mlsanityData to 14 Oct 2026

3rd Place Solution for NeurIPS 2021 Shifts Challenge: Vehicle Motion Prediction

arXiv 2112.01348 ↗First posted 2 Dec 2021cs.CVcited by 0

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Abstract (arXiv)

Shifts Challenge: Robustness and Uncertainty under Real-World Distributional Shift is a competition held by NeurIPS 2021. The objective of this competition is to search for methods to solve the motion prediction problem in cross-domain. In the real world dataset, It exists variance between input data distribution and ground-true data distribution, which is called the domain shift problem. In this report, we propose a new architecture inspired by state of the art papers. The main contribution is the backbone architecture with self-attention mechanism and predominant loss function. Subsequently, we won 3rd place as shown on the leaderboard.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 10 of its 11 references that are in the map. First posted on arXiv 2 Dec 2021.

Builds on

10 of 11 references.

References · 10

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  3. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  4. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  5. 2019Searching for MobileNetV3cited by 1,994
  6. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
  7. 2017End-to-end Driving via Conditional Imitation Learningcited by 485
  8. 2021High-Performance Large-Scale Image Recognition Without Normalizationcited by 360
  9. 2020Can Autonomous Vehicles Identify, Recover From, and Adapt to Distribution Shifts?cited by 139
  10. 2018Deep Imitative Models for Flexible Inference, Planning, and Controlcited by 76

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