交叉口不间断交通流中自动驾驶汽车驾驶风格识别的自适应博弈论决策

IF 5.2 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS
Yuxiao Cao, Yinuo Jiang, Xiangrui Zeng
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引用次数: 0

摘要

缺乏标准化的冲突解决机制给自动驾驶汽车在不间断的交通流中运行带来了严峻的挑战,特别是在管理与异构道路使用者的时间敏感互动时。现有方法要么通过过度简化多智能体交互而采取过于保守的策略,要么忽略了异构驾驶风格的关键影响。本文提出了一个针对不间断交通流场景下自动驾驶汽车的博弈论决策框架,专门用于解决多目标优化和驾驶风格适应的相互交织的挑战。一个层次博弈论架构集成了运动状态演化、可行性约束和交互行为建模,以严格模拟动态混合交通条件下的多车交互。一种新的在线识别机制通过实时交互模式分析来估计驾驶风格,而机器学习驱动的自适应框架通过离线随机森林训练和上下文感知在线策略调整来生成参数化策略。综合仿真验证了该框架在单个和多个交叉场景下的有效性,与传统的非自适应方法相比,显示出增强的交互适应性(效率提高10%以上)。实验结果表明,该模型能够有效处理不同类型的驾驶行为,并动态优化协商策略,为混合交通环境下的车辆决策提供了系统的、类人的决策解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Adaptive game-theoretic decision-making with driving style recognition for autonomous vehicles in uninterrupted traffic flows at intersections
The absence of standardized conflict resolution mechanisms presents critical challenges for autonomous vehicles operating in uninterrupted traffic flows, particularly when managing time-sensitive interactions with heterogeneous road users. Existing approaches either adopt overly conservative policies by oversimplifying multi-agent interactions or neglect the critical influence of heterogeneous driving styles. This paper proposes a game-theoretic decision-making framework for autonomous vehicles in uninterrupted traffic flow scenarios, specifically designed to address the intertwined challenges of multi-objective optimization and driving style adaptation. A hierarchical game-theoretic architecture integrates kinematic state evolution, feasibility constraints, and interactive behavior modeling to rigorously model multi-vehicle interactions under dynamic mixed traffic conditions. A novel online identification mechanism estimates driving styles through real-time interaction pattern analysis, while a machine learning-driven adaptive framework generates parametric policies through offline random forest training coupled with context-aware online policy adjustments. Comprehensive simulations validate the framework’s effectiveness in both single and multiple intersection scenarios, demonstrating enhanced interaction adaptability (more than 10% efficiency improvements) compared to conventional non-adaptive methods. Experimental results demonstrate the model’s capability to efficiently handle heterogeneous driving behaviors and dynamically refine negotiation strategies, providing a systematic, human-like vehicle decision-making solution for mixed traffic environments.
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来源期刊
Robotics and Autonomous Systems
Robotics and Autonomous Systems 工程技术-机器人学
CiteScore
9.00
自引率
7.00%
发文量
164
审稿时长
4.5 months
期刊介绍: Robotics and Autonomous Systems will carry articles describing fundamental developments in the field of robotics, with special emphasis on autonomous systems. An important goal of this journal is to extend the state of the art in both symbolic and sensory based robot control and learning in the context of autonomous systems. Robotics and Autonomous Systems will carry articles on the theoretical, computational and experimental aspects of autonomous systems, or modules of such systems.
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