Advancements in Lane Marking Detection: An Extensive Evaluation of Current Methods and Future Research Direction

As the automotive industry moves towards Autonomous Vehicles (AVs), developing reliable sensing systems such as lane marking detection, is crucial. Lane markings offer essential spatial and navigational cues for AVs' safety and efficiency. Therefore, it is vital to thoroughly understand the evolution and effectiveness of different lane marking detection technologies, particularly in identifying the challenges and influencing factors for their successful implementation. To facilitate an objective comparison, a comprehensive dataset was collected from a motorway in the UK employing an instrumented vehicle. This dataset contains representative scenarios including optimal conditions, faded lane markings, adverse weather conditions, nighttime and traffic congestion. Using this dataset, a comparative analysis of four prominent lane marking detection methods such as (i) Spatial Convolutional Neural Network, (ii) Lane detection model with attention mechanism, (iii) Inverse Saliency Pyramid Reconstruction Network (InSPyReNet) and (iv) REcurrent Feature-Shift Aggregator was conducted. InSPyReNet technique emerged superior, demonstrating outstanding precision and sensitivity in lane detection. The consistent evaluation approach in this research contributes to identifying the most suitable technique for robust lane marking detection under various environmental conditions. The study also navigates future research paths, emphasising model generalisation, and the importance of robustness in challenging conditions. The uniqueness of this research lies in its inclusive comparison of lane detection methods under varying conditions, evaluated on a single dataset. This not only serves as a valuable reference, but also opens new possibilities and avenues for future advancements in the field.