From prediction to prevention: safety modeling of driver takeover time with mental workload, risk perception, and driving style in ramp scenarios

In conditionally automated driving, delayed or unstable takeovers can escalate into hazardous situations, making accurate prediction of driver readiness a key element of accident prevention. This study develops a predictive framework that integrates mental workload, driving style, and real-time driving risk to anticipate takeover time and identify safety-critical conditions. Using data from 44 participants in a high-fidelity driving simulator replicating urban expressway ramps, takeover scenarios were categorized by ramp type, driver role, and driving style, with eye-tracking derived workload and risk perception metrics as inputs. The CatBoost-based model, supported by interpretability analysis, was applied to assess how individual and situational factors influence takeover performance. Results show that higher mental workload significantly prolongs takeover time, particularly in visually low-risk but cognitively demanding scenarios. Aggressive drivers respond faster but with reduced post-takeover stability, while cautious drivers show the opposite pattern. Ramp type and vehicle interaction events, such as lane cut-ins, further modulate takeover risk, with the model anticipating risk-inducing interactions up to 0.78 s before the actual interaction onset. These findings offer direct implications for adaptive takeover prompt timing, role-aware assistance, and personalized safety interventions, supporting proactive risk mitigation in automated driving.