  In this paper we present a novel approach for lane detection and segmentation
using generative models. Traditionally discriminative models have been employed
to classify pixels semantically on a road. We model the probability
distribution of lanes and road symbols by training a generative adversarial
network. Based on the learned probability distribution, context-aware lanes and
road signs are generated for a given image which are further quantized for
nearest class label. Proposed method has been tested on BDD100K and Baidu
ApolloScape datasets and performs better than state of the art and exhibits
robustness to adverse conditions by generating lanes in faded out and occluded
scenarios.
