Layered SOTIF Analysis and 3σ-Criterion-Based Adaptive EKF for Lidar-Based Multi-Sensor Fusion Localization System on Foggy Days

The detection range and accuracy of light detection and ranging (LiDAR) systems are sensitive to variations in fog concentration, leading to the safety of the intended functionality-related (SOTIF-related) problems in the LiDAR-based fusion localization system (LMSFLS). However, due to the uncontrollable weather, it is almost impossible to quantitatively analyze the effects of fog on LMSFLS in a realistic environment. Therefore, in this study, we conduct a layered quantitative SOTIF analysis of the LMSFLS on foggy days using fog simulation. Based on the analysis results, we identify the component-level, system-level, and vehicle-level functional insufficiencies of the LMSFLS, the corresponding quantitative triggering conditions, and the potential SOTIF-related risks. To address the SOTIF-related risks, we propose a functional modification strategy that incorporates visibility recognition and a 3σ-criterion-based variance mismatch degree grading adaptive extended Kalman filter. The visibility of a scenario is recognized to judge whether the measurement information of the LiDAR odometry is disturbed by fog. Moreover, the proposed filter is adopted to fuse the abnormal measurement information of the LiDAR odometry with IMU and GNSS. Simulation results demonstrate that the proposed strategy can inhibit the divergence of the LMSFLS, improve the SOTIF of self-driving cars on foggy days, and accurately recognize the visibility of the scenarios.