  End-to-end autonomous driving has garnered widespread attention. Current
end-to-end approaches largely rely on the supervision from perception tasks
such as detection, tracking, and map segmentation to aid in learning scene
representations. However, these methods require extensive annotations,
hindering the data scalability. To address this challenge, we propose a novel
self-supervised method to enhance end-to-end driving without the need for
costly labels. Specifically, our framework \textbf{LAW} uses a LAtent World
model to predict future latent features based on the predicted ego actions and
the latent feature of the current frame. The predicted latent features are
supervised by the actually observed features in the future. This supervision
jointly optimizes the latent feature learning and action prediction, which
greatly enhances the driving performance. As a result, our approach achieves
state-of-the-art performance in both open-loop and closed-loop benchmarks
without costly annotations.
