Real-time safety systems are crucial components of intelligent vehicles. This
paper introduces a prediction-based collision risk assessment approach on
highways. Given a point mass vehicle dynamics system, a stochastic forward
reachable set considering two-dimensional motion with vehicle state probability
distributions is firstly established. We then develop an acceleration
prediction model, which provides multi-modal probabilistic acceleration
distributions to propagate vehicle states. The collision probability is
calculated by summing up the probabilities of the states where two vehicles
spatially overlap. Simulation results show that the prediction model has
superior performance in terms of vehicle motion position errors, and the
proposed collision detection approach is agile and effective to identify the
collision in cut-in crash events.

本文介绍了一种基于预测的高速公路碰撞风险评估方法，利用点动态系统建立了双向概率分布运动的随机正向可达集，提出了加速度预测模型，以及对车辆状态进行推广的多模态概率加速度分布和碰撞概率计算方法，模拟结果表明该预测模型在车辆位置误差方面具有优秀的性能，该碰撞检测方法在切入事件中识别碰撞的能力敏捷且有效。