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

Abstract

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.

Degradation state detection and local map optimization for enhancing the SOTIF of map-matching-based fusion localization system

“…Although few existing studies related to MMBFLS explicitly address SOTIF, many of these investigations are closely associated with SOTIF scenarios. In adverse weather scenarios, Cao et al [21] proposed a positioning strategy for a LiDAR-based fusion system under foggy conditions, utilizing visibility recognition and the 3σ criterion to mitigate the interference of fog on the fusion localization system. Zhang et al [22] introduced a map-matching localization method using 3D LiDAR ground reflection and vertical features.…”