| Title |
Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure |
| ID_Doc |
42520 |
| Authors |
Ghanem, S; Kanungo, P; Panda, G; Satapathy, SC; Sharma, R |
| Title |
Lane detection under artificial colored light in tunnels and on highways: an IoT-based framework for smart city infrastructure |
| Year |
2023 |
| Published |
Complex & Intelligent Systems, 9, 4 |
| DOI |
10.1007/s40747-021-00381-2 |
| Abstract |
Lane detection (LD) under different illumination conditions is a vital part of lane departure warning system and vehicle localization which are current trends in the future smart cities. Recently, vision-based methods are proposed to detect lane markers in different road situations including abnormal marker cases. However, an inclusive framework for driverless cars has not been introduced yet. In this work, a novel LD and tracking method is proposed for the autonomous vehicle in the IoT-based framework (IBF). The IBF consists of three modules which are vehicle board (VB), cloud module (CM), and the vehicle remote controller. The LD and tracking are carried out initially by the VB, and then, in case of any failure, the whole set of data is passed to CM to be processed and the results are sent to the VB to perform the appropriate action. If the CM detects a lane departure, then the autonomous vehicle is driven remotely and the VB would be restarted. In addition to the proposed framework, an illumination invariance method is presented to detect lane markers under different light conditions. The simulation results with real-life data demonstrate lane-keeping rates of 95.3% and 95.2% in tunnels and on highways, respectively. The approximate processing time of the proposed method is 31 ms/frame which fulfills the real-time requirements. |
| Author Keywords |
Vehicle localization; IoT-based framework; Illumination invariance; Cloud module; Vehicle board |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000646097200001 |
| WoS Category |
Computer Science, Artificial Intelligence |
| Research Area |
Computer Science |
| PDF |
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