  Trajectory forecasting is critical for autonomous platforms to make safe
planning and actions. Currently, most trajectory forecasting methods assume
that object trajectories have been extracted and directly develop trajectory
predictors based on the ground truth trajectories. However, this assumption
does not hold in practical situations. Trajectories obtained from object
detection and tracking are inevitably noisy, which could cause serious
forecasting errors to predictors built on ground truth trajectories. In this
paper, we propose a trajectory predictor directly based on detection results
without relying on explicitly formed trajectories. Different from the
traditional methods which encode the motion cue of an agent based on its
clearly defined trajectory, we extract the motion information only based on the
affinity cues among detection results, in which an affinity-aware state update
mechanism is designed to take the uncertainty of association into account. In
addition, considering that there could be multiple plausible matching
candidates, we aggregate the states of them. This design relaxes the
undesirable effect of noisy trajectory obtained from data association.
Extensive ablation experiments validate the effectiveness of our method and its
generalization ability on different detectors. Cross-comparison to other
forecasting schemes further proves the superiority of our method. Code will be
released upon acceptance.
