Advances In Lane Marking Detection Algorithms For All-Weather Conditions

Driving vehicles in all-weather conditions is challenging as the lane markers tend to be unclear to the drivers for detecting the lanes. Moreover, the vehicles will move slower hence increasing the road traffic congestion which causes difficulties in detecting the lane markers especially for advanced driving assistance systems (ADAS). Therefore this paper conducts a thorough review on vision-based lane marking detection algorithms developed for all-weather conditions. The review methodology consists of two major areas, which are a review on the general system models employed in the lane marking detection algorithms and a review on the types of weather conditions considered for the algorithms. Throughout the review process, it is observed that the lane marking detection algorithms in literature have mostly considered weather conditions such as fog, rain, haze and snow. Most the research work focus on lane detection, but the classification of the types of lane markers remains a significant research gap that is worth to be addressed for ADAS and intelligent transport systems.