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.

通过使用环境中富含地图的车道中心线来提高多模态、长期车辆轨迹预测方法的准确性，实现了汽车行驶的目标方向建模，生成一组已提议的车辆目标路径，并将其用作空间锚点，进而预测基于目标的轨迹和对目标的分类分布，在 6 秒的预测角度内，在内部驾驶数据集和公共 nuScenes 数据集上实验结果表明，该模型的性能优于先进的车辆轨迹预测方法。