Dual Microwave Radar Vehicle Detection System at Four-Quadrant-Gate Railroad Grade Crossing

2014 ◽  
Vol 2458 (1) ◽  
pp. 110-117 ◽  
Author(s):  
Juan C. Medina ◽  
Rahim (Ray) F. Benekohal
Keyword(s):  
Detection System ◽  
Vehicle Detection ◽  
Microwave Radar ◽  
Grade Crossing ◽  
Four Quadrant

Trapped Vehicle Detection System for Four-Quadrant Gates in High Speed Rail Corridors: Design Methodology and Implementation Issues

1998 ◽  
Vol 1648 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Fred Coleman ◽  
Young J. Moon
Keyword(s):  
High Speed ◽  
Detection System ◽  
Vehicle Detection ◽  
High Speed Rail ◽  
Track System ◽  
The Status ◽  
System Functioning ◽  
Status Information ◽  
Four Quadrant ◽  
And Control

The objective of this paper is to determine the location of sensors in the track system functioning as checkpoints to provide information to a train on the status of the crossing and provide evasive maneuver time for the train and trapped vehicle. Two train-operating scenarios are evaluated: the first provides no deceleration when a trapped vehicle is detected; the second scenario has the train decelerate at a tolerable deceleration rate to passengers when a trapped vehicle is detected. The findings indicate that there is a trade-off between minimizing the distances to locate the trapped vehicle detection sensors in the track system and potential issues of reliability of vehicle detection and maximization of safety. Recommendations include provision of on-board real-time status information on the crossing(s) in the train with automatic train location and control to continuously provide safe stopping distances in event of a trapped vehicle.

Acoustic Signal Based Automatic Vehicle Detection System

2012 ◽  
Vol 2 (4) ◽  
pp. 88-89
Author(s):  
Sai Sandeep.k Sai Sandeep.k ◽  
◽  
P. Vijay Kumar
Keyword(s):  
Acoustic Signal ◽  
Detection System ◽  
Vehicle Detection

scholarly journals Lightweight PVIDNet: A Priority Vehicles Detection Network Model Based on Deep Learning for Intelligent Traffic Lights

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6218
Author(s):  
Rodrigo Carvalho Barbosa ◽  
Muhammad Shoaib Ayub ◽  
Renata Lopes Rosa ◽  
Demóstenes Zegarra Rodríguez ◽  
Lunchakorn Wuttisittikulkij
Keyword(s):  
Deep Learning ◽  
Traffic Control ◽  
Detection System ◽  
Lightweight Design ◽  
Vehicle Detection ◽  
Activation Function ◽  
Urban Traffic ◽  
Vehicle Classification ◽  
Traffic Light ◽  
Traffic Lights

Minimizing human intervention in engines, such as traffic lights, through automatic applications and sensors has been the focus of many studies. Thus, Deep Learning (DL) algorithms have been studied for traffic signs and vehicle identification in an urban traffic context. However, there is a lack of priority vehicle classification algorithms with high accuracy, fast processing, and a lightweight solution. For filling those gaps, a vehicle detection system is proposed, which is integrated with an intelligent traffic light. Thus, this work proposes (1) a novel vehicle detection model named Priority Vehicle Image Detection Network (PVIDNet), based on YOLOV3, (2) a lightweight design strategy for the PVIDNet model using an activation function to decrease the execution time of the proposed model, (3) a traffic control algorithm based on the Brazilian Traffic Code, and (4) a database containing Brazilian vehicle images. The effectiveness of the proposed solutions were evaluated using the Simulation of Urban MObility (SUMO) tool. Results show that PVIDNet reached an accuracy higher than 0.95, and the waiting time of priority vehicles was reduced by up to 50%, demonstrating the effectiveness of the proposed solution.

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