@inproceedings{65834231ca14474a89219e595186f54e,
title = "Traffic light detection and recognition in autonomous vehicles",
abstract = "Abstract - 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",
keywords = "Autonomous Vehicles (AV), traffic light detection, Autonomous Vehicles (AVs), Dlib, Raspberry Pi, Histogram of Oriented Gradient (HOG), Support Vector Machine (SVM), Colour Recognition, Imglab, Object Detector",
author = "Edward Dawam and Xiaohua Feng",
note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 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) ; Conference date: 12-09-2022 Through 15-09-2022",
year = "2022",
month = dec,
day = "13",
doi = "10.1109/dasc/picom/cbdcom/cy55231.2022.9927813",
language = "English",
series = "Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
editor = "Giancarlo Fortino and Raffaele Gravina and Antonio Guerrieri and Claudio Savaglio",
booktitle = "Proceedings of the 2022 IEEE International Conference on Dependable, Autonomic and Secure Computing, International Conference on Pervasive Intelligence and Computing, International Conference on Cloud and Big Data Computing, International Conference on Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2022",
address = "United States",
}