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Traffic sign identification using camera images from vehicles plays acritical role in autonomous driving and path planning. However, the frontcamera images can be distorted due to blurriness, lighting variations andvandalism which can lead to degradation of detection performances. As asolution, machine learning models must be trained with data from multipledomains, and collecting and labeling more data in each new domain is timeconsuming and expensive. In this work, we present an end-to-end framework toaugment traffic sign training data using optimal reinforcement learningpolicies and a variety of Generative Adversarial Network (GAN) models, that canthen be used to train traffic sign detector modules. Our automated augmenterenables learning from transformed nightime, poor lighting, and varying degreesof occlusions using the LISA Traffic Sign and BDD-Nexar dataset. The proposedmethod enables mapping training data from one domain to another, therebyimproving traffic sign detection precision/recall from 0.70/0.66 to 0.83/0.71for nighttime images.

Automated Augmentation with Reinforcement Learning and GANs for Robust Identification of Traffic Signs using Front Camera Images