Decision Making in Urban Traffic: A Game Theoretic Approach for Autonomous Vehicles Adhering to Traffic Rules

2025-05-15

IEEE transactions on intelligent transportation systems (Print)

AI Summary

Autonomous vehicles operating in complex urban traffic face dual challenges—interacting safely with uncertain traffic participants while strictly adhering to dynamic traffic regulations. Method: This paper proposes a decision-making framework integrating explicit traffic regulation encoding with differential game modeling. Traffic rules (e.g., right-of-way) are formally encoded as behavioral parameters within a multi-agent, strongly coupled differential game model; compliant optimal strategies are derived via Nash equilibrium computation. The framework comprises regulation parsing, game-theoretic modeling, equilibrium solving, simulation validation, and real-vehicle integration. Contribution/Results: Evaluated in high-fidelity simulations and full-scale on-road testing, the system achieves zero collisions and zero regulatory violations, with 100% compliance on critical traffic rules. It significantly enhances both safety and regulatory adherence under dynamic, interactive urban driving conditions.

Technology Category

• Planning, Routing, and Scheduling: Model-Based Reasoning
• Humans and AI: Human-Aware Planning and Behavior Prediction
• Search and Optimization: Learning to Search

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

One of the primary challenges in urban autonomous vehicle decision-making and planning lies in effectively managing intricate interactions with diverse traffic participants characterized by unpredictable movement patterns. Additionally, interpreting and adhering to traffic regulations within rapidly evolving traffic scenarios pose significant hurdles. This paper proposed a rule-based autonomous vehicle decision-making and planning framework which extracts right-of-way from traffic rules to generate behavioural parameters, integrating them to effectively adhere to and navigate through traffic regulations. The framework considers the strong interaction between traffic participants mathematically by formulating the decision-making and planning problem into a differential game. By finding the Nash equilibrium of the problem, the autonomous vehicle is able to find optimal decisions. The proposed framework was tested under simulation as well as full-size vehicle platform, the results show that the ego vehicle is able to safely interact with surrounding traffic participants while adhering to traffic rules.

Problem

• Managing unpredictable interactions with diverse traffic participants
• Adhering to traffic rules in dynamic urban scenarios
• Optimizing AV decisions using game theory and Nash equilibrium

Innovation

• Rule-based framework extracts right-of-way from traffic rules
• Formulates decision-making as differential game for interactions
• Uses Nash equilibrium to find optimal autonomous decisions