In response to the accelerating deployment of autonomous vehicles and the growing reliance on deep learning-based algorithms, multiple line detection datasets have been released in the last few years. However, current datasets tend to focus only on image-plane-level line detection, neglecting the broader scope of this task and thus limiting their utility for comprehensive validation. To address this limitation, this study proposes a novel, custom-acquired dataset designed to enhance the validation of complete line detection pipelines. In particular, our dataset was recorded in controlled environments, capturing the RTK-GNSS position of road lines and the position of a vehicle equipped with a wide field of view camera. The dataset, which was recorded on closed race tracks, presents realistic challenges for line detection algorithms, particularly in narrow sections and rapid chicanes of the roads where lateral lines are not always visible. Furthermore, it offers researchers a unique resource, providing precise ground truth information for both road lines and vehicle positions, enabling the evaluation of complete line detection pipelines from the segmentation phase to the mapping one. Finally, the dataset also reflects the diverse and challenging situations faced by autonomous vehicles in the real world (i.e., multiple weather conditions, sharp cornering sections, tunnels, etc.), making it a valuable tool for enhancing the performance and safety of autonomous vehicles. The complete dataset is made available to researchers at https://airlab.deib.polimi.it/datasets-and-tools/ 1 1 Complete dataset released upon paper acceptance