Motion prediction of surrounding agents is an important task in context of autonomous driving since it is closely related to driver's safety. Vehicle Motion Prediction (VMP) track of Shifts Challenge focuses on developing models which are robust to distributional shift and able to measure uncertainty of their predictions. In this work we present the approach that significantly improved provided benchmark and took 2nd place on the leaderboard.

翻译：对周围物剂的机动性预测是自主驾驶方面的一项重要任务,因为它与驾驶员的安全密切相关。 车辆机动性预测(VMP)对轮椅挑战的追踪(Thangs Front)侧重于开发对分配性转移具有活力并能测量其预测不确定性的模型。 在这项工作中,我们提出了大大改进了基准的方法,并在领导板上占据了第二位。