3rd Place Solution for NeurIPS 2021 Shifts Challenge: Vehicle Motion Prediction
This work addresses domain shift in vehicle motion prediction for autonomous driving, but it is incremental as it builds on existing state-of-the-art methods.
The paper tackled the motion prediction problem under real-world distributional shift by proposing a new architecture with self-attention and a predominant loss function, achieving 3rd place in the NeurIPS 2021 Shifts Challenge.
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.