| Title |
Smart City Lane Detection for Autonomous Vehicle |
| ID_Doc |
38048 |
| Authors |
Dawam, ES; Feng, XH |
| Title |
Smart City Lane Detection for Autonomous Vehicle |
| Year |
2020 |
| Published |
|
| DOI |
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. |
| Author Keywords |
Artificial Intelligence (AI); Autonomous Vehicles (AV); Smart City; You Only Look Once (YOLO); Cloud; Convolutional Neural Network (CNN); Mean Average Precision (mAP) |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000942908500039 |
| WoS Category |
Automation & Control Systems; Computer Science, Artificial Intelligence; Computer Science, Theory & Methods; Engineering, Electrical & Electronic |
| Research Area |
Automation & Control Systems; Computer Science; Engineering |
| PDF |
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