Accurately assessing collision risk in dynamic traffic scenarios is a crucial requirement for trajectory planning in autonomous vehicles~(AVs) and enables a comprehensive safety evaluation of automated driving systems. To that end, this paper presents a novel probabilistic occupancy risk assessment~(PORA) metric. It uses spatiotemporal heatmaps as probabilistic occupancy predictions of surrounding traffic participants and estimates the risk of a collision along an AV's planned trajectory based on potential vehicle interactions. The use of probabilistic occupancy allows PORA to account for the uncertainty in future trajectories and velocities of traffic participants in the risk estimates. The risk from potential vehicle interactions is then further adjusted through a Cox model\edit{,} which considers the relative \edit{motion} between the AV and surrounding traffic participants. We demonstrate that the proposed approach enhances the accuracy of collision risk assessment in dynamic traffic scenarios, resulting in safer vehicle controllers, and provides a robust framework for real-time decision-making in autonomous driving systems. From evaluation in Monte Carlo simulations, PORA is shown to be more effective at accurately characterizing collision risk compared to other safety surrogate measures. Keywords: Dynamic Risk Assessment, Autonomous Vehicle, Probabilistic Occupancy, Driving Safety

翻译：在动态交通场景中准确评估碰撞风险是自动驾驶车辆（AVs）轨迹规划的关键需求，也是全面评估自动驾驶系统安全性的基础。为此，本文提出了一种新颖的概率占据风险评估（PORA）指标。该方法利用时空热图作为周围交通参与者的概率占据预测，并基于潜在的车辆交互作用，沿自动驾驶车辆的规划轨迹估计碰撞风险。通过采用概率占据，PORA能够在风险评估中考虑交通参与者未来轨迹和速度的不确定性。随后，通过Cox模型进一步调整由潜在车辆交互产生的风险，该模型考虑了自动驾驶车辆与周围交通参与者之间的相对运动。我们证明，所提出的方法提高了动态交通场景中碰撞风险评估的准确性，从而产生更安全的车辆控制器，并为自动驾驶系统中的实时决策提供了稳健的框架。通过蒙特卡洛模拟评估，PORA在准确表征碰撞风险方面相比其他安全替代指标更为有效。关键词：动态风险评估，自动驾驶车辆，概率占据，驾驶安全