摘要
随着智能交通系统的发展，自动驾驶汽车成为现代交通领域的重要研究方向，环境感知系统作为其核心组成部分，对实现安全可靠的自动驾驶至关重要。本文聚焦于自动驾驶汽车环境感知系统的关键技术探索，旨在解决复杂环境下车辆对周围环境的精准识别与理解问题。通过对现有传感器技术（如激光雷达、毫米波雷达、摄像头等）的性能分析，结合多源信息融合算法，提出一种基于深度学习的多模态感知框架，该框架能够有效提升目标检测精度和鲁棒性。实验结果表明，在不同天气条件和光照环境下，所提出的感知系统能准确识别道路参与者并提供实时可靠的环境信息，相较于传统方法，检测准确率提高了15%，误检率降低了20%。此外，本文创新性地引入了场景语义理解模块，实现了从单纯的目标检测到场景认知的跨越，为后续路径规划和决策控制提供了更丰富的信息支持。研究结果不仅验证了所提方法的有效性，还为未来自动驾驶环境感知系统的优化设计提供了理论依据和技术参考，推动了自动驾驶技术向更高水平发展。
关键词：自动驾驶汽车；环境感知系统；多模态感知框架
Abstract
With the development of intelligent transportation systems, autonomous driving vehicles have become a crucial research direction in modern transportation. As a core component, the environmental perception system plays an essential role in achieving safe and reliable autonomous driving. This paper focuses on exploring key technologies of the environmental perception system for autonomous vehicles, aiming to address the challenge of accurate recognition and understanding of the surrounding environment in complex conditions. By analyzing the performance of existing sensor technologies such as LiDAR, millimeter-wave radar, and cameras, and integrating multi-source information fusion algorithms, we propose a deep learning-based multimodal perception fr amework that significantly enhances target detection accuracy and robustness. Experimental results demonstrate that under various weather and lighting conditions, the proposed perception system can accurately identify road participants and provide real-time and reliable environmental information, achieving a 15% improvement in detection accuracy and a 20% reduction in false detection rate compared to traditional methods. Additionally, this study innovatively introduces a scene semantic understanding module, enabling a leap from mere ob ject detection to scene cognition, thereby providing richer information support for subsequent path planning and decision-making control. The findings not only validate the effectiveness of the proposed method but also offer theoretical foundations and technical references for optimizing the design of future autonomous driving environmental perception systems, promoting the advancement of autonomous driving technology to a higher level.
Keywords：Autonomous Vehicle; Environmental Perception System; Multimodal Perception fr amework
目 录
摘要 I
Abstract II
一、绪论 1
(一) 自动驾驶环境感知的研究背景 1
(二) 环境感知系统的关键意义 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、环境感知传感器技术 2
(一) 视觉传感器的应用分析 2
(二) 激光雷达的技术特点 3
(三) 毫米波雷达的性能评估 3
(四) 多传感器融合方案 4
三、环境信息处理算法 5
(一) 目标检测与识别技术 5
(二) 路径规划与决策机制 5
(三) 数据融合与校准方法 6
(四) 实时性与鲁棒性优化 7
四、系统集成与测试验证 7
(一) 环境感知系统的架构设计 7
(二) 测试场景与评价指标 8
(三) 实车测试结果分析 8
(四) 系统可靠性提升策略 9
结 论 11
参考文献 12
随着智能交通系统的发展，自动驾驶汽车成为现代交通领域的重要研究方向，环境感知系统作为其核心组成部分，对实现安全可靠的自动驾驶至关重要。本文聚焦于自动驾驶汽车环境感知系统的关键技术探索，旨在解决复杂环境下车辆对周围环境的精准识别与理解问题。通过对现有传感器技术（如激光雷达、毫米波雷达、摄像头等）的性能分析，结合多源信息融合算法，提出一种基于深度学习的多模态感知框架，该框架能够有效提升目标检测精度和鲁棒性。实验结果表明，在不同天气条件和光照环境下，所提出的感知系统能准确识别道路参与者并提供实时可靠的环境信息，相较于传统方法，检测准确率提高了15%，误检率降低了20%。此外，本文创新性地引入了场景语义理解模块，实现了从单纯的目标检测到场景认知的跨越，为后续路径规划和决策控制提供了更丰富的信息支持。研究结果不仅验证了所提方法的有效性，还为未来自动驾驶环境感知系统的优化设计提供了理论依据和技术参考，推动了自动驾驶技术向更高水平发展。
关键词：自动驾驶汽车；环境感知系统；多模态感知框架
Abstract
With the development of intelligent transportation systems, autonomous driving vehicles have become a crucial research direction in modern transportation. As a core component, the environmental perception system plays an essential role in achieving safe and reliable autonomous driving. This paper focuses on exploring key technologies of the environmental perception system for autonomous vehicles, aiming to address the challenge of accurate recognition and understanding of the surrounding environment in complex conditions. By analyzing the performance of existing sensor technologies such as LiDAR, millimeter-wave radar, and cameras, and integrating multi-source information fusion algorithms, we propose a deep learning-based multimodal perception fr amework that significantly enhances target detection accuracy and robustness. Experimental results demonstrate that under various weather and lighting conditions, the proposed perception system can accurately identify road participants and provide real-time and reliable environmental information, achieving a 15% improvement in detection accuracy and a 20% reduction in false detection rate compared to traditional methods. Additionally, this study innovatively introduces a scene semantic understanding module, enabling a leap from mere ob ject detection to scene cognition, thereby providing richer information support for subsequent path planning and decision-making control. The findings not only validate the effectiveness of the proposed method but also offer theoretical foundations and technical references for optimizing the design of future autonomous driving environmental perception systems, promoting the advancement of autonomous driving technology to a higher level.
Keywords：Autonomous Vehicle; Environmental Perception System; Multimodal Perception fr amework
目 录
摘要 I
Abstract II
一、绪论 1
(一) 自动驾驶环境感知的研究背景 1
(二) 环境感知系统的关键意义 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、环境感知传感器技术 2
(一) 视觉传感器的应用分析 2
(二) 激光雷达的技术特点 3
(三) 毫米波雷达的性能评估 3
(四) 多传感器融合方案 4
三、环境信息处理算法 5
(一) 目标检测与识别技术 5
(二) 路径规划与决策机制 5
(三) 数据融合与校准方法 6
(四) 实时性与鲁棒性优化 7
四、系统集成与测试验证 7
(一) 环境感知系统的架构设计 7
(二) 测试场景与评价指标 8
(三) 实车测试结果分析 8
(四) 系统可靠性提升策略 9
结 论 11
参考文献 12
