Contextual Road Lane and Symbol Generation for Autonomous Driving
International Conference on Machine Learning and Applications(2021)
Abstract
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 BDDIOOK and Baidu’s 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.
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Key words
Lane detection,GAN,CGAN,Autonomous driving,ADAS,Computer Vision,Machine Learning,Deep Learning,Road Symbol Detection,VAE,Adversarial loss,Adversarial attacks,encoder decoder,Generative models
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