计算机视觉在自动驾驶中的应用 -计算机科学与技术专业

摘要

随着智能交通系统的发展，自动驾驶技术成为当前研究热点，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，以提高车辆行驶的安全性、可靠性及智能化水平为目的。通过融合多种传感器数据，采用深度学习算法对道路环境进行感知，包括目标检测与识别、车道线检测、交通标志和信号灯识别等任务，实现了复杂场景下的精准环境理解。在此基础上构建了基于端到端学习的决策控制系统，创新性地将视觉特征直接映射到车辆控制指令，减少了中间环节带来的误差。实验结果表明，该方法能够有效应对不同天气条件和光照变化下的驾驶场景，相比传统方法具有更高的准确率和鲁棒性。此外，还提出了一种新的数据增强策略，利用生成对抗网络合成更多样化的训练样本，进一步提升了模型的泛化能力。本研究为实现高级别自动驾驶提供了重要的理论依据和技术支持，推动了计算机视觉与自动驾驶领域的融合发展。

关键词：自动驾驶计算机视觉深度学习

Abstract

With the development of intelligent transportation systems, autonomous driving technology has become a current research hotspot, and computer vision, as one of its core technologies, plays an irreplaceable role. This study aims to explore the application of computer vision in autonomous driving to enhance the safety, reliability, and intelligence level of vehicle operation. By integrating data from multiple sensors and employing deep learning algorithms, this research achieves precise environmental perception of road conditions, including ob ject detection and recognition, lane line detection, traffic sign, and signal light recognition, thereby realizing accurate environmental understanding in complex scenarios. On this foundation, an end-to-end learning-based decision control system is constructed, innovatively mapping visual features directly to vehicle control commands, which reduces errors brought by intermediate links. The experimental results show that this method can effectively cope with driving scenarios under different weather conditions and lighting changes, exhibiting higher accuracy and robustness compared to traditional methods. Moreover, a new data augmentation strategy is proposed, utilizing generative adversarial networks to synthesize more diverse training samples, further improving the generalization ability of the model. This research provides important theoretical basis and technical support for achieving high-level autonomous driving and promotes the integrated development of computer vision and autonomous driving fields.

Keyword:Autonomous Driving Computer Vision Deep Learning

1绪论 1

1.1计算机视觉与自动驾驶的背景 1

1.2国内外研究现状综述 1

1.3本文研究方法概述 2

2环境感知技术 2

2.1视觉传感器选型分析 2

2.2障碍物检测算法研究 3

2.3车道线识别技术应用 3

3行为决策机制 4

3.1视觉信息融合处理 4

3.2驾驶场景理解分析 5