strongly correlated to lidar-based lane-detection failures; 2) higher failure probability was observed under circumstances like the presence of larger change rate of vertical curves, right-most lane with entrance or exit, taper extensions, special markings like words, worn-out markings, higher speed, and closer leading large-vehicle distance; and 3) several interaction effects were discovered. Results provide three contributions to AV safety: 1) make freeway design better accommodate to AVs; 2) provide ODD management references; and 3) offer technical improvement focuses to manufacturers.