Likely, Light, and Accurate Context-Free Clusters-based Trajectory
  Prediction
Tiago Rodrigues de Almeida Oscar Martinez Mozos
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.