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README.md

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+# BridgeDrive Model Card
+
+## Model Details
+
+Diffusion-based planners excel in autonomous driving by capturing multi-modal behaviors, but guiding them for safe, closed-loop planning remains challenging. Existing methods rely on anchor trajectories but suffer from a truncated diffusion process that breaks theoretical consistency. We introduce BridgeDrive, an anchor-guided diffusion bridge policy that directly transforms coarse anchors into refined plans while preserving consistency between forward and reverse processes. BridgeDrive supports efficient ODE solvers for real-time deployment. We achieve state-of-the-art performance on the Bench2Drive closed-loop evaluation benchmark, improving the success rate by 7.72% and 2.45% over prior arts with PDM-Lite and LEAD datasets, respectively.
+- **Developed by:** [Bosch] (https://www.bosch.de/), 
+- **Model type:** An end-to-end autonomous driving model based on the diffusion bridge policy for closed-loop settings.
+
+
+### Model Sources
+
+- **Repository:** https://github.com/shuliu-ethz/BridgeDrive
+- **Paper:** https://openreview.net/pdf?id=dJKhjK4zpp 
+
+## Uses
+
+The primary use of BridgeDrive is for the end-to-end autonomous driving in closed-loop settings.
+
+
+## Citation Information
+
+@inproceedings{
+liu2026bridgedrive,
+title={BridgeDrive: Diffusion Bridge Policy for Closed-Loop Trajectory Planning in Autonomous Driving},
+author={Shu Liu and Wenlin Chen and Weihao Li and Zheng Wang and Lijin Yang and Jianing Huang and Yipin Zhang and Zhongzhan Huang and Ze Cheng and Hao Yang},
+booktitle={The Fourteenth International Conference on Learning Representations},
+year={2026},
+url={https://arxiv.org/abs/2509.23589}
+}