Transformer based trajectory prediction
This work addresses the critical problem of predicting future motions of other agents for autonomous vehicles, but it is incremental as it builds on existing transformer methods.
The paper tackles motion prediction for autonomous vehicles by proposing a simple transformer-based baseline that is uncertainty-aware and robust to domain changes, achieving first place in the 2021 Shifts Vehicle Motion Prediction Competition.
To plan a safe and efficient route, an autonomous vehicle should anticipate future motions of other agents around it. Motion prediction is an extremely challenging task which recently gained significant attention of the research community. In this work, we present a simple and yet strong baseline for uncertainty aware motion prediction based purely on transformer neural networks, which has shown its effectiveness in conditions of domain change. While being easy-to-implement, the proposed approach achieves competitive performance and ranks 1$^{st}$ on the 2021 Shifts Vehicle Motion Prediction Competition.