# Dynamic Game-Theoretical Decision-Making Framework for Vehicle-Pedestrian Interaction with Human Bounded Rationality

arXiv: https://arxiv.org/abs/2409.15629 · HTML: https://mlsanity.com/paper/2409.15629/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Sep 2024.

## Built on by (most cited first, 7 of 7)

- [Game-Theoretic Modeling of Vehicle Unprotected Left Turns Considering Drivers' Bounded Rationality](https://mlsanity.com/paper/2507.03002/) (2025)
- [An Explainable Emotion Alignment Framework for LLM-Empowered Agent in Metaverse Service Ecosystem](https://mlsanity.com/paper/2507.22326/) (2025)
- [HABIT: Human Action Benchmark for Interactive Traffic in CARLA](https://mlsanity.com/paper/2511.19109/) (2025)
- [Free Energy-Inspired Cognitive Risk Integration for AV Navigation in Pedestrian-Rich Environments](https://mlsanity.com/paper/2507.20850/) (2025)
- [Emotional Cognitive Modeling Framework with Desire-Driven Objective Optimization for LLM-empowered Agent in Social Simulation](https://mlsanity.com/paper/2510.13195/) (2025)
- [Realistic adversarial scenario generation via human-like pedestrian model for autonomous vehicle control parameter optimisation](https://mlsanity.com/paper/2601.02082/) (2026)
- [Trajectory Prediction for Autonomous Driving in Road Construction Zones Using Human-in-the-Loop Driving Data](https://mlsanity.com/paper/2610.12747/) (2026)

## Abstract (arXiv)

Human-involved interactive environments pose significant challenges for autonomous vehicle decision-making processes due to the complexity and uncertainty of human behavior. It is crucial to develop an explainable and trustworthy decision-making system for autonomous vehicles interacting with pedestrians. Previous studies often used traditional game theory to describe interactions for its interpretability. However, it assumes complete human rationality and unlimited reasoning abilities, which is unrealistic. To solve this limitation and improve model accuracy, this paper proposes a novel framework that integrates the partially observable markov decision process with behavioral game theory to dynamically model AV-pedestrian interactions at the unsignalized intersection. Both the AV and the pedestrian are modeled as dynamic-belief-induced quantal cognitive hierarchy (DB-QCH) models, considering human reasoning limitations and bounded rationality in the decision-making process. In addition, a dynamic belief updating mechanism allows the AV to update its understanding of the opponent's rationality degree in real-time based on observed behaviors and adapt its strategies accordingly. The analysis results indicate that our models effectively simulate vehicle-pedestrian interactions and our proposed AV decision-making approach performs well in safety, efficiency, and smoothness. It closely resembles real-world driving behavior and even achieves more comfortable driving navigation compared to our previous virtual reality experimental data.