Intelligent Model for Traffic Safety Applications

2012 ◽  
Vol 8 (3) ◽  
pp. 358-365
Author(s):  
R.
Keyword(s):  
Traffic Safety ◽  
Traffic Safety Applications ◽  
Intelligent Model ◽  
Safety Applications

scholarly journals Application of Advanced Communication Technologies in the Improvement of Traffic Safety

2017 ◽  
Vol 2 (1-2) ◽  
Author(s):  
Goran Z. Marković
Keyword(s):  
Traffic Safety ◽  
Communication Systems ◽  
Communication Technologies ◽  
Vehicular Communication ◽  
Future Directions ◽  
Car Industry ◽  
Dedicated Short Range Communications ◽  
Traffic Safety Applications ◽  
Safety Applications

Incorporation of advanced info-communication technologies into vehicular environment currently captures a large attention by numerous investigators, telecommunications operators, traffic safety regulatory institutions, car industry manufacturers and other interested participants. In this paper, we overview of some prospective wireless communication technologies, such as the DSRC (Dedicated Short Range Communications) and advanced LTE (Long Term Evolution) mobile communication systems, which are considered as two promising candidates to support future traffic safety applications in vehicular environment is presented. The communication requirements of some active traffic safety applications are pointed. A summary of various types of communications for intelligent VCS (Vehicular Communication System) applications is given. Some future directions and challenging issues for implementing traffic safety applications are also discussed. Our goal is to demonstrate the growing impact and importance of modern communication technologies in achieving future traffic accident-free roads.

Reliable data dissemination protocol for VANET traffic safety applications

2017 ◽  
Vol 63 ◽  
pp. 30-44 ◽  
Author(s):  
Renê Oliveira ◽  
Carlos Montez ◽  
Azzedine Boukerche ◽  
Michelle S. Wangham
Keyword(s):  
Traffic Safety ◽  
Data Dissemination ◽  
Reliable Data ◽  
Traffic Safety Applications ◽  
Safety Applications

scholarly journals Real-Time Communication Support for Cooperative, Infrastructure-Based Traffic Safety Applications

2011 ◽  
Vol 2011 ◽  
pp. 1-17 ◽  
Author(s):  
Annette Böhm ◽  
Magnus Jonsson
Keyword(s):  
Real Time ◽  
Traffic Safety ◽  
Road Traffic ◽  
Radio Channel ◽  
Ieee 802.11P ◽  
Transport Systems ◽  
Traffic Density ◽  
Road Side Unit ◽  
Traffic Safety Applications ◽  
Safety Applications

The implementation of ITS (Intelligent Transport Systems) services offers great potential to improve the level of safety, efficiency and comfort on our roads. Although cooperative traffic safety applications rely heavily on the support for real-time communication, the Medium Access Control (MAC) mechanism proposed for the upcoming IEEE 802.11p standard, intended for ITS applications, does not offer deterministic real-time support, that is, the access delay to the common radio channel is not upper bounded. To address this problem, we present a framework for a vehicle-to-infrastructure-based (V2I) communication solution extending IEEE 802.11p by introducing a collision-free MAC phase assigning each vehicle an individual priority based on its geographical position, its proximity to potential hazards and the overall road traffic density. Our solution is able to guarantee the timely treatment of safety-critical data, while minimizing the required length of this real-time MAC phase and freeing bandwidth for best-effort services (targeting improved driving comfort and traffic efficiency). Furthermore, we target fast connection setup, associating a passing vehicle to an RSU (Road Side Unit), and proactive handover between widely spaced RSUs. Our real-time MAC concept is evaluated analytically and by simulation based on a realistic task set from a V2I highway merge assistance scenario.

scholarly journals Extraction of Vehicle Turning Trajectories at Signalized Intersections Using Convolutional Neural Networks

2020 ◽  
Vol 45 (10) ◽  
pp. 8011-8025
Author(s):  
Osama Abdeljaber ◽  
Adel Younis ◽  
Wael Alhajyaseen
Keyword(s):  
Traffic Safety ◽  
Signalized Intersections ◽  
Average Error ◽  
Minimum Jerk ◽  
Video Frames ◽  
Moving Vehicles ◽  
Traffic Safety Applications ◽  
Safety Applications ◽  
Potential Use ◽  
Traffic Rule

Abstract This paper aims at developing a convolutional neural network (CNN)-based tool that can automatically detect the left-turning vehicles (right-hand traffic rule) at signalized intersections and extract their trajectories from a recorded video. The proposed tool uses a region-based CNN trained over a limited number of video frames to detect moving vehicles. Kalman filters are then used to track the detected vehicles and extract their trajectories. The proposed tool achieved an acceptable accuracy level when verified against the manually extracted trajectories, with an average error of 16.5 cm. Furthermore, the trajectories extracted using the proposed vehicle tracking method were used to demonstrate the applicability of the minimum-jerk principle to reproduce variations in the vehicles’ paths. The effort presented in this paper can be regarded as a way forward toward maximizing the potential use of deep learning in traffic safety applications.

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