Computer Vision for Autonomous Driving

Engineering notes

Key topics: autonomous driving, self-driving vehicle, self-driving, autonomous vehicle, lane detection, depth estimation, semantic segmentation, object detection, sensor fusion, deployment, perception, prediction. See the paper for implementation details and experimental results.

Chinese explanation / 中文解读

中文解读待补充：本站会优先为端到端自动驾驶、BEV感知、3D目标检测、轨迹预测、路径规划、LiDAR感知等高价值论文补充中文说明。

Original abstract

Autonomous driving has emerged as one of the most transformative innovations in intelligent transportation systems. Modern self-driving vehicles rely heavily on computer vision techniques to understand, interpret, and react to their surroundings in real time. The complexity of the driving environment characterized by dynamic obstacles, traffic signs, lane markings, pedestrians, and varying lighting conditions demands robust perception systems. Computer vision enables autonomous vehicles to detect objects, segment scenes, estimate depth, track motion, and make high-accuracy decisions essential for safe navigation. This paper presents a comprehensive overview of the essential computer vision components used in autonomous driving, including camera systems, sensor fusion, object detection, lane detection, traffic sign recognition, and depth estimation. It also discusses commonly used datasets, algorithms, and deep learning models such as CNNs, YOLO, SSD, HOG, SVM, RANSAC, Canny, and semantic segmentation networks. The work synthesizes how these modules collectively support perception, prediction, and planning stages in autonomous vehicles. Expanded sections detail the design, workflow, implementation considerations, and practical challenges encountered in real-world deployments. The discussion illustrates the critical role of computer vision in enabling vehicles to operate safely and efficiently without human intervention.

Links and sources

• Official / arXiv page

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