DeepLanes: End-To-End Lane Position Estimation Using Deep Neural Networks

Alexandru Gurghian, Tejaswi Koduri, Smita V. Bailur, Kyle J. Carey, Vidya N. Murali
2016 2016 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)  
Camera-based lane detection algorithms are one of the key enablers for many semi-autonomous and fullyautonomous systems, ranging from lane keep assist to level-5 automated vehicles. Positioning a vehicle between lane boundaries is the core navigational aspect of a self-driving car. Even though this should be trivial, given the clarity of lane markings on most standard roadway systems, the process is typically mired with tedious pre-processing and computational effort. We present an approach to
more » ... ent an approach to estimate lane positions directly using a deep neural network that operates on images from laterally-mounted down-facing cameras. To create a diverse training set, we present a method to generate semi-artificial images. Besides the ability to distinguish whether there is a lane-marker present or not, the network is able to estimate the position of a lane marker with sub-centimeter accuracy at an average of 100 frames/s on an embedded automotive platform, requiring no pre-or post-processing. This system can be used not only to estimate lane position for navigation, but also provide an efficient way to validate the robustness of driver-assist features which depend on lane information.
doi:10.1109/cvprw.2016.12 dblp:conf/cvpr/GurghianKBCM16 fatcat:6w5fn72rr5bbxpaedxjnr4ssyq