![[2602.22801] Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving](https://arxiv.org/static/browse/0.3.4/images/arxiv-logo-fb.png){kind=logo}
[2602.22801] Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving
Summary
This article explores the application of diffusion models in end-to-end autonomous driving, demonstrating their effectiveness through extensive real-world testing and improvements in planning performance.
Why It Matters
As autonomous driving technology evolves, understanding how to effectively implement advanced models like diffusion can lead to safer and more efficient driving systems. This research highlights the potential of these models in real-world scenarios, addressing a critical gap in the field.
Key Takeaways
- Diffusion models show promise as planners for end-to-end autonomous driving.
- The study achieved a 10x performance improvement over baseline models through real-world testing.
- Key insights into diffusion loss space and trajectory representation were identified, impacting planning performance.
Computer Science > Robotics arXiv:2602.22801 (cs) [Submitted on 26 Feb 2026] Title:Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving Authors:Yinan Zheng, Tianyi Tan, Bin Huang, Enguang Liu, Ruiming Liang, Jianlin Zhang, Jianwei Cui, Guang Chen, Kun Ma, Hangjun Ye, Long Chen, Ya-Qin Zhang, Xianyuan Zhan, Jingjing Liu View a PDF of the paper titled Unleashing the Potential of Diffusion Models for End-to-End Autonomous Driving, by Yinan Zheng and 13 other authors View PDF HTML (experimental) Abstract:Diffusion models have become a popular choice for decision-making tasks in robotics, and more recently, are also being considered for solving autonomous driving tasks. However, their applications and evaluations in autonomous driving remain limited to simulation-based or laboratory settings. The full strength of diffusion models for large-scale, complex real-world settings, such as End-to-End Autonomous Driving (E2E AD), remains underexplored. In this study, we conducted a systematic and large-scale investigation to unleash the potential of the diffusion models as planners for E2E AD, based on a tremendous amount of real-vehicle data and road testing. Through comprehensive and carefully controlled studies, we identify key insights into the diffusion loss space, trajectory representation, and data scaling that significantly impact E2E planning performance. Moreover, we also provide an effective reinforcement learning post-training strategy to further e...
