A Deep Learning Approach for Lane Detection
State-of-the-art lane detection methods use a variety of deep learning techniques for lane feature extraction and prediction, demonstrating better performance than conventional lane detectors. However, deep learning approaches are computationally demanding and often fail to meet real-time requirements of autonomous vehicles. In this work, we proposed and implemented a lane detection method using a light-weight convolutional neural network model as a feature extractor exploiting the potential of deep learning while meeting real-time needs. The developed model is trained with a dataset containing small image patches of dimension 16*64 pixels and a non-overlapping sliding window approach is employed to achieve fast inference. Then, the predictions are clustered and fitted with a polynomial to model the lane boundaries. The proposed method was tested on the KITTI and Caltech datasets and demonstrated an acceptable performance. We also integrated the detector into the localization and planning system of our autonomous vehicle and runs at 28 fps in a CPU on image resolution of 768*1024 meeting real-time requirements needed for self-driving cars.
This work is done by Tesfamchael Getahun, Ali Karimoddini, and Priyantha Mudalige, and presented at 2021 IEEE International Intelligent Transportation Systems Conference (ITSC), Indianapolis, IN, USA, September 19-22, 2021.