We propose a novel truncated diffusion model, DiffusionDrive, for real-time end-to-end autonomous driving, which is much faster (10x reduction in diffusion denoising steps), more accurate (3.5 higher PDMS on NAVSIM), and more diverse (64% higher mode diversity score) than the vanilla diffusion policy. Without bells and whistles, DiffusionDrive achieves record-breaking 88.1 PDMS on NAVSIM benchmark with the same ResNet-34 backbone by directly learning from human demonstrations, while running at a real-time speed of 45 FPS.

The primary use of DiffusionDrive is for the end-to-end autonomous driving.

@article{diffusiondrive, title={DiffusionDrive: Truncated Diffusion Model for End-to-End Autonomous Driving}, author={Bencheng Liao and Shaoyu Chen and Haoran Yin and Bo Jiang and Cheng Wang and Sixu Yan and Xinbang Zang and Xiangyu Li and Ying Zhang and Qian Zhang and Xinggang Wang}, journal={arXiv preprint arXiv:2411.15139}, year={2024} }