We propose a LAtent World model (LAW) for self-supervised learning that enhances the training of end-to-end autonomous driving.

Initailly, we develop an end-to-end driving framework to extract view latents and predict waypoints. Then, we predict the view latents of the next frame via the latent world model. The predicted view latent is supervised by the observed view latent of the next frame.

Please consider citing our work as follows if it is helpful.

@misc{li2024enhancing,
      title={Enhancing End-to-End Autonomous Driving with Latent World Model}, 
      author={Yingyan Li and Lue Fan and Jiawei He and Yuqi Wang and Yuntao Chen and Zhaoxiang Zhang and Tieniu Tan},
      year={2024},
      eprint={2406.08481},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}