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arXiv AI By Ruizheng Xu (Heudiasyc), Lounis Adouane (Heudiasyc), Javier Iba\~nez-Guzm\'an, Cl\'ement Zinoune

Learning from Shared-Control Overrides: Context-Driven Acceleration Profile Prediction for Personalized Overtaking

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The paper introduces Context-driven Personalized ACC (CoP-ACC), a data‑driven framework that learns from drivers’ throttle overrides to tailor Adaptive Cruise Control behavior. It uses unsupervised clustering to identify representative acceleration profiles, a context classifier to select the appropriate profile based on pre‑maneuver conditions, and a residual regressor to smooth the final profile. Evaluations on real‑world public‑road data show that CoP-ACC reconstructs driver‑expected acceleration patterns more accurately than a standard forced‑ACC baseline, suggesting it can reduce manual interventions and improve ride comfort.

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 AI.

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