Method and Applications of Solid-State Lidar Modeling for X-in-the-Loop Testing of Autonomous Vehicles

With the continuous progress of autonomous vehicle (AV) technologies, the resulting accidents have generated public concern, underscoring the need for comprehensive, reliable, and authoritative testing to enhance safety. X-in-the-loop (XiL) testing has emerged as a promising paradigm to bridge the gap between simulation and real-world deployment. This study addresses the gap in supporting solid-state LiDAR sensors, thereby bolstering the authority and credibility of XiL testing. Based on the operating principles of actual LiDAR, the proposed high-fidelity model simulates unique mechanisms of solid-state LiDAR to produce point clouds that closely correspond to real-world data. Additionally, the model accounts for the impact of various weather conditions on laser propagation, enhancing the coverage and reliability of testing scenarios. Extensive experiments have been conducted to validate the proposed LiDAR model from multiple levels, including scanning pattern, point cloud acquisition, and XiL testing, with real world data as the benchmark. Results confirm that the model replicates real LiDAR behavior and supports AV functions (mapping, localization, perception), offering a novel sensor model extension for controlled and repeatable XiL testing.