dc.contributor.author |
Amrutasagar, K. |
|
dc.contributor.author |
Manoj, Pera |
|
dc.contributor.author |
Divya, Morla |
|
dc.contributor.author |
Mahesh Babu, Meethukulla |
|
dc.contributor.author |
Gangotri, Lavudiya |
|
dc.date.accessioned |
2024-03-16T18:33:37Z |
|
dc.date.available |
2024-03-16T18:33:37Z |
|
dc.date.issued |
2024-03-14 |
|
dc.identifier.issn |
2210-142X |
|
dc.identifier.uri |
https://journal.uob.edu.bh:443/handle/123456789/5527 |
|
dc.description.abstract |
This Paper presents a novel approach for real-time ambulance identification, distance calculation to traffic
junctions, and automated traffic signal control. The system utilizes strong computer vision procedures combined with
deep learning algorithms (YOLOV7) to accurately discern and follow ambulances despite being fed video and image
data, with the major task being to track ambulances. The base of the proposed solution includes designing a strong
ambulance identification algorithm by using the convolutional neural networks (CNNs) and distribution
algorithms. With this algorithm, not only do the pileups of ambulance navigate the nearby traffic junctions but they also
determine their distance from standstill vehicles. The contribution of this approach is found in the potential impact of
the real-time adaptive of traffic signals that give the top priority to ambulance lanes by allowing them to travel faster
during the home repair time. The following part of the project is implementation of traffic lights established based on
the automated system which moves at the speed of an ambulance close to the traffic junctions. Our model impact traffic
light control one way by introducing an intelligent system that minimizes delays of the ambulances in
intersections. Bright emergencies each second can be a slim window between living and dying that is why the speedy
the passage of green light traffic to ambulance lanes will become the main priority in our approach. To close this
technical paper, it summarizes the devised comprehensive system which is effective not only in detecting ambulances
but also in calculating distances joining the different traffic lights. Our model's integration with traffic control especially
during emergency situations through prioritizing ambulance lanes will clear the path for emergencies. The lanes will
help reduce the time ambulances use in traffic jams. |
en_US |
dc.language.iso |
en |
en_US |
dc.publisher |
University of Bahrain |
en_US |
dc.subject |
Emergency Vehicle Detection, Smart City Technologies, Urban Traffic Congestion, Traffic Signal Adaptation, Ambulance Prioritization, Real-time Traffic Management, Traffic Signal Control Systems, Real-time Ambulance Identification |
en_US |
dc.title |
Enhanced Traffic Signal Adaptation with Ambulance Identification and Distance Computation |
en_US |
dc.identifier.doi |
http://dx.doi.org/10.12785/ijcds/XXXXXX |
|
dc.volume |
16 |
en_US |
dc.issue |
1 |
en_US |
dc.pagestart |
1 |
en_US |
dc.pageend |
10 |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authorcountry |
India |
en_US |
dc.contributor.authoraffiliation |
Department of Computer Science and Engineering, SR Gudlavalleru Engineering College |
en_US |
dc.contributor.authoraffiliation |
Department of Computer Science and Engineering, SR Gudlavalleru Engineering College |
en_US |
dc.contributor.authoraffiliation |
Department of Computer Science and Engineering, SR Gudlavalleru Engineering College |
en_US |
dc.contributor.authoraffiliation |
Department of Computer Science and Engineering, SR Gudlavalleru Engineering College |
en_US |
dc.contributor.authoraffiliation |
Department of Computer Science and Engineering, SR Gudlavalleru Engineering College |
en_US |
dc.source.title |
International Journal of Computing and Digital Systems |
en_US |
dc.abbreviatedsourcetitle |
IJCDS |
en_US |