论文标题
基于多头注意的概率车辆轨迹预测
Multi-Head Attention based Probabilistic Vehicle Trajectory Prediction
论文作者
论文摘要
本文介绍了概率车辆轨迹预测的在线能力深度学习模型。我们提出了一个基于多头注意的简单编码器架构。所提出的模型并联多个车辆的预测轨迹的分布。我们对互动进行建模的方法可以学会以无监督的方式参与一些有影响力的车辆，从而可以提高网络的解释性。使用高速公路上的自然轨迹的实验表明，在纵向和横向方向上的位置误差方面有明显的改善。
This paper presents online-capable deep learning model for probabilistic vehicle trajectory prediction. We propose a simple encoder-decoder architecture based on multi-head attention. The proposed model generates the distribution of the predicted trajectories for multiple vehicles in parallel. Our approach to model the interactions can learn to attend to a few influential vehicles in an unsupervised manner, which can improve the interpretability of the network. The experiments using naturalistic trajectories at highway show the clear improvement in terms of positional error on both longitudinal and lateral direction.
