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Smart city article details
| Title | Deepdrive: Innovations In Distance Detection And Intervehicle Communication Through Deep Learning |
|---|---|
| ID_Doc | 18126 |
| Authors | Vivekanand C.V.; Inbamalar T.M.; Amala Justus Selvam M.; Monika Devi A.; Mohanambikai A.; Pooja Shree K. |
| Year | 2023 |
| Published | 4th International Conference on Communication, Computing and Industry 6.0, C216 2023 |
| DOI | http://dx.doi.org/10.1109/C2I659362.2023.10430712 |
| Abstract | Over 1.2 million people lost their lives in traffic accidents in 2018, according to the World Health Organization (WHO). This conference paper proposes ground-breaking research that integrates deep learning approaches for object identification and distance estimates utilising onboard sensors with intervehicle communication using Li-Fi (Light Fidelity) technology. Li-Fi integration allows for fast wireless communication between vehicles, while deep learning improves the precision and dependability of object identification and distance calculation, leading to improvements in traffic flow and road safety. Traditional radio frequency-based systems are supplemented by this communication protocol, which offers faster and more secure data transfer between cars. A sensor suite that includes cameras and distance sensors simultaneously documents the area around the vehicle. Our method integrates sensor data with Li-Fi-based intervehicle communication for distance calculation. This study concludes by highlighting an innovative experiment that combines deep learning and Li-Fi inter-vehicle communication to detect a wide range of objects and estimate their distance with great precision. These technologies have been used to show a viable route towards safer and more effective road transport systems, advancing intelligent vehicles and upcoming smart cities. © 2023 IEEE. |
| Author Keywords | Inter-vehicle communication; Light Fidelity (Li-Fi) |
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