Mechanistic Interpretability for End-to-End Self-Driving
Read the original on arXiv Machine Learning →The paper applies mechanistic interpretability to end‑to‑end autonomous driving by training Sparse Autoencoders on a single‑camera path‑planning model’s internal activations. Unsupervised clustering of the autoencoder’s latent space uncovers interpretable features linked to route intent, vehicle speed, and braking, achieving 98.3% accuracy in recovering route intent without supervision. Visual counterfactual edits confirm that specific latent features correspond to scene changes such as traffic lights and stop signs, and manipulating these latents yields predictable changes in planner outputs; a lightweight adapter trained on these latents reduces trajectory selection error by 3.44% compared to one that ignores them.
Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.