  In this paper, we propose a unified end-to-end trainable multi-task network
that jointly handles lane and road marking detection and recognition that is
guided by a vanishing point under adverse weather conditions. We tackle rainy
and low illumination conditions, which have not been extensively studied until
now due to clear challenges. For example, images taken under rainy days are
subject to low illumination, while wet roads cause light reflection and distort
the appearance of lane and road markings. At night, color distortion occurs
under limited illumination. As a result, no benchmark dataset exists and only a
few developed algorithms work under poor weather conditions. To address this
shortcoming, we build up a lane and road marking benchmark which consists of
about 20,000 images with 17 lane and road marking classes under four different
scenarios: no rain, rain, heavy rain, and night. We train and evaluate several
versions of the proposed multi-task network and validate the importance of each
task. The resulting approach, VPGNet, can detect and classify lanes and road
markings, and predict a vanishing point with a single forward pass.
Experimental results show that our approach achieves high accuracy and
robustness under various conditions in real-time (20 fps). The benchmark and
the VPGNet model will be publicly available.
