VANET Based Embedded Traffic Control System

Shirabur, Sadashiv; Hunagund, Shivaling; Murgd, Suresh

doi:10.1109/rteict49044.2020.9315602

Cited by 9 publications

(2 citation statements)

References 4 publications

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“…While vehicles in VANET have constrained mobility and can only travel along the paved road network, nodes in mobile ad hoc networks are assumed to move freely in any direction. The topology of the network changes quickly in the VANET because nodes are moving vehicles 7 . Computing the distance between the vehicle and the traffic light is made easier with the aid of a digital road map.…”

Section: Traffic Light Recognitionmentioning

confidence: 99%

An innovative traffic light recognition method using vehicular ad-hoc networks

Al-Ezaly

El-Bakry²,

Abo-Elfetoh³

et al. 2023

Sci Rep

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Car congestion is a pressing issue for everyone on the planet. Car congestion can be caused by accidents, traffic lights, rapid accelerations, deceleration, and hesitation of drivers, as well as a small low-carrying capacity road without bridges. Increasing road width and constructing roundabouts and bridges are solutions to car congestion, but the cost is significant. TLR (traffic light recognition) reduces accidents and traffic congestion caused by traffic lights (TLs). Image processing with convolutional neural network (CNN) lakes dealing with harsh weather. A semi-automatic annotation for traffic light detection employs a global navigation satellite system, raising the cost of automobiles. Data was not collected in harsh conditions, and tracking was not supported. Integrated channel feature tracking (ICFT) combines detection and tracking, but it does not support sharing information with neighbors. This study used vehicular ad-hoc networks (VANETs) for VANET traffic light recognition (VTLR). Information exchange as well as monitoring of the TL status, time remaining before a change, and recommended speeds are supported. Based on testing, it has been determined that VTLR performs better than semi-automatic annotation, image processing with CNN, and ICFT in terms of delay, success ratio, and the number of detections per second.

show abstract

Section: Traffic Light Recognitionmentioning

confidence: 99%

An innovative traffic light recognition method using vehicular ad-hoc networks

Al-Ezaly

El-Bakry²,

Abo-Elfetoh³

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

“…CAVs, combining AVs and Vehicular Ad-hoc Networking (VANET), can facilitate traffic management [5], where a TLCS factors in incoming traffic in advance to provide the best traffic lights schedule, e.g., to minimize the average waiting time. CAV can accelerate and decelerate based on TLCS guidance [5]. In addition, CAV can form platoons automatically to reduce the space between each other, leading to full use of the road capacity.…”

Section: Introductionmentioning

confidence: 99%