Conflict prediction is a vital component of path planning for autonomous vehicles. Prediction methods must be accurate for reliable navigation, but also computationally efficient to enable online path planning. Efficient prediction methods are especially crucial when testing large sets of candidate trajectories. We present a prediction method that has the same accuracy as existing methods, but up to an order of magnitude faster. This is achieved by rewriting the conflict prediction problem in terms of the first-passage time distribution using a dimension-reduction transform. First-passage time distributions are analytically derived for a subset of Gaussian processes describing vehicle motion. The proposed method is applicable to 2-D stochastic processes where the mean can be approximated by line segments, and the conflict boundary can be approximated by piece-wise straight lines. The proposed method was tested in simulation and compared to two probability flow methods, as well as a recent instantaneous conflict probability method. The results demonstrate a significant decrease of computation time.

翻译：冲突预测是自动驾驶车辆路径规划中的关键组成部分。预测方法必须精确以实现可靠导航，同时需要具备计算高效性以支持在线路径规划。在测试大量候选轨迹时，高效的预测方法尤为重要。本文提出一种预测方法，其精度与现有方法相当，但计算速度快一个数量级。该方法通过降维变换将冲突预测问题重新表述为首达时间分布的形式实现。针对描述车辆运动的高斯过程子集，我们解析推导了首达时间分布。该方法适用于均值可近似为线段、冲突边界可近似为分段直线的二维随机过程。通过仿真测试，我们将该方法与两种概率流方法以及一种近期提出的瞬时冲突概率方法进行比较。结果表明计算时间显著降低。