论文标题
基于地图自适应目标的轨迹预测
Map-Adaptive Goal-Based Trajectory Prediction
论文作者
论文摘要
我们提出了一种用于多模式的长期车辆轨迹预测的新方法。我们的方法依赖于使用在环境的丰富地图中捕获的车道中心线来为每辆车生成一组建议的目标路径。使用这些路径（在运行时生成，因此可以动态适应场景）作为空间锚，我们预测了一组基于目标的轨迹以及对目标的绝对分布。这种方法使我们能够直接对交通行为者的目标指导行为进行建模，从而释放了更准确的长期预测的潜力。我们对大规模内部驾驶数据集和公共Nuscenes数据集的实验结果表明，我们的模型在6秒地平线上优于车辆轨迹预测的最先进方法。我们还从经验上证明，与现有方法相比，我们的模型能够更好地推广到全新城市的道路场景。
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.
