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arXiv · 2112.01348

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

Ching-Yu Tseng·Po-Shao Lin·Yu-Jia Liou·Kuan-Chih Huang·Winston H. Hsu
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Abstract

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.

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Ching-Yu Tseng, Po-Shao Lin, Yu-Jia Liou, Kuan-Chih Huang, Winston H. Hsu. 2021-12-02. 3rd Place Solution for NeurIPS 2021 Shifts Challenge: Vehicle Motion Prediction. https://arxiv.org/abs/2112.01348

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