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arXiv · 2009.04450

Map-Adaptive Goal-Based Trajectory Prediction

Lingyao Zhang·Po-Hsun Su·Jerrick Hoang·Galen Clark Haynes·Micol Marchetti-Bowick
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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.

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Lingyao Zhang, Po-Hsun Su, Jerrick Hoang, Galen Clark Haynes, Micol Marchetti-Bowick. 2020-11-13. Map-Adaptive Goal-Based Trajectory Prediction. https://arxiv.org/abs/2009.04450

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