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A particular challenge for both autonomous and human driving is dealing withrisk associated with dynamic occlusion, i.e., occlusion caused by othervehicles in traffic. Based on the theory of hypergames, we develop a novelmulti-agent dynamic occlusion risk (DOR) measure for assessing situational riskin dynamic occlusion scenarios. Furthermore, we present a white-box,scenario-based, accelerated safety validation framework for assessing safety ofstrategic planners in AV. Based on evaluation over a large naturalisticdatabase, our proposed validation method achieves a 4000% speedup compared todirect validation on naturalistic data, a more diverse coverage, and ability togeneralize beyond the dataset and generate commonly observed dynamic occlusioncrashes in traffic in an automated manner.

I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames