Localization of the vehicle with respect to road lanes plays a critical role in the advances of making the vehicle fully autonomous. Vision based road lane line detection provides a feasible and low cost solution as the vehicle pose can be derived from the detection. While good progress has been made, the road lane line detection has remained an open one, given challenging road appearances with shadows, varying lighting conditions, worn-out lane lines etc. In this paper, we propose a more robust vision-based approach with respect to these challenges. The approach incorporates four key steps. Lane line pixels are first pooled with a ridge detector. An effective noise filtering mechanism will next remove noise pixels to a large extent. A modified version of sequential RANdom Sample Consensus) is then adopted in a model fitting procedure to ensure each lane line in the image is captured correctly. Finally, if lane lines on both sides of the road exist, a parallelism reinforcement technique is imposed to improve the model accuracy. The results obtained show that the proposed approach is able to detect the lane lines accurately and at a high success rate compared to current approaches. The model derived from the lane line detection is capable of generating precise and consistent vehicle localization information with respect to road lane lines, including road geometry, vehicle position and orientation.
['Xinxin Du', 'Kok Kiong Tan']
Vision-based approach towards lane line detection and vehicle localization