Map-Adaptive Goal-Based Trajectory Prediction
Authors
Lingyao Zhang (Uber ATG)*; Po-Hsun Su (UATC LLC); Jerrick Hoang (Uber ATG); Galen Clark Haynes (Uber ATC); Micol Marchetti-Bowick (Uber ATG)
Interactive Session
2020-11-16, 11:50 - 12:20 PST | PheedLoop Session
Abstract
We present a new method for multi-modal, long-term vehicle trajectory prediction. Our approach relies on using lane centerlines captured in rich maps of the environment to generate a set of proposed goal paths for each vehicle. Using these paths - which are generated at run time and therefore dynamically adapt to the scene - as spatial anchors, we predict a set of goal-based trajectories along with a categorical distribution over the goals. This approach allows us to directly model the goal-directed behavior of traffic actors, which unlocks the potential for more accurate long-term prediction. Our experimental results on both a large-scale internal driving dataset and on the public nuScenes dataset show that our model outperforms state-of-the-art approaches for vehicle trajectory prediction over a 6-second horizon. We also empirically demonstrate that our model is better able to generalize to road scenes from a completely new city than existing methods.