Autonomous lane keeping system is the key technique to autonomous driving. It includes lane detection, lane tracking and control. It has been developed enormously, but it is still a challenge work due to different factors such as illumination, general hyper-parameters setting for different road condition and lane boundary correction. In addition, due to imbalance on accuracy and processing time, it is hard to conduct in embedding system. In this study, an autonomous lane keeping system is developed based on deep learning. First, a lane detection and tracking system is designed, which is robust to lane boundary correction. Especially for lane detection, a light-weight network named as LaneFCNet is proposed, which can balance accuracy and processing time. Then, lane tracking was followed by detector to improve the detection performance and create autonomous driving trajectory. Finally, to brief lane fitting problem, it was treated as ridge regression problem, which can enhance the effectiveness to the whole system. Experimental results show that our integrated lane detection and tracking system can trade off accuracy and processing time and the whole line keeping system is robust enough to autonomous driving.

中文翻译：

---

自主车道保持系统：嵌入式系统上的车道检测、跟踪和控制

自动车道保持系统是自动驾驶的关键技术。它包括车道检测、车道跟踪和控制。它已经得到了巨大的发展，但由于光照、不同路况的通用超参数设置和车道边界校正等不同因素，它仍然是一项具有挑战性的工作。另外，由于精度和处理时间的不平衡，很难在嵌入系统中进行。在本研究中，基于深度学习开发了自主车道保持系统。首先，设计了一个车道检测和跟踪系统，该系统对车道边界校正具有鲁棒性。特别是对于车道检测，提出了一种名为 LaneFCNet 的轻量级网络，可以平衡精度和处理时间。然后，车道跟踪之后是检测器，以提高检测性能并创建自动驾驶轨迹。最后，将车道拟合问题简单化为岭回归问题，可以提高整个系统的有效性。实验结果表明，我们的集成车道检测和跟踪系统可以在准确性和处理时间之间进行权衡，并且整个线路保持系统对于自动驾驶来说足够健壮。