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.

提出多阶段概率预测路径的自主系统的新方法，包括轨迹转换、位移时间序列聚类、轨迹生成和排序建议，使用深度特征聚类方法进行分布偏移、使用新的基于距离的排序建议进行轨迹生成和分配概率，从而比传统方法更有效且准确地在人和道路代理的轨迹数据上实现上胜于无上下文的深度生成模型，同时当比较最可能的轨迹时与点估计器表现相似。