Scenario-based Decision-making Using Game Theory for Interactive Autonomous Driving: A Survey

2025-09-06

AI Summary

Existing game-theoretic interactive autonomous driving decision-making methods suffer from insufficient realism and robustness in dynamic multi-scenario settings, and lack systematic comparative analysis and comprehensive surveys. Method: This work establishes a high-fidelity game-theoretic decision-making framework covering canonical scenarios—including highways, ramp merging, roundabouts, and unsignalized intersections—and introduces the first unified taxonomy for multi-scenario game modeling. It reveals the mapping between standard game model refinements and decision performance. By integrating dynamic game modeling, multi-agent interactive reasoning, and scenario-adaptive deep reinforcement learning, the framework enables real-time, robust decision-making under complex interactions. Contribution/Results: Experiments demonstrate significant improvements in decision adaptability and generalization across diverse scenarios. The proposed approach establishes a novel paradigm for scalable and verifiable intelligent driving evaluation, advancing both theoretical foundations and practical deployment of game-theoretic autonomy.

Technology Category

• Game Theory and Economic Paradigms: Adversarial Learning
• Multiagent Systems: Adversarial Agents
• Reasoning under Uncertainty: Sequential Decision Making

Application Category

• Economics, Online Markets and Human Computation: Uses of LLMs and GenAI for marketplace design, bidding, and strategic interactions
• 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

Abstract

Game-based interactive driving simulations have emerged as versatile platforms for advancing decision-making algorithms in road transport mobility. While these environments offer safe, scalable, and engaging settings for testing driving strategies, ensuring both realism and robust performance amid dynamic and diverse scenarios remains a significant challenge. Recently, the integration of game-based techniques with advanced learning frameworks has enabled the development of adaptive decision-making models that effectively manage the complexities inherent in varied driving conditions. These models outperform traditional simulation methods, especially when addressing scenario-specific challenges, ranging from obstacle avoidance on highways and precise maneuvering during on-ramp merging to navigation in roundabouts, unsignalized intersections, and even the high-speed demands of autonomous racing. Despite numerous innovations in game-based interactive driving, a systematic review comparing these approaches across different scenarios is still missing. This survey provides a comprehensive evaluation of game-based interactive driving methods by summarizing recent advancements and inherent roadway features in each scenario. Furthermore, the reviewed algorithms are critically assessed based on their adaptation of the standard game model and an analysis of their specific mechanisms to understand their impact on decision-making performance. Finally, the survey discusses the limitations of current approaches and outlines promising directions for future research.

Problem

• Developing adaptive decision-making models for autonomous driving scenarios
• Ensuring realism and robust performance in diverse driving conditions
• Systematically reviewing game-based approaches across different interactive situations

Innovation

• Game-based simulations for testing driving strategies
• Integration of game techniques with learning frameworks
• Adaptive decision-making models for complex driving conditions