Assessing Unknown Hazards for SOTIF Based on Twin Scenarios Empowered Autonomous Driving

Safety of the intended functionality (SOTIF) is paramount in autonomous driving, particularly at and beyond Level 3. An essential aspect of SOTIF research involves constructing and accurately assessing a broad range of hazardous scenarios. The current research landscape presents challenges in extensively testing autonomous vehicles and identifying all the unknown triggering events within their intended functionalities. Filling this gap, this article introduces a new methodology, noisy-HBN, driven by four-layer factors aggregated through the Internet of Vehicles (IoV), which integrates the Bayesian network with noisy nodes to assess the impact of unknown causes on SOTIF. To appraise the efficacy of this model, a new virtual data set, DADA-Plus, was developed, an extension of the open-source DADA data set, created using the twin scenario generation algorithm. The scenario-based verification underscores that the noisy-HBN model is adept at estimating the influence of unknown factors. Furthermore, the sensitivity analysis reveals that the known factors identified significantly correlate with the occurrence of accidents. Comparative analysis results show that the risk assessment coverage of the noisy-HBN has reached the state-of-the-art (SOTA) level.