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One of the unresolved challenges for autonomous vehicles is safe navigationamong occluded pedestrians and vehicles. Previous approaches includedgenerating phantom vehicles and assessing their risk, but they often made theego vehicle overly conservative or could not conduct a real-time riskassessment in heavily occluded situations. We propose an efficientocclusion-aware risk assessment method using simplified reachabilityquantification that quantifies the reachability of phantom agents with a simpledistribution model on phantom agents' state. Furthermore, we propose a drivingstrategy for safe and efficient navigation in occluded areas that sets thespeed limit of an autonomous vehicle using the risk of phantom agents.Simulations were conducted to evaluate the performance of the proposed methodin various occlusion scenarios involving other vehicles and obstacles. Comparedwith the baseline case of no occlusion-aware risk assessment, the proposedmethod increased the traversal time of an intersection by 1.48 times butdecreased the average collision rate and discomfort score by up to 6.14 timesand 5.03 times, respectively. The proposed method has shown thestate-of-the-art level of time efficiency with constant time complexity andcomputational time less than 5 ms.

Occlusion-aware Risk Assessment and Driving Strategy for Autonomous Vehicles Using Simplified Reachability Quantification