Understanding Driver Cognition and Decision-Making Behaviors in High-Risk Scenarios: A Drift Diffusion Perspective

2025-03-16

AI Summary

In high-risk mixed-traffic scenarios, safe interaction between autonomous vehicles (AVs) and human drivers remains hindered by insufficient cognitive modeling of human behavior. Method: This paper proposes a cognition-decision framework integrating individual differences with shared behavioral mechanisms. It innovatively couples a multivariate Gaussian risk-sensitivity model with a drift-diffusion model (DDM) featuring an adaptive decision threshold, enabling personalized, context-aware driving decision modeling. Contribution/Results: Validated on driving simulator data, the framework accurately predicts cognitive responses and decision behaviors under emergency maneuvers across lateral, longitudinal, and multidimensional risk scenarios. It significantly outperforms classical car-following and lane-changing models—including IDM, Gipps, and MOBIL—in predictive accuracy and behavioral fidelity. The framework provides an interpretable, quantifiable theoretical foundation and modeling tool for AV–human collaborative safety.

Technology Category

• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

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.

Problem

• Modeling driver cognition and decision-making in high-risk scenarios.
• Integrating individual variability and commonalities in driver behavior.
• Enhancing autonomous vehicle-human interaction in mixed traffic systems.

Innovation

• Risk sensitivity model using multivariate Gaussian distribution
• Cognitive decision-making model based on drift diffusion model
• Dynamic adjustment of DDM parameters for personalized decisions