摘 要
随着智能交通系统的快速发展，计算机视觉技术在自动驾驶领域的重要性日益凸显。本研究旨在探讨计算机视觉技术在自动驾驶中的应用现状、关键挑战及未来发展方向，并提出一种基于深度学习的多任务感知框架以提升环境理解能力。通过分析目标检测、语义分割和行为预测等核心算法，结合实际道路场景数据集进行实验验证，结果表明该框架能够显著提高复杂场景下的感知精度与实时性。此外，研究还深入探讨了光照变化、恶劣天气及遮挡等常见问题对视觉系统性能的影响，并提出了相应的优化策略。主要创新点在于将多任务学习与跨模态融合相结合，有效提升了模型的鲁棒性和泛化能力。研究表明，尽管计算机视觉技术在自动驾驶中取得了显著进展，但仍然面临数据标注成本高、计算资源需求大以及安全性保障不足等问题。未来的研究应进一步探索轻量化模型设计、无监督学习方法以及人机协同决策机制，以推动自动驾驶技术向更高效、更安全的方向发展。
关键词：计算机视觉；自动驾驶；多任务感知框架；深度学习；环境理解能力
Abstract
With the rapid development of intelligent transportation systems, the importance of computer vision technology in the field of autonomous driving has become increasingly prominent. This study investigates the current applications, key challenges, and future directions of computer vision technology in autonomous driving, proposing a deep-learning-based multi-task perception fr amework to enhance environmental understanding capabilities. By analyzing core algorithms such as ob ject detection, semantic segmentation, and behavior prediction, and validating them through experiments on real-world road scene datasets, the results demonstrate that the fr amework significantly improves the accuracy and real-time performance of perception in complex scenarios. Furthermore, this research delves into the impact of common issues like illumination changes, adverse weather conditions, and occlusions on the performance of vision systems, presenting corresponding optimization strategies. The primary innovation lies in combining multi-task learning with cross-modal fusion, effectively enhancing the robustness and generalization ability of the model. The study reveals that, despite significant advancements in computer vision for autonomous driving, challenges remain, including high data annotation costs, substantial computational resource requirements, and insufficient safety guarantees. Future research should focus on exploring lightweight model designs, unsupervised learning methods, and human-machine collaborative decision-making mechanisms to promote the development of autonomous driving technology toward greater efficiency and safety.
Keywords: Computer Vision；Autonomous Driving；Multi-Task Perception fr amework；Deep Learning；Environmental Understanding Capability
目 录
摘 要 I
Abstract II
一、绪论 1
（一）计算机视觉与自动驾驶的背景意义 1
（二）国内外研究现状分析 1
（三）本文研究方法与技术路线 1
二、计算机视觉在自动驾驶中的关键技术 2
（一）图像识别与目标检测技术 2
（二）深度学习在场景理解中的应用 3
（三）实时数据处理与优化算法 3
三、自动驾驶中计算机视觉的应用场景 4
（一）车道线检测与路径规划 4
（二）行人与障碍物识别分析 4
（三）天气与光照条件下的视觉适应 5
四、计算机视觉在自动驾驶中的挑战与应对 5
（一）数据标注与模型训练的难点 6
（二）算法鲁棒性与安全性问题 6
（三）技术伦理与法规约束 7
结 论 7
致 谢 9
参考文献 10
