Probabilistic risk uncertainty assessment for driver over-trust and under-trust in Level 3 human-automated driving systems cooperative driving based on the drift-diffusion model

Over-trust in automated driving systems (ADS) can trigger severe accidents, whereas under-trust may reduce system acceptance and efficiency. Thus, assessing risk uncertainty is critical for ensuring driving safety and enhancing system performance. This study aims to develop a cognitive model–based framework for risk uncertainty assessment in human-ADS cooperative driving, enabling precise tracking of the evolving risks of over-trust and under-trust. We propose a drift-diffusion model (DDM)–based risk uncertainty assessment approach applicable across diverse driving task scenarios. A driving simulation experiment was conducted with three levels of ADS reliability and five levels of task difficulty, yielding 7200 behavioral observations for model fitting and validation. The hierarchical Bayesian DDM demonstrated strong predictive performance, with simulated distributions closely matching experimental data. Results reveal that higher ADS reliability significantly shortens trust decision time, while the impact of task difficulty is non-monotonic. More importantly, the model successfully quantifies the time-varying risk uncertainty of over-trust and under-trust. These findings highlight the proposed framework as an effective and interpretable tool for evaluating time-varying risk uncertainty in human-ADS cooperation, providing a crucial model foundation for the future development of real-time risk prediction and intervention systems.