摘要
智能驾驶辅助系统作为现代交通领域的重要研究方向，旨在提升车辆行驶的安全性和效率。本文聚焦于路径规划与决策算法这一关键技术环节，针对复杂多变的交通环境，提出了一种融合深度强化学习与传统搜索算法的混合框架。该方法通过构建多层次场景模型，实现了对动态交通要素的有效感知与预测，克服了现有技术在应对非结构化道路及突发状况时的局限性。实验结果表明，所提出的算法能够在保证计算效率的前提下，显著提高路径规划的成功率和鲁棒性，特别是在高密度交通流环境中表现优异。此外，本研究还引入了基于风险评估的决策机制，为车辆提供了更加智能化、人性化的操作建议。通过对真实路况数据集的大规模测试验证，证明了该算法不仅能够有效降低碰撞风险，还能优化行程时间，为智能驾驶系统的实际应用奠定了坚实基础。本研究的主要创新点在于将深度学习与经典算法有机结合，提出了适应性强且可解释性高的解决方案，为未来智能交通系统的发展提供了新的思路和技术支撑。
关键词：智能驾驶辅助系统；路径规划与决策算法；深度强化学习
Abstract
Intelligent driving assistance systems, as a critical research direction in modern transportation, aim to enhance the safety and efficiency of vehicle operations. This study focuses on path planning and decision-making algorithms, proposing a hybrid fr amework that integrates deep reinforcement learning with traditional search algorithms to address the complexities of dynamic traffic environments. By constructing multi-level scene models, this method achieves effective perception and prediction of dynamic traffic elements, overcoming limitations of existing technologies in handling unstructured roads and unexpected situations. Experimental results demonstrate that the proposed algorithm significantly improves path planning success rates and robustness while maintaining computational efficiency, particularly excelling in high-density traffic flow environments. Additionally, a risk assessment-based decision-making mechanism is introduced, providing more intelligent and human-centric operational recommendations for vehicles. Large-scale testing using real-world traffic datasets verifies that this algorithm not only effectively reduces collision risks but also optimizes travel time, laying a solid foundation for the practical application of intelligent driving systems. The primary innovation of this research lies in the organic combination of deep learning with classical algorithms, offering a highly adaptable and interpretable solution that provides new perspectives and technical support for the development of future intelligent transportation systems.
Keywords：Intelligent Driving Assistance System; Path Planning And Decision-making Algorithm; Deep Reinforcement Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 智能驾驶辅助系统发展背景 1
(二) 路径规划与决策算法的意义 1
(三) 国内外研究现状综述 1
(四) 本文研究方法与创新点 2
二、路径规划算法基础理论 2
(一) 几何路径规划方法分析 2
(二) 拓扑路径规划技术应用 3
(三) 动态环境下的路径优化 3
(四) 多目标路径规划策略 4
三、决策算法关键技术 5
(一) 驾驶行为建模方法 5
(二) 环境感知与风险评估 5
(三) 实时决策机制设计 6
(四) 复杂场景应对策略 7
四、算法集成与验证 7
(一) 系统架构设计原则 7
(二) 测试平台搭建方案 8
(三) 实验结果分析评价 9
(四) 应用前景展望 9
结 论 11
参考文献 12
智能驾驶辅助系统作为现代交通领域的重要研究方向，旨在提升车辆行驶的安全性和效率。本文聚焦于路径规划与决策算法这一关键技术环节，针对复杂多变的交通环境，提出了一种融合深度强化学习与传统搜索算法的混合框架。该方法通过构建多层次场景模型，实现了对动态交通要素的有效感知与预测，克服了现有技术在应对非结构化道路及突发状况时的局限性。实验结果表明，所提出的算法能够在保证计算效率的前提下，显著提高路径规划的成功率和鲁棒性，特别是在高密度交通流环境中表现优异。此外，本研究还引入了基于风险评估的决策机制，为车辆提供了更加智能化、人性化的操作建议。通过对真实路况数据集的大规模测试验证，证明了该算法不仅能够有效降低碰撞风险，还能优化行程时间，为智能驾驶系统的实际应用奠定了坚实基础。本研究的主要创新点在于将深度学习与经典算法有机结合，提出了适应性强且可解释性高的解决方案，为未来智能交通系统的发展提供了新的思路和技术支撑。
关键词：智能驾驶辅助系统；路径规划与决策算法；深度强化学习
Abstract
Intelligent driving assistance systems, as a critical research direction in modern transportation, aim to enhance the safety and efficiency of vehicle operations. This study focuses on path planning and decision-making algorithms, proposing a hybrid fr amework that integrates deep reinforcement learning with traditional search algorithms to address the complexities of dynamic traffic environments. By constructing multi-level scene models, this method achieves effective perception and prediction of dynamic traffic elements, overcoming limitations of existing technologies in handling unstructured roads and unexpected situations. Experimental results demonstrate that the proposed algorithm significantly improves path planning success rates and robustness while maintaining computational efficiency, particularly excelling in high-density traffic flow environments. Additionally, a risk assessment-based decision-making mechanism is introduced, providing more intelligent and human-centric operational recommendations for vehicles. Large-scale testing using real-world traffic datasets verifies that this algorithm not only effectively reduces collision risks but also optimizes travel time, laying a solid foundation for the practical application of intelligent driving systems. The primary innovation of this research lies in the organic combination of deep learning with classical algorithms, offering a highly adaptable and interpretable solution that provides new perspectives and technical support for the development of future intelligent transportation systems.
Keywords：Intelligent Driving Assistance System; Path Planning And Decision-making Algorithm; Deep Reinforcement Learning
目 录
摘要 I
Abstract II
一、绪论 1
(一) 智能驾驶辅助系统发展背景 1
(二) 路径规划与决策算法的意义 1
(三) 国内外研究现状综述 1
(四) 本文研究方法与创新点 2
二、路径规划算法基础理论 2
(一) 几何路径规划方法分析 2
(二) 拓扑路径规划技术应用 3
(三) 动态环境下的路径优化 3
(四) 多目标路径规划策略 4
三、决策算法关键技术 5
(一) 驾驶行为建模方法 5
(二) 环境感知与风险评估 5
(三) 实时决策机制设计 6
(四) 复杂场景应对策略 7
四、算法集成与验证 7
(一) 系统架构设计原则 7
(二) 测试平台搭建方案 8
(三) 实验结果分析评价 9
(四) 应用前景展望 9
结 论 11
参考文献 12
