Estimating Uncertainty For Vehicle Motion Prediction on Yandex Shifts Dataset
This addresses safety-critical uncertainty estimation for autonomous driving systems, though it appears incremental as it builds on an existing challenge framework.
The paper tackled vehicle motion prediction under distributional shift by developing a model that measures prediction uncertainty, achieving 2nd place on the Shifts Challenge leaderboard with significant benchmark improvements.
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.