Lane perception in rainy conditions for autonomous vehicles - Mahendran, Prabhu Shankar (Nanyang Technological University, 2024)
One of the critical tasks engaged by Autonomous Vehicles (AV) and Advanced Driver Assistance Systems (ADAS) is the detection and localization of lanes. To effectively automate lane keeping, route planning, and lane changing, it is essential to know the number of lanes, their orientations, and trajectories. As a result, lane detection has been of immense research interest in recent years. However, lane detection in rainy conditions remains underexplored. Rain particles interact with the visual sensors in numerous ways making the extraction of strong discriminative features even more difficult and antagonizing the detection task. De-raining has been intensively studied in the past to combat these disruptive effects. However, recent studies suggest that de-raining may further destroy some discriminative features. There is also a shortage of research work that focuses on lane detection in rainy conditions. Together with this, there is a lack of annotated lane detection datasets in rainy weather.