Lane Reconstruction for Self-Driving Vehicles on Dynamic Road Networks

Abstract

Lane detection is a crucial task that involves identifying lane lines and proper markings on road structures. The field of lane detection has gained significant attention due to the increasing use of autonomous vehicles and ADAS systems, as they require robust lane detection systems to navigate safely in complex environments. While previous studies have focused on lane detection in various scenarios, lane line reconstruction for dynamic road networks has been largely overlooked. To address this gap, we propose a novel approach to reconstruct lane lines for dynamic road networks using roadblocks, specifically red and white striped markers, as cues. To evaluate the effectiveness of our proposed approach, we first built and labeled our own dataset by extracting 1854 frames from recorded videos. We trained a Faster-RCNN V2 model on 80% of the dataset to detect the roadblocks and used the OpenCV DNN module, along with a range of techniques, such as ROI selection, color selection, region masking, and polynomial and linear fittings for lane detection and reconstruction. Our approach successfully reconstructed the left, middle, and right lane lines, achieving an overall accuracy of 68%, a recall of 73%, and a precision score of 73% on an unseen set of 50 images. Our study contributes to the field by proposing a novel approach for reconstructing lane lines in dynamic road networks using roadblocks as markers. This approach has the potential to enhance the accuracy and robustness of lane detection systems in complex environments. Furthermore, our work addresses a gap in the existing literature and provides insights for future research in this area.