mlsanityData to 7 Oct 2026

Estimating Uncertainty For Vehicle Motion Prediction on Yandex Shifts Dataset

arXiv 2112.08355 ↗First posted 15 Dec 2021cs.LG cs.RO stat.MLcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 20 of its 23 references that are in the map. First posted on arXiv 15 Dec 2021.

Builds on

20 of 23 references.

References · 20

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8 more
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Cited by year

Citing papers per year: 2022: 1; 2023: 0; 2024: 2.

3 papers.

  1. 2022Uncertainty estimation for Cross-dataset performance in Trajectory predictioncited by 13
  2. 2024Stochasticity in Motion: An Information-Theoretic Approach to Trajectory Predictioncited by 4
  3. 2024Building Real-time Awareness of Out-of-distribution in Trajectory Prediction for Autonomous Vehiclescited by 3