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
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.