Hypergame-based Cognition Modeling and Intention Interpretation for Human-Driven Vehicles in Connected Mixed Traffic

2025-10-17

AI Summary

Existing trajectory prediction models for human-driven vehicles (HVs) in mixed traffic suffer from inaccurate forecasts and over-reliance on assumptions of full rationality, failing to capture bounded rationality and inter-driver cognitive heterogeneity. Method: This paper proposes a hierarchical cognitive modeling framework grounded in hypergame theory. Leveraging V2X communication, it enables distributed driving-intent recognition by integrating inverse learning with real-time trajectory prediction. The framework formally characterizes HVs’ bounded rationality and driver-specific cognitive differences, and rigorously proves the existence of a hyper-Nash equilibrium under cognitive equilibrium conditions. Contribution/Results: The method supports both offline calibration and online prediction. In high-speed lane-change simulations, it significantly improves prediction accuracy and robustness, demonstrates strong resilience to observation noise, and enhances both safety and traffic efficiency of connected and autonomous vehicles (CAVs).

Technology Category

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

Application Category

• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

With the practical implementation of connected and autonomous vehicles (CAVs), the traffic system is expected to remain a mix of CAVs and human-driven vehicles (HVs) for the foreseeable future. To enhance safety and traffic efficiency, the trajectory planning strategies of CAVs must account for the influence of HVs, necessitating accurate HV trajectory prediction. Current research often assumes that human drivers have perfect knowledge of all vehicles' objectives, an unrealistic premise. This paper bridges the gap by leveraging hypergame theory to account for cognitive and perception limitations in HVs. We model human bounded rationality without assuming them to be merely passive followers and propose a hierarchical cognition modeling framework that captures cognitive relationships among vehicles. We further analyze the cognitive stability of the system, proving that the strategy profile where all vehicles adopt cognitively equilibrium strategies constitutes a hyper Nash equilibrium when CAVs accurately learn HV parameters. To achieve this, we develop an inverse learning algorithm for distributed intention interpretation via vehicle-to-everything (V2X) communication, which extends the framework to both offline and online scenarios. Additionally, we introduce a distributed trajectory prediction and planning approach for CAVs, leveraging the learned parameters in real time. Simulations in highway lane-changing scenarios demonstrate the proposed method's accuracy in parameter learning, robustness to noisy trajectory observations, and safety in HV trajectory prediction. The results validate the effectiveness of our method in both offline and online implementations.

Problem

• Modeling human driver cognition with bounded rationality in mixed traffic
• Developing inverse learning for distributed intention interpretation via V2X
• Creating trajectory prediction and planning for connected autonomous vehicles

Innovation

• Hypergame theory models human cognitive limitations
• Inverse learning algorithm interprets intentions via V2X
• Distributed trajectory planning uses real-time learned parameters