There are several unresolved challenges for autonomous vehicles. One of them is safely navigating among occluded pedestrians and vehicles. Much of the previous work tried to solve this problem by generating phantom cars and assessing their risk. In this paper, motivated by the previous works, we propose an algorithm that efficiently assesses risks of phantom pedestrians/vehicles using Simplified Reachability Quantification. We utilized this occlusion risk to set a speed limit at the risky position when planning the velocity profile of an autonomous vehicle. This allows an autonomous vehicle to safely and efficiently drive in occluded areas. The proposed algorithm was evaluated in various scenarios in the CARLA simulator and it reduced the average collision rate by 6.14X, the discomfort score by 5.03X, while traversal time was increased by 1.48X compared to baseline 1, and computation time was reduced by 20.15X compared to baseline 2.

翻译：自主车辆仍面临若干未解决的挑战，其中之一是在被遮挡的行人与车辆中安全导航。以往许多研究试图通过生成虚拟车辆并评估其风险来解决这一问题。受前人工作启发，本文提出了一种利用简化可达性量化高效评估虚拟行人/车辆风险的算法。在规划自主车辆速度曲线时，我们利用这一遮挡风险在危险位置设置速度限制，从而使自主车辆能够在遮挡区域安全高效地行驶。所提算法在CARLA模拟器的多种场景下进行了评估，与基线1相比，平均碰撞率降低至1/6.14，不适评分降低至1/5.03，而通行时间增加至1.48倍；与基线2相比，计算时间减少至1/20.15。