Abstract:
The generation of safety-critical scenarios is a pivotal focus in the domain of autonomous driving, holding significant application value in areas such as autonomous driving testing, automotive safety assessments, and the establishment of automotive safety standards. It is the key to the implementation of autonomous driving applications. Existing research lacks a survey focusing on safety-critical scenario generation techniques. We provide a systematic review of safety-critical scenario generation techniques. We summarize the research progress in the field of safety-critical scenario generation techniques. Furthermore we conduct a comparative analysis of models dedicated to safety-critical scenario generation. In addition we explore safety-critical scenario generation methods based on clustering Bayesian networks and adversarial networks. Finally we present a prospective outlook on research trends in safety-critical scenario generation methods.
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