Trajectory prediction of other vehicles is crucial for autonomous vehicles, with applications from missile guidance to UAV collision avoidance. Typically, target trajectories are assumed deterministic, but real-world aerial vehicles exhibit stochastic behavior, such as evasive maneuvers or gliders circling in thermals. This paper uses Conditional Normalizing Flows, an unsupervised Machine Learning technique, to learn and predict the stochastic behavior of targets of guided missiles using trajectory data. The trained model predicts the distribution of future target positions based on initial conditions and parameters of the dynamics. Samples from this distribution are clustered using a time series k-means algorithm to generate representative trajectories, termed virtual targets. The method is fast and target-agnostic, requiring only training data in the form of target trajectories. Thus, it serves as a drop-in replacement for deterministic trajectory predictions in guidance laws and path planning. Simulated scenarios demonstrate the approach's effectiveness for aerial vehicles with random maneuvers, bridging the gap between deterministic predictions and stochastic reality, advancing guidance and control algorithms for autonomous vehicles.

翻译：其他车辆的轨迹预测对于自动驾驶车辆至关重要，其应用范围从导弹制导到无人机防撞。通常，目标轨迹被假定为确定性的，但现实世界中的飞行器表现出随机行为，例如规避机动或滑翔机在热气流中盘旋。本文采用条件归一化流——一种无监督机器学习技术——利用轨迹数据来学习和预测制导导弹目标的随机行为。训练后的模型根据初始条件和动力学参数预测未来目标位置的分布。通过时间序列k均值算法对该分布中的样本进行聚类，以生成代表性轨迹，称为虚拟目标。该方法快速且与目标无关，仅需要以目标轨迹形式存在的训练数据。因此，它可以作为制导律和路径规划中确定性轨迹预测的直接替代方案。仿真场景验证了该方法对具有随机机动的飞行器的有效性，弥合了确定性预测与随机现实之间的差距，推动了自动驾驶车辆制导与控制算法的进步。