Enhancing safety in freeway on-ramp merging: Takeover warning through dynamic trust and risk perception modelling...

Freeway on-ramp merging represents a high risk conflict zone in which control transition failures in conditionally automated driving (AD) can contribute to severe collisions. Such events are often linked to a mismatch between rapidly shrinking safety margins and driver cognitive readiness at takeover. Conventional takeover warning strategies that rely on static kinematic thresholds may not account for the driver dynamic pre-crash state including trust, risk perception, and driving style. This limitation can increase the likelihood of missed alarms and nuisance alarms and can reduce the effectiveness of warnings for accident prevention. To address this safety gap, this study proposes a personalized accident prevention framework that identifies high risk states and triggers predictive intervention before physical conflict becomes critical. The method integrates a Kalman filter based dynamic trust estimator with a learned risk perception module to construct a driving style sensitive joint trigger mechanism. The mechanism issues warnings when the driver trust state deviates from a personalized trust interval that represents a safe operating envelope under prevailing risk. Two independent simulator experiments were conducted to calibrate style specific trust intervals and to validate the proposed strategy in complex merging scenarios. Comparative results show improved safety outcomes relative to a fixed threshold baseline. The proposed strategy reduced mean collision rate from 40.46% to 17.05% and increased correct takeover rate from 51.23% to 64.22%. These findings indicate that integrating dynamic human state estimation and driving style heterogeneity into takeover warning logic can support prevention of takeover related accidents and can enhance safety resilience in automated merging operations.