Map-Adaptive Goal-Based Trajectory Prediction
- URL: http://arxiv.org/abs/2009.04450v2
- Date: Fri, 13 Nov 2020 23:20:43 GMT
- Title: Map-Adaptive Goal-Based Trajectory Prediction
- Authors: Lingyao Zhang, Po-Hsun Su, Jerrick Hoang, Galen Clark Haynes, Micol
Marchetti-Bowick
- Abstract summary: 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.
We show that our model outperforms state-of-the-art approaches for vehicle trajectory prediction over a 6-second horizon.
- Score: 3.1948816877289263
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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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