HyperTraj: Towards Simple and Fast Scene-Compliant Endpoint Conditioned Trajectory Prediction

Renhao Huang, Maurice Pagnucco, Yang Song

Abstract: An important task in trajectory prediction is to model the uncertainty of agents' motions, which requires the system to propose multiple plausible future trajectories for agents based on their past movements. Recently, many approaches have been developed following an endpointconditioned deep learning framework by firstly predicting the distribution of endpoints, then sampling endpoints from it and finally completing their waypoints. However, this framework suffers a severe efficiency issue as it needs to repeatedly execute a separate decoder conditioned on multiple sampled endpoints. In this work, we propose a simple and fast endpoint conditioned fully convolutional trajectory prediction framework, called HyperTraj, by using dynamic convolutions to generate multiple trajectories, with the main benefits that (1) our prediction is conditioned on endpoint but takes almost constant time when the number of goals increases and (2) our model benefits from convolutional based predictions, such as the acceptance of various scene sizes and better modeling of agent-scene interactions. In our experiment, our model shows comparable or even better accuracy than our state-of-the-art baselines on SDD and VIRAT datasets with around 84% of acceleration and 90% model weight reduction for waypoint decoding.

External IDs:dblp:conf/iros/HuangP023