摘 要
随着自动驾驶技术的快速发展，车辆决策系统面临着复杂多变的道路环境挑战。本研究聚焦于增强学习在自动驾驶决策中的应用，旨在通过智能算法提升车辆在动态交通环境中的自主决策能力。研究采用深度强化学习框架，结合卷积神经网络与策略梯度方法，构建了适用于城市道路场景的决策模型。该模型能够在模拟环境中实时处理传感器数据，根据交通状况、行人行为等因素做出最优行驶决策。实验结果表明，所提出的增强学习算法能够有效提高车辆应对突发情况的能力，在复杂路况下的决策准确率达到95%以上。与传统基于规则的方法相比，该模型展现出更强的适应性和鲁棒性，特别是在非结构化道路环境中表现尤为突出。创新点在于引入了多模态感知融合机制，实现了视觉、雷达等多源信息的有效整合；同时设计了分层强化学习架构，将全局路径规划与局部避障控制有机结合，显著提升了系统的整体性能。研究表明，增强学习为解决自动驾驶决策问题提供了新的思路和方法，具有重要的理论意义和实际应用价值。
关键词：自动驾驶决策 增强学习 深度强化学习
Abstract
With the rapid development of autonomous driving technology, vehicle decision-making systems face challenges from increasingly complex and dynamic road environments. This study focuses on the application of reinforcement learning in autonomous driving decision-making, aiming to enhance vehicles' autonomous decision-making capabilities in dynamic traffic environments through intelligent algorithms. The research adopts a deep reinforcement learning fr amework, integrating convolutional neural networks with policy gradient methods, to construct a decision-making model suitable for urban road scenarios. This model can process sensor data in real-time within a simulated environment and make optimal driving decisions based on factors such as traffic conditions and pedestrian behavior. Experimental results demonstrate that the proposed reinforcement learning algorithm effectively improves the vehicle's ability to respond to emergencies, achieving a decision accuracy rate of over 95% in complex road conditions. Compared with traditional rule-based methods, this model exhibits stronger adaptability and robustness, especially in unstructured road environments. Innovations include the introduction of a multimodal perception fusion mechanism, which achieves effective integration of multi-source information such as visual and radar data, and the design of a hierarchical reinforcement learning architecture that organically combines global path planning with local obstacle avoidance control, significantly enhancing the overall performance of the system. The study indicates that reinforcement learning provides new approaches and methods for solving autonomous driving decision-making problems, possessing significant theoretical importance and practical application value.
Keyword:Autonomous Driving Decision Enhanced Learning Deep Reinforcement Learning
目 录
引言 1
1增强学习基础理论与自动驾驶 1
1.1增强学习基本原理 1
1.2自动驾驶决策需求分析 2
1.3增强学习在自动驾驶中的适配性 2
2环境感知与状态表示 3
2.1感知数据获取与处理 3
2.2状态空间定义方法 4
2.3动态环境建模技术 4
3决策模型构建与优化 5
3.1强化学习算法选择 5
3.2奖励函数设计原则 5
3.3模型训练与参数调优 6
4实际应用与挑战应对 6
4.1场景模拟与测试 6
4.2安全性保障机制 7
4.3技术发展趋势展望 7
结论 8
参考文献 10
致谢 11
