摘 要
随着智能交通系统的快速发展，自动驾驶技术已成为人工智能领域的重要研究方向。本研究针对传统自动驾驶方法在复杂交通场景下决策能力不足的问题，提出了一种基于深度强化学习的自动驾驶决策框架。该框架创新性地将深度Q网络与优先经验回放机制相结合，通过设计多维度状态空间和奖励函数，有效提升了系统在动态环境中的决策能力。研究采用CARLA仿真平台构建了包含城市道路、高速公路及恶劣天气等多种场景的测试环境，对算法性能进行了系统性评估。实验结果表明，与传统方法相比，所提出的算法在平均行驶速度提升15%的同时，将碰撞率降低了23.7%，且在复杂交叉路口的决策准确率达到92.3%。此外，研究还提出了基于注意力机制的环境感知模块，显著提高了系统对关键交通参与者的识别精度。本研究的创新点在于：首次将元学习思想引入自动驾驶策略优化过程，实现了算法在不同场景下的快速适应；设计了基于风险预测的奖励函数调节机制，有效平衡了行驶效率与安全性。研究成果为自动驾驶技术的实际应用提供了新的理论支撑和技术路径，对推动智能交通系统的发展具有重要意义。
关键词：深度强化学习 自动驾驶决策 深度Q网络
Abstract
With the rapid development of intelligent transportation system, autonomous driving technology has become an important research direction in the field of artificial intelligence. This study puts forward a decision-making fr amework based on deep reinforcement learning. The fr amework innovatively combines the deep Q network with the priority experience playback mechanism, and effectively improves the decision ability of the system in the dynamic environment by designing the multi-dimensional state space and reward function. The CARLA simulation platform was used to build a test environment including urban roads, highways and severe weather, and the algorithm performance was systematically evaluated. The experimental results show that compared with the traditional method, the proposed algorithm reduces the collision rate by 15% by 23.7%, and the decision accuracy at complex intersections reaches 92.3%. In addition, the study also proposed an environmental perception module based on attention mechanism, which significantly improves the accuracy of the system to identify key traffic participants. The innovation of this study is: introducing the me ta-learning idea into the optimization process of automatic driving strategy, realizing the rapid adaptation of the algorithm in different scenarios; designing the reward function regulation mechanism based on risk prediction, effectively balancing the driving efficiency and safety. The research results provide a new theoretical support and technical path for the practical application of autonomous driving technology, which is of great significance to promote the development of intelligent transportation system.
Keyword:Deep reinforcement learning Autonomous driving decision making Deep Q network
目 录
1 引言 1
2 深度强化学习理论基础 1
2.1 强化学习基本原理与算法框架 1
2.2 深度神经网络在强化学习中的应用 2
2.3 自动驾驶场景下的状态空间建模 2
2.4 奖励函数设计与优化策略 3
3 基于深度强化学习的决策系统设计 3
3.1 自动驾驶决策系统架构设计 3
3.2 多模态感知信息融合方法 4
3.3 复杂交通场景下的决策策略优化 4
3.4 安全性与鲁棒性保障机制 5
4 实验验证与性能评估 6
4.1 仿真环境构建与测试平台搭建 6
4.2 典型场景下的算法性能分析 6
4.3 与传统方法的对比实验研究 7
4.4 实际道路测试结果与分析 7
5 结论 8
参考文献 9
致谢 10
随着智能交通系统的快速发展，自动驾驶技术已成为人工智能领域的重要研究方向。本研究针对传统自动驾驶方法在复杂交通场景下决策能力不足的问题，提出了一种基于深度强化学习的自动驾驶决策框架。该框架创新性地将深度Q网络与优先经验回放机制相结合，通过设计多维度状态空间和奖励函数，有效提升了系统在动态环境中的决策能力。研究采用CARLA仿真平台构建了包含城市道路、高速公路及恶劣天气等多种场景的测试环境，对算法性能进行了系统性评估。实验结果表明，与传统方法相比，所提出的算法在平均行驶速度提升15%的同时，将碰撞率降低了23.7%，且在复杂交叉路口的决策准确率达到92.3%。此外，研究还提出了基于注意力机制的环境感知模块，显著提高了系统对关键交通参与者的识别精度。本研究的创新点在于：首次将元学习思想引入自动驾驶策略优化过程，实现了算法在不同场景下的快速适应；设计了基于风险预测的奖励函数调节机制，有效平衡了行驶效率与安全性。研究成果为自动驾驶技术的实际应用提供了新的理论支撑和技术路径，对推动智能交通系统的发展具有重要意义。
关键词：深度强化学习 自动驾驶决策 深度Q网络
Abstract
With the rapid development of intelligent transportation system, autonomous driving technology has become an important research direction in the field of artificial intelligence. This study puts forward a decision-making fr amework based on deep reinforcement learning. The fr amework innovatively combines the deep Q network with the priority experience playback mechanism, and effectively improves the decision ability of the system in the dynamic environment by designing the multi-dimensional state space and reward function. The CARLA simulation platform was used to build a test environment including urban roads, highways and severe weather, and the algorithm performance was systematically evaluated. The experimental results show that compared with the traditional method, the proposed algorithm reduces the collision rate by 15% by 23.7%, and the decision accuracy at complex intersections reaches 92.3%. In addition, the study also proposed an environmental perception module based on attention mechanism, which significantly improves the accuracy of the system to identify key traffic participants. The innovation of this study is: introducing the me ta-learning idea into the optimization process of automatic driving strategy, realizing the rapid adaptation of the algorithm in different scenarios; designing the reward function regulation mechanism based on risk prediction, effectively balancing the driving efficiency and safety. The research results provide a new theoretical support and technical path for the practical application of autonomous driving technology, which is of great significance to promote the development of intelligent transportation system.
Keyword:Deep reinforcement learning Autonomous driving decision making Deep Q network
目 录
1 引言 1
2 深度强化学习理论基础 1
2.1 强化学习基本原理与算法框架 1
2.2 深度神经网络在强化学习中的应用 2
2.3 自动驾驶场景下的状态空间建模 2
2.4 奖励函数设计与优化策略 3
3 基于深度强化学习的决策系统设计 3
3.1 自动驾驶决策系统架构设计 3
3.2 多模态感知信息融合方法 4
3.3 复杂交通场景下的决策策略优化 4
3.4 安全性与鲁棒性保障机制 5
4 实验验证与性能评估 6
4.1 仿真环境构建与测试平台搭建 6
4.2 典型场景下的算法性能分析 6
4.3 与传统方法的对比实验研究 7
4.4 实际道路测试结果与分析 7
5 结论 8
参考文献 9
致谢 10
