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Title Smart City Lane Detection For Autonomous Vehicle
ID_Doc 50341
Authors Dawam E.S.; Feng X.
Year 2020
Published Proceedings - IEEE 18th International Conference on Dependable, Autonomic and Secure Computing, IEEE 18th International Conference on Pervasive Intelligence and Computing, IEEE 6th International Conference on Cloud and Big Data Computing and IEEE 5th Cyber Science and Technology Congress, DASC/PiCom/CBDCom/CyberSciTech 2020
DOI http://dx.doi.org/10.1109/DASC-PICom-CBDCom-CyberSciTech49142.2020.00065
Abstract One of AI branch, Computer Vision-based recognition systems is necessary for security in Autonomous Vehicles (AVs). Traffic sign recognition systems are popularly used in AVs because it ensures driver safety and decrease vehicles accidents on roads. However, the inability of AVs to accurately detect road signs and pedestrian behaviour has led to road crashes and even death in recent times. Additionally, as cities become smarter, the traditional traffic signs dataset will change considerably, as theGoogle, 2020se vehicles and city infrastructure introduce modern facilities into their operation. In this paper, we introduce a computer vision based road surface marking recognition system to serve as an added layer of data source from which AVs will make decisions. We trained our detector using YOLOv3 running in the cloud to detect 25 classes of Road surface markings using over 25,000 images. The results of our experiment demonstrate a robust performance in terms of the accuracy and speed of detection. The results of which will consolidate the traffic sign recognition system, thereby ensuring more reliability and safety in AVs decision making. New algorithm using Deep Learning technology in Artificial intelligence (AI) application is implemented and tested successfully. © 2020 IEEE.
Author Keywords Artificial Intelligence (AI); Autonomous Vehicles (AV); Cloud; Convolutional Neural Network (CNN); Mean Average Precision (mAP); Smart City; You Only Look Once (YOLO)


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