I Know You Can't See Me: Dynamic Occlusion-Aware Safety Validation of Strategic Planners for Autonomous Vehicles Using Hypergames
This addresses safety validation for autonomous vehicles in occlusion scenarios, representing a novel method for a known bottleneck with strong specific gains.
The paper tackles the challenge of dynamic occlusion risk in autonomous vehicles by developing a novel multi-agent risk measure and an accelerated safety validation framework, achieving a 4000% speedup in validation compared to direct methods while generating realistic crash scenarios.
A particular challenge for both autonomous and human driving is dealing with risk associated with dynamic occlusion, i.e., occlusion caused by other vehicles in traffic. Based on the theory of hypergames, we develop a novel multi-agent dynamic occlusion risk (DOR) measure for assessing situational risk in dynamic occlusion scenarios. Furthermore, we present a white-box, scenario-based, accelerated safety validation framework for assessing safety of strategic planners in AV. Based on evaluation over a large naturalistic database, our proposed validation method achieves a 4000% speedup compared to direct validation on naturalistic data, a more diverse coverage, and ability to generalize beyond the dataset and generate commonly observed dynamic occlusion crashes in traffic in an automated manner.