부산대학교 도서관

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 diff erent factors such as illumination, general hyper-parameters setting for diff erent 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 fi tting problem, it was treated as ridge regression problem, which can enhance the eff ectiveness 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.