A Safety Assessment Method Based on Cloud Model for Decision-making of Autonomous Vehicles

Safety is a paramount concern in the realm of autonomous vehicles. Developing precise safety assessment is challenging due to the need to blend qualitative and quantitative analyses of various safety factors. To address this challenge, this paper presents an innovative safety assessment method based on the cloud model. This method employs fundamental cloud model elements like expectation, entropy, and ultra-entropy. It also employs a sophisticated double conditional single rule generator to integrate multiple assessment indicators, resulting in an integrated risk assessment cloud. This cloud dynamically represents varying risk levels based on indicator characteristics. The method evaluates the real-time safety level by assessing the proximity between the integrated risk assessment cloud and the standard cloud. This proximity analysis reveals the prevailing risk level. Empirical validation involves rigorous testing within typical scenarios, demonstrating the utility and potential of the method to assess safety for decision-making of autonomous vehicles. The capacity of the method to monitor and assess autonomous vehicle decision-making systems makes it a significant contribution to the field. Beyond empirical contributions, this paper offers theoretical insights that can shape the future of safety assessment methods for autonomous vehicles. In summary, this paper emphasizes the importance of safety for autonomous vehicles and paves the way for evolving safety assessment methods in this dynamic field.