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About this paper
Xiaolei Chen, Junchi Yan, Wenlong Liao, Tao He, Pai Peng
The paper introduces Int2Planner, an intention-based, Transformer-based model that integrates multi-modal trajectory prediction and motion planning for autonomous driving. It achieved competitive performance on the nuPlan benchmark and a private dataset, demonstrating robust real-world deployment in urban environments by generating safe and reasonable trajectories.
Key insights
- Integrating trajectory prediction and motion planning within a unified framework significantly improves interaction modeling and overall planning robustness for autonomous vehicles.
- Leveraging the ego vehicle's known navigation route to generate dynamic "Route Intention Points" provides an effective mechanism to constrain uncertainty and produce meaningful multi-modal planning trajectories.
- An iterative refinement process within a Transformer-based decoder consistently improves the accuracy and confidence of both predicted and planned trajectories.
Summary by alphaXiv.
Tip: change arxiv.org to autoarxiv.org in any arXiv abstract link to open the paper here.