To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of other agents around it. Motion prediction is an extremely challenging task which recently gained significant attention of the research community. In this work, we present a simple and yet strong baseline for uncertainty aware motion prediction based purely on transformer neural networks, which has shown its effectiveness in conditions of domain change. While being easy-to-implement, the proposed approach achieves competitive performance and ranks 1$^{st}$ on the 2021 Shifts Vehicle Motion Prediction Competition.
翻译:为了规划一条安全而高效的路线,自主的车辆应该预见其他代理人今后围绕这条路线的动向; 运动预测是一项极具挑战性的任务,最近引起了研究界的极大关注; 在这项工作中,我们提出了一个简单而有力的基线,说明完全基于变压器神经网络的不确定感知运动预测,这显示了其在领域变化条件下的有效性; 拟议的办法虽然易于实施,但取得了竞争性业绩,在2021年轮动车辆预测竞赛中排名1美元。