[Submitted on 30 Oct 2026]
Title:Mechanistic Interpretability for End-to-End Self-Driving
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Abstract:End-to-end (E2E) autonomous driving models achieve strong planning performance but remain largely opaque, limiting observability and trust. We apply mechanistic interpretability to E2E driving by training Sparse Autoencoders (SAEs) on the internal activations of a simple, single-camera end-to-end path planning model. Unsupervised clustering of SAE latents reveals interpretable structure aligned with route intent, vehicle speed, and braking behavior, recovering route intent with 98.3% accuracy without supervision. Through paired counterfactual image edits, we further show that specific SAE features are visually grounded and systematically respond to scene changes such as traffic light state and stop sign presence. Steering individual SAE latents produces predictable, monotonic changes in planner outputs including lateral displacement, curvature, and braking. Finally, we demonstrate a practical application of these representations: we build a lightweight adapter trained on SAE latents that reduces trajectory selection error by 3.44%, outperforming an equivalent adapter that omits SAE latents, confirming that the sparse representation captures useful information for interpretability. Code and visualizations are available at this https URL
Comments: Accepted to the Conference on Robot Learning (CoRL) 2026. Project page: this https URL
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV); Machine Learning (cs.LG)
Cite as: arXiv:2610.41185 [cs.RO]
(or arXiv:2610.41185v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2610.41185
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Manav Gagvani [view email] [v1] Fri, 30 Oct 2026 03:05:24 UTC (10,085 KB)
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