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BridgeDrive: Diffusion Bridge Policy for Closed-Loop Trajectory Planning in Autonomous Driving
arXiv – CS AI|Shu Liu, Wenlin Chen, Weihao Li, Zheng Wang, Lijin Yang, Jianing Huang, Yipin Zhang, Zhongzhan Huang, Ze Cheng, Hao Yang||4 views
🤖AI Summary
BridgeDrive introduces a novel diffusion bridge policy for autonomous driving trajectory planning that transforms coarse anchor trajectories into refined plans while maintaining theoretical consistency. The system achieves state-of-the-art performance on the Bench2Drive benchmark with a 7.72% improvement in success rate and is compatible with real-time deployment.
Key Takeaways
- →BridgeDrive addresses asymmetry issues in existing diffusion-based planners by using a theoretically consistent diffusion bridge approach.
- →The system transforms coarse anchor trajectories into refined, context-aware plans for closed-loop autonomous driving scenarios.
- →Achieves 7.72% improvement in success rate over prior methods on the Bench2Drive closed-loop evaluation benchmark.
- →Compatible with efficient ODE solvers enabling real-time deployment in autonomous vehicles.
- →Addresses the key challenge of safe and reactive planning where the vehicle's actions influence future states.
#autonomous-driving#diffusion-models#trajectory-planning#machine-learning#ai-research#closed-loop-planning#bridgedrive#arxiv
Read Original →via arXiv – CS AI
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