Author

Benjamin Namikas

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#machine learning Preprint Oct 2026

Mechanistic Interpretability for End-to-End Self-Driving

Mechanistic interpretability to E2E driving is applied by training Sparse Autoencoders on the internal activations of a simple, single-camera end-to-end path planning model, revealing interpretable structure aligned with route intent, vehicle speed, and braking behavior.

Manav Gagvani, Benjamin Namikas, Sivamurugan Velmurugan et al. · 0 citations