Adaptive Lane Point Extraction in Rain Conditions

The lane detection systems in ADAS and self-driving applications need to be resilient to adverse weather like rain, to minimize the chance of serious accidents. However, it is still challenging to maintain high precision in these conditions. State-of-the-art segmentation-based detectors use a naive post-processing method to quickly extract lane points from the segmented output based on the local maxima, but it is prone to failure when the segmentation contains errors. In this paper, we present an Adaptive Lane point EXtractor (ALEX) which overcomes this limitation by harnessing statistical properties from local regions of the segmentation to implicitly identify and compensate for errors. ALEX merges both local maximum and local mean statistics with CNN attributes to build a holistic hybrid feature set. Lane points are predicted from this representation by estimating their location on a reduced-size confidence map. A lateral offset is predicted to compute the precise location on the full-sized scene, while a class label prediction denotes which lane class the point belongs to. Besides the segmentation output and lane point ground truth, no additional information is required by ALEX, making it effectively agnostic to weather conditions and segmentation methods. Evaluation on two publicly available clear-weather databases and one rain-translated database verified that our approach consistently improved accuracy while reducing false positive and false negative rates, regardless of weather or segmentation method.