摘 要
随着智能交通系统的发展，自动驾驶技术成为研究热点，计算机视觉作为其核心技术之一发挥着不可替代的作用。本研究旨在探讨计算机视觉在自动驾驶中的应用与挑战，以提升自动驾驶的安全性和可靠性为目的。通过分析计算机视觉算法，如目标检测、识别与跟踪等，在环境感知方面的方法，发现其能够精准获取道路状况、车辆行人信息等。基于深度学习的卷积神经网络模型被广泛应用，可实现对复杂场景的理解。结果表明，计算机视觉能有效提高自动驾驶汽车对周围环境的认知能力，但同时也存在诸多挑战，例如恶劣天气下视觉信息获取困难、数据标注成本高且易出错、算法实时性难以满足需求等问题。
关键词：自动驾驶 计算机视觉 环境感知
Abstract
With the development of intelligent transportation system, autonomous driving technology has become a research hotspot, and computer vision, as one of its core technologies, plays an irreplaceable role. This study aims to explore the application and challenges of computer vision in autonomous driving to improve the safety and reliability of autonomous driving. Through the analysis of computer vision algorithms, such as target detection, identification and tracking, and the methods in environmental perception, it is found that it can accurately obtain road conditions, vehicle and pedestrian information, etc. Deep learning-based convolutional neural network models are widely used to realize the understanding of complex scenarios. The results show that computer vision can effectively improve the cognitive ability of autonomous vehicles to the surrounding environment, but there are also many challenges, such as obtaining visual information in bad weather, high data annotation cost and error prone, and the real-time algorithm.
目 录
1引言 1
2计算机视觉在环境感知中的应用 1
2.1 环境感知的重要性 1
2.2 视觉传感器的选择与优化 2
2.3 复杂环境下的感知挑战 2
3目标检测与识别技术 3
3.1 目标检测算法综述 3
3.2 动态目标的实时识别 3
3.3 特殊场景下的目标识别 4
4路径规划与决策支持 4
4.1 可行驶区域的视觉判定 4
4.2 基于视觉的路径规划方法 5
4.3 决策系统的视觉信息融合 5
5安全性与可靠性保障 6
5.1 视觉系统的容错机制 6
5.2 恶劣天气条件下的性能 7
5.3 系统安全性的评估标准 7
6结论 8
参考文献 9
致谢 10
