Ensuring safe interactions between autonomous vehicles (AVs) and human drivers in mixed traffic systems remains a major challenge, particularly in complex, high-risk scenarios. This paper presents a cognition-decision framework that integrates individual variability and commonalities in driver behavior to quantify risk cognition and model dynamic decision-making. First, a risk sensitivity model based on a multivariate Gaussian distribution is developed to characterize individual differences in risk cognition. Then, a cognitive decision-making model based on the drift diffusion model (DDM) is introduced to capture common decision-making mechanisms in high-risk environments. The DDM dynamically adjusts decision thresholds by integrating initial bias, drift rate, and boundary parameters, adapting to variations in speed, relative distance, and risk sensitivity to reflect diverse driving styles and risk preferences. By simulating high-risk scenarios with lateral, longitudinal, and multidimensional risk sources in a driving simulator, the proposed model accurately predicts cognitive responses and decision behaviors during emergency maneuvers. Specifically, by incorporating driver-specific risk sensitivity, the model enables dynamic adjustments of key DDM parameters, allowing for personalized decision-making representations in diverse scenarios. Comparative analysis with IDM, Gipps, and MOBIL demonstrates that DDM more precisely captures human cognitive processes and adaptive decision-making in high-risk scenarios. These findings provide a theoretical basis for modeling human driving behavior and offer critical insights for enhancing AV-human interaction in real-world traffic environments.

With the emerging Internet of Things (IoT) and Vehicle-Road-Cloud Integration System (VRCIS) technologies, coordinating Connected and Automated Vehicles (CAVs) and traffic signal is becoming a practical solution to further enhance traffic efficiency. However, current studies still have limitations. Firstly, there is a domain mismatch between CAV trajectory planning (temporal domain) and signal optimization (spatial domain). This mismatch requires separate modeling of trajectory planning and signal optimization, which greatly reduces global optimality. Secondly, previous studies are not applicable to actual mixed traffic environment, since they mostly simplify Human-driven Vehicle’s (HV) behavior without considering queuing and stop-and-go maneuvers. Therefore, we propose a novel Multi-Vehicles and Signal Cooperation (MVSC) planner to solve the limitations via following designs. (i) Joint optimization is achieved via formulating in the spatial domain, unifying CAV’s planning domain with traffic signal optimizing domain. (ii) A parallel algorithm is designed for the adaptation to numbers of CAVs. This algorithm is based on Alternating Direction Method of Multipliers (ADMM), making full use of IoT and VRCIS. (iii) HV queuing and stop-and-go behaviors are considered in our modeling. Simulation results show that the proposed MVSC planner can enhance efficiency and ecology by 23.60% and 15.63%. At CAV’s penetration rate of 40% and V/C ratio of 0.75, the proposed planner shows its full potential in performance enhancement. The average computation time of parallel computing approach is only within 10 milliseconds, which confirms the real-time implementation capability.