Summary
Adaptive Cruise Control (ACC) systems often drive in a way that feels too cautious for many drivers, especially when overtaking on highways. When drivers press the throttle to override ACC, it shows their preference for more aggressive acceleration. The authors view these overrides as helpful signals and use them to teach the system to better match each driver's style. They create a way to recognize driving situations and predict personalized acceleration patterns for smoother and more comfortable overtaking. Their tests on real road data show the method can create acceleration profiles that better fit individual driver expectations.
What this means in practice
- •For automotive software engineers: Improve ACC systems to predict and adjust acceleration based on driver override behaviors during highway overtaking.
- •For automotive product managers: Develop personalized ACC features that reduce driver intervention and enhance ride comfort by learning from real-world driver input.$Commercial implications: Enables personalized ACC products for vehicle manufacturers seeking competitive driver experience improvements.
Authors
Ruizheng Xu, Lounis Adouane, Javier Ibañez-Guzmán, Clément Zinoune
Abstract
Adaptive Cruise Control (ACC) systems are typically calibrated for an average driver, often resulting in a mismatch between vehicle behavior and individual expectations during time-critical maneuvers such as highway overtaking. When the ACC is perceived as too conservative and inconsistent, drivers intervene through throttle overrides, providing implicit feedback on the system's behavior. This paper reframes these override actions as human-in-theloop supervisory signals and proposes a data-driven framework for personalized vehicle adaptation, termed Context-driven Personalized ACC (CoP-ACC). Rather than relying solely on end-to-end regression, which tends to over-smooth dynamic responses, we introduce a hybrid pipeline combining: (i) unsupervised hierarchical clustering to extract representative acceleration profiles from override events; (ii) a context classifier that maps pre-maneuver driving conditions to the appropriate profile; and (iii) a residual regressor that refines the selected profile into a smooth, personalized acceleration profile tailored to the immediate context. Evaluated on real-world public-road data against a withheld forced-ACC baseline, the approach demonstrates high reconstruction fidelity and generates acceleration profiles that tend toward the driver's expected behavior in potential override contexts. The results highlight the potential of learning from shared-control overrides to enable anticipatory, personalized ACC behavior, reducing manual interventions and improving ride comfort.