Lane detection is a critical component of many autonomous and advanced driver assistance systems. In this paper, the performance of “You Only Look Once for Panoptic” driving perception (YOLOP) deep learning model is evaluated on challenging conditions targeting road lane detection. YOLOP is evaluated on two publicly available datasets, including the BDD100K dataset and the KITTI dataset. Moreover, a third test dataset, which was created by the authors containing road images in Dubai city, was also utilized in the evaluation. The performance of YOLOP model was demonstrated on detecting lanes in challenging conditions such as curved roads, occluded markings, presence of shadow, and on rainy and foggy weather conditions. Results suggest that YOLOP achieves high Pixel Accuracy (PA) and low Mean Square Error (MSE) on all test sets. However, the Intersection over Union (IoU) metric was only 12.45%, 16.70%, and 26.79%, for BDD100K, KITTI, and the created test set, respectively. Overall, the results show that YOLOP provides a robust and efficient solution for lane detection that can be easily integrated into existing autonomous driving systems though its performance degrades in challenging conditions, particularly in the presence of shadows.