Lane Detection Using U-Net With Spatiotemporal Feature Extraction for Improved Road Segmentation

Engineering notes

Key topics: autonomous driving, lane detection, adas, deployment. 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

This paper aims to develop and evaluate a robust lane detection system for Advanced Driver Assistance Systems (ADAS) using deep learning techniques. While advanced deep learning models offer excellent detection accuracy, their overwhelming requests on computational resources, energy, and memory often ruin their deployment on embedded platforms. In our work, a unique approach was utilized to generate a comprehensive dataset that not only captures ideal conditions but also incorporates challenging scenarios with wrong detections. The network was trained on this diverse dataset, which improved its robustness and resulted in high accuracy for lane marking detection. The outcomes of this study offer a promising path toward more dependable and effective lane detection systems that can be integrated into real-time automotive and innovative infrastructure applications, ultimately contributing to improved road safety in autonomous driving technologies.

Links and sources

• Official / arXiv page

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