Personalized Warning Systems for Automated Driving: Adapting to Individual Driving Styles for Enhanced Takeover Performance

Abstract

The transition of control between automated systems and human drivers represents a critical safety junction in autonomous vehicle operation. This abstract describes an investigation into how individual driving styles (aggressive versus cautious) affect takeover performance when control shifts from autonomous to manual driving. Various warning system configurations, including multi-modal alerts with different timing parameters, were tested to determine optimal notification strategies. Results indicate that driving style significantly influences reaction time, quality of control resumption, and subsequent driving stability following takeover events. Aggressive drivers demonstrated delayed responses to takeover requests but exhibited faster stabilization patterns, while cautious drivers showed more consistent and measured reactions throughout the takeover sequence. The findings suggest that adaptive warning systems, tailored to individual driving characteristics, could substantially improve safety during critical transitions. Vehicle manufacturers might implement personalized warning algorithms that adjust timing, intensity, and modality based on detected driving patterns to enhance human-automation collaboration during takeover scenarios.