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：自动驾驶车辆面临的一个尚未解决的挑战是在被遮挡的行人和车辆之间安全导航。以往的方法包括生成虚拟车辆并评估其风险，但这些方法往往使自车过于保守，或在严重遮挡情况下无法进行实时风险评估。我们提出了一种高效的遮挡感知风险评估方法，采用简化可达性量化技术，利用虚拟代理状态的简单分布模型来量化虚拟代理的可达性。此外，我们还提出了一种用于遮挡区域安全高效导航的驾驶策略，该策略利用虚拟代理的风险设定自动驾驶车辆的速度限制。通过仿真实验，我们在涉及其他车辆和障碍物的多种遮挡场景中评估了所提方法的性能。与无遮挡感知风险评估的基准情况相比，所提方法将交叉口通行时间增加了1.48倍，但平均碰撞率和不适分数分别降低了6.14倍和5.03倍。所提方法在时间效率上达到了先进水平，具有恒定时间复杂度，且计算时间小于5毫秒。