摘 要
随着智能交通系统的快速发展，自动驾驶技术已成为研究热点，其中决策算法作为核心模块对车辆的安全性和效率起着关键作用。本研究旨在设计一种高效、可靠的自动驾驶车辆决策算法，并通过仿真验证其性能。为此，提出了一种融合规则与机器学习的混合决策框架，该框架结合了基于场景的知识库和深度强化学习模型，能够在复杂动态环境中实现路径规划与行为决策的优化。通过构建高精度仿真平台，模拟多种典型交通场景，验证了算法在不确定性环境下的适应性与鲁棒性。实验结果表明，所提算法能够显著提升决策效率，降低计算资源消耗，同时在安全性指标上优于传统方法。本研究的主要贡献在于创新性地将数据驱动与知识引导相结合，为自动驾驶决策系统提供了新的解决思路，为实际应用奠定了理论基础。
关键词：自动驾驶决策算法；混合决策框架；深度强化学习
Abstract
With the rapid development of intelligent transportation systems, autonomous driving technology has become a research hotspot, where decision-making algorithms, as a core module, play a critical role in ensuring vehicle safety and efficiency. This study aims to design an efficient and reliable decision-making algorithm for autonomous vehicles and validate its performance through simulations. To achieve this, a hybrid decision-making fr amework integrating rules and machine learning is proposed, combining a scenario-based knowledge base with a deep reinforcement learning model to optimize path planning and behavioral decision-making in complex dynamic environments. A high-precision simulation platform was constructed to replicate various typical traffic scenarios, demonstrating the algorithm's adaptability and robustness under conditions of uncertainty. Experimental results indicate that the proposed algorithm significantly enhances decision-making efficiency while reducing computational resource consumption and outperforms traditional methods in terms of safety metrics. The primary contribution of this research lies in innovatively integrating data-driven approaches with knowledge-guided strategies, providing a novel solution for autonomous driving decision-making systems and laying a theoretical foundation for practical applications.
Keywords: Autonomous Driving Decision Algorithm；Hybrid Decision fr amework；Deep Reinforcement Learning
目 录
引言 1
一、自动驾驶决策算法基础研究 1
（一）决策算法分类与特点 1
（二）关键技术挑战分析 2
（三）算法设计核心原则 2
二、基于场景的决策算法建模 2
（一）场景数据采集与处理 3
（二）决策模型构建方法 3
（三）模型优化策略探讨 3
三、决策算法性能评估体系 4
（一）评估指标体系设计 4
（二）数据驱动的测试方法 4
（三）性能瓶颈分析与改进 5
四、决策算法仿真验证研究 5
（一）仿真平台搭建与配置 5
（二）仿真场景设计与实现 6
（三）验证结果分析与总结 6
结 论 6
致 谢 8
参考文献 9
