Autonomous Lane Keeping Control System Based on Road Lane Model Using Deep Convolutional Neural Networks *

In this paper, we propose a novel autonomous lane keeping system (LKS) based on a road lane model using a deep convolutional neural network (DCNN). The DCNN was trained by a dataset which consists of reliable road coefficients from the vision system mounted on the test vehicle driven by a human, and images captured by another camera. Then the proposed system was validated with a dataset which was not used for training the DCNN. We confirmed that there were good agreements between the steering wheel angles by the human driver and those given by the proposed LKS. Furthermore, we observed that the proposed system can provide the road coefficients necessary for implementing the LKS even either when there is no lane marker and/or when the vehicle is maneuvered for turning at an intersection.

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