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若干关键技术展开深入研究，旨在解决复杂环境下车辆对外界信息获取的准确性、实时性和鲁棒性问题。针对现有技术在多传感器融合、目标检测与识别方面存在的不足，提出了一种基于深度学习框架下的多源异构数据融合算法，通过构建轻量级卷积神经网络模型，实现了对动态交通场景中不同类型目标的高效检测与分类，同时引入时空上下文关联机制以增强感知结果的一致性。实验结果表明，所提方法能够在多种典型工况下有效提升目标检测精度约15%，并将系统响应时间缩短至20毫秒以内，显著改善了系统的整体性能。此外，本研究还探索了恶劣天气条件及非结构化道路环境下的适应性解决方案，为提高自动驾驶汽车环境感知能力提供了新的思路和技术手段，对于推动智能网联汽车技术进步具有重要意义。
关键词：自动驾驶汽车；环境感知系统；多源异构数据融合
Abstract
With the development of intelligent transportation systems, autonomous driving vehicles have become a significant research direction in modern transportation. As a core component, the environmental perception system is crucial for achieving safe and reliable autonomous driving. This paper focuses on several key technologies of the environmental perception system for autonomous driving vehicles, aiming to address the accuracy, real-time performance, and robustness of information acquisition from the external environment in complex conditions. In response to the inadequacies of existing technologies in multi-sensor fusion and ob ject detection and recognition, this study proposes a multi-source heterogeneous data fusion algorithm based on a deep learning fr amework. By constructing a lightweight convolutional neural network model, this algorithm achieves efficient detection and classification of different types of ob jects in dynamic traffic scenes. Additionally, a spatiotemporal context association mechanism is introduced to enhance the consistency of perception results. Experimental results demonstrate that the proposed method can improve ob ject detection accuracy by approximately 15% under various typical operating conditions and reduce system response time to within 20 milliseconds, significantly enhancing overall system performance. Furthermore, this research explores adaptive solutions for adverse weather conditions and unstructured road environments, providing new ideas and technical means to improve the environmental perception capabilities of autonomous driving vehicles, which is of great significance for advancing the technology of intelligent connected vehicles.
Keywords：Autonomous Vehicle; Environmental Perception System; Multi-source Heterogeneous Data Fusion
目 录
摘要 I
Abstract II
一、绪论 1
(一) 自动驾驶汽车环境感知研究背景 1
(二) 环境感知系统的研究意义 1
(三) 国内外研究现状综述 1
(四) 本文研究方法与技术路线 2
二、环境感知传感器技术 2
(一) 视觉传感器原理与应用 2
(二) 激光雷达技术分析 3
(三) 毫米波雷达工作特性 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) 路面特征提取方法 5
(三) 环境建模与地图构建 6
(四) 实时性优化策略 7
四、系统集成与测试验证 7
(一) 环境感知系统架构设计 7
(二) 测试场景与评价指标 8
(三) 实车测试结果分析 9
(四) 系统可靠性评估 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若干关键技术展开深入研究，旨在解决复杂环境下车辆对外界信息获取的准确性、实时性和鲁棒性问题。针对现有技术在多传感器融合、目标检测与识别方面存在的不足，提出了一种基于深度学习框架下的多源异构数据融合算法，通过构建轻量级卷积神经网络模型，实现了对动态交通场景中不同类型目标的高效检测与分类，同时引入时空上下文关联机制以增强感知结果的一致性。实验结果表明，所提方法能够在多种典型工况下有效提升目标检测精度约15%，并将系统响应时间缩短至20毫秒以内，显著改善了系统的整体性能。此外，本研究还探索了恶劣天气条件及非结构化道路环境下的适应性解决方案，为提高自动驾驶汽车环境感知能力提供了新的思路和技术手段，对于推动智能网联汽车技术进步具有重要意义。
关键词：自动驾驶汽车；环境感知系统；多源异构数据融合
Abstract
With the development of intelligent transportation systems, autonomous driving vehicles have become a significant research direction in modern transportation. As a core component, the environmental perception system is crucial for achieving safe and reliable autonomous driving. This paper focuses on several key technologies of the environmental perception system for autonomous driving vehicles, aiming to address the accuracy, real-time performance, and robustness of information acquisition from the external environment in complex conditions. In response to the inadequacies of existing technologies in multi-sensor fusion and ob ject detection and recognition, this study proposes a multi-source heterogeneous data fusion algorithm based on a deep learning fr amework. By constructing a lightweight convolutional neural network model, this algorithm achieves efficient detection and classification of different types of ob jects in dynamic traffic scenes. Additionally, a spatiotemporal context association mechanism is introduced to enhance the consistency of perception results. Experimental results demonstrate that the proposed method can improve ob ject detection accuracy by approximately 15% under various typical operating conditions and reduce system response time to within 20 milliseconds, significantly enhancing overall system performance. Furthermore, this research explores adaptive solutions for adverse weather conditions and unstructured road environments, providing new ideas and technical means to improve the environmental perception capabilities of autonomous driving vehicles, which is of great significance for advancing the technology of intelligent connected vehicles.
Keywords：Autonomous Vehicle; Environmental Perception System; Multi-source Heterogeneous Data Fusion
目 录
摘要 I
Abstract II
一、绪论 1
(一) 自动驾驶汽车环境感知研究背景 1
(二) 环境感知系统的研究意义 1
(三) 国内外研究现状综述 1
(四) 本文研究方法与技术路线 2
二、环境感知传感器技术 2
(一) 视觉传感器原理与应用 2
(二) 激光雷达技术分析 3
(三) 毫米波雷达工作特性 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) 路面特征提取方法 5
(三) 环境建模与地图构建 6
(四) 实时性优化策略 7
四、系统集成与测试验证 7
(一) 环境感知系统架构设计 7
(二) 测试场景与评价指标 8
(三) 实车测试结果分析 9
(四) 系统可靠性评估 9
结 论 11
参考文献 12
