  One of the unresolved challenges for autonomous vehicles is safe navigation
among occluded pedestrians and vehicles. Previous approaches included
generating phantom vehicles and assessing their risk, but they often made the
ego vehicle overly conservative or could not conduct a real-time risk
assessment in heavily occluded situations. We propose an efficient
occlusion-aware risk assessment method using simplified reachability
quantification that quantifies the reachability of phantom agents with a simple
distribution model on phantom agents' state. Furthermore, we propose a driving
strategy for safe and efficient navigation in occluded areas that sets the
speed limit of an autonomous vehicle using the risk of phantom agents.
Simulations were conducted to evaluate the performance of the proposed method
in various occlusion scenarios involving other vehicles and obstacles. Compared
with the baseline case of no occlusion-aware risk assessment, the proposed
method increased the traversal time of an intersection by 1.48 times but
decreased the average collision rate and discomfort score by up to 6.14 times
and 5.03 times, respectively. The proposed method has shown the
state-of-the-art level of time efficiency with constant time complexity and
computational time less than 5 ms.
