Risk assessment is a crucial component of collision warning and avoidance systems in intelligent vehicles. To accurately detect potential vehicle collisions, reachability-based formal approaches have been developed to ensure driving safety, but suffer from over-conservatism, potentially leading to false-positive risk events in complicated real-world applications. In this work, we combine two reachability analysis techniques, i.e., backward reachable set (BRS) and stochastic forward reachable set (FRS), and propose an integrated probabilistic collision detection framework in highway driving. Within the framework, we can firstly use a BRS to formally check whether a two-vehicle interaction is safe; otherwise, a prediction-based stochastic FRS is employed to estimate a collision probability at each future time step. In doing so, the framework can not only identify non-risky events with guaranteed safety, but also provide accurate collision risk estimation in safety-critical events. To construct the stochastic FRS, we develop a neural network-based acceleration model for surrounding vehicles, and further incorporate confidence-aware dynamic belief to improve the prediction accuracy. Extensive experiments are conducted to validate the performance of the acceleration prediction model based on naturalistic highway driving data, and the efficiency and effectiveness of the framework with the infused confidence belief are tested both in naturalistic and simulated highway scenarios. The proposed risk assessment framework is promising in real-world applications.

翻译：风险评估是智能车辆碰撞预警与避撞系统的关键组成部分。为精确检测潜在车辆碰撞，基于可达性的形式化方法已被开发用于保障驾驶安全，但在复杂实际应用中存在过度保守的问题，可能导致假阳性风险事件。本文融合两种可达性分析技术——后向可达集与随机前向可达集，提出面向高速公路驾驶的集成式概率碰撞检测框架。在该框架中，我们首先利用后向可达集形式化检验两车交互是否安全；若存在风险，则采用基于预测的随机前向可达集估算每个未来时间步的碰撞概率。通过这种设计，该框架既能无风险事件进行安全保证识别，又可在安全关键事件中提供精确的碰撞风险估计。为构建随机前向可达集，我们开发了基于神经网络的环境车辆加速度模型，并进一步引入置信感知动态信念以提升预测精度。基于自然驾驶高速公路数据验证加速度预测模型性能，并在自然与仿真高速公路场景中测试融合置信信念框架的效能与效率。所提出的风险评估框架在实际应用中具有显著潜力。