Autonomous systems in the road transportation network require intelligent mechanisms that cope with uncertainty to foresee the future. In this paper, we propose a multi-stage probabilistic approach for trajectory forecasting: trajectory transformation to displacement space, clustering of displacement time series, trajectory proposals, and ranking proposals. We introduce a new deep feature clustering method, underlying self-conditioned GAN, which copes better with distribution shifts than traditional methods. Additionally, we propose novel distance-based ranking proposals to assign probabilities to the generated trajectories that are more efficient yet accurate than an auxiliary neural network. The overall system surpasses context-free deep generative models in human and road agents trajectory data while performing similarly to point estimators when comparing the most probable trajectory.

翻译：道路交通运输网络中的自主系统需要具备应对不确定性的智能机制，以预判未来。本文提出一种用于轨迹预测的多阶段概率方法：将轨迹变换至位移空间、对位移时间序列进行聚类、生成轨迹提议及提议排序。我们引入了一种新的深度特征聚类方法——基于自条件生成对抗网络，该方法相较于传统方法能更好地应对分布偏移。此外，我们提出了一种新颖的基于距离的提议排序方法，用于为生成的轨迹分配概率，该方法比辅助神经网络更高效且准确。整体系统在人类和道路智能体轨迹数据上的表现超越了无上下文深度生成模型，同时在与最可能轨迹对比时，其表现与点估计方法相当。