Genetic Algorithm-Based SOTIF Scenario Construction for Complex Traffic Flow

Abstract

The Safety of The Intended Functionality (SOTIF) challenge represents the triggering condition by elements of a specific scenario and exposes the function limitation of an autonomous vehicle (AV), which leads to hazards. As for operation-content-related features, the scenario is similar to AVs’ SOTIF research and development. Therefore, scenario generation is a significant topic for SOTIF verification and validation procedure, especially in the simulation testing of AVs. Thus, in this paper, a well-designed scenario architecture is first defined, with comprehensive scenario elements, to present SOTIF trigger conditions. Then, considering complex traffic disturbance as trigger conditions, a novel SOTIF scenario generation method is developed. An indicator, also known as Scenario Potential Risk, is defined as the combination of the safety control intensity and the prior collision probability. This indicator helps identify critical scenarios in the proposed method. In addition, the corresponding vehicle motion models are established for general straight roads, curved roads, and safety assessment areas. As for the traffic participants’ motion model, it is designed to construct the key dynamic events. To efficiently search for critical scenarios with the trigger of complex traffic flow, this scenario is encoded as genes and it is regenerated through selection, mutation, and crossover iteration processes, known as the Genetic Algorithm (GA). Experimental results show that the GA-based method could efficiently construct diverse and critical traffic scenarios, contributing to the construction of the SOTIF scenario library.