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.

翻译：自主车辆在遮挡行人与车辆环境中的安全导航仍是未解决的挑战之一。现有方法虽能生成虚拟车辆并评估其风险，但常导致自车过于保守，或在严重遮挡场景下无法实现实时风险评估。我们提出一种高效遮挡感知风险评估方法，通过简化可达性量化技术，利用虚拟代理状态的简单分布模型量化其可达性。进一步，我们提出基于虚拟代理风险设置自主车辆速度限制的驾驶策略，实现在遮挡区域的安全高效导航。通过模拟实验评估该方法在包含其他车辆及障碍物的多类遮挡场景中的性能。与无遮挡感知风险评估的基准方法相比，本方法使路口通行时间增加1.48倍，但平均碰撞率与不适得分分别降低6.14倍和5.03倍。该方法以恒定时间复杂度和小于5毫秒的计算耗时，展现出当前最优的时间效率水平。