Multi-Lane Detection System Based on Segmentation Model for Autonomous Vehicles

Nowadays, autonomous driving has recently started to attract more attention; therefore, numerous automotive companies are developing various techniques for semi-autonomous to fully-autonomous driving systems. Among these techniques, multi-lane detection is one of the critical elements of an autonomous vehicle's decision-making process. Until now, various methods and approaches have been proposed ranging from the use of traditional methods for detecting lanes to the implementation of state-of-the-art deep learning architectures. In this research, inspired by the state-of-the-art image segmentation's approaches, we have come up with novel approaches: Multi-Lane Detection System Based on Segmentation Model with applying some notable post-processing operations, which are connected component analysis and clustering methods, on the TuSimple dataset. In this study, the DeepLabv3+ architecture, which was the most effective of the several segmentation architectures utilized in the model training phase, is described. 98.98% train accuracy, 98.48% validation accuracy, 62.80% intersection over union (IoU), 89.81% precision, 85.51% recall, and 87.43% F1 score results were achieved for the model we trained. In summary, we propose an efficient multi-lane detection system that works at high performance, running at 136.7 frame per second (FPS) for the segmentation model and 16.8 FPS for overall system, can detect a variety of lanes, and can handle lane changes using a lane localization system.