AffiliationUniversity of Bedfordshire
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AbstractAbstract - Autonomous Vehicles (AVs) face the challenge of recognising active traffic lights under harsh environmental conditions. Standard cameras and computer vision algorithms also face the same challenge. In this paper, we built a small-scale system to mitigate this challenge. First, we developed a light controller and a dataset builder script. The light controller and dataset builder script were then used to build a dataset of traffic lights with different lights activated. Bounding boxes were annotated on the traffic light dataset using dlib's imglab software. The dataset uses the HOG with Linear SVM object detector. An RGB histogram approach is adopted to train a logistic regression model on the feature vector data to recognise which light is "on" among the training images. Finally, a robot control script is developed and tested. The script uses both the object detector and colour recogniser for its detection and recognition. Our results show 89% accuracy in identifying a red-yellow-green traffic light under extreme environmental conditions
CitationDawam ES, Feng X (2022) 'Traffic light detection and recognition in autonomous vehicles', 2022 IEEE Intl Conf on Dependable, Autonomic and Secure Computing, Intl Conf on Pervasive Intelligence and Computing, Intl Conf on Cloud and Big Data Computing, Intl Conf on Cyber Science and Technology Congress (DASC/PiCom/CBDCom/CyberSciTech) - Falerna, IEEE.
TypeConference papers, meetings and proceedings