VEACON: A Vehicular Accident Ontology designed to improve safety on the roads

Barrachina, Javier; Garrido, Piedad; Fogue, Manuel; Martínez, Francisco J.; Cano, Juan-Carlos; Calafate, Carlos T.; Manzoni, Pietro

doi:10.1016/j.jnca.2012.07.013

Cited by 48 publications

(16 citation statements)

References 19 publications

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“…Since our proposal is focused on the use of an infrastructure to notify emergency services when an accident occurs, the warning messages exchanged between vehicles and RSUs are built according the Vehicular Accident Ontology (VEACON) [1], specially designed for sharing and reusing knowledge about the vehicles involved in road accidents. We simulated several front impact scenarios with two cars involved.…”

Section: D-rsu Deployment Policymentioning

confidence: 99%

Road Side Unit Deployment: A Density-Based Approach

Barrachina

Garrido

Fogue

et al. 2013

IEEE Intell. Transport. Syst. Mag.

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131

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Currently, the number of vehicles increases every year, raising the probability of having accidents. When an accident occurs, wireless technologies enable vehicles to share warning messages with other vehicles by using vehicle to vehicle (V2V) communications, and with the emergency services by using vehicle to infrastructure (V2I) communications. Regarding vehicle to infrastructure communications, Road Side Units (RSUs) act similarly to wireless LAN access points, and can provide communications with the infrastructure. Since RSUs are usually very expensive to install, authorities limit their number, especially in suburbs and areas of sparse population, making RSUs a precious resource in vehicular environments. In this paper, we propose a Density-based Road Side Unit deployment policy (D-RSU), specially designed to obtain an efficient system with the lowest possible cost to alert emergency services in case of an accident. Our approach is based on deploying RSUs using an inverse proportion to the expected density of vehicles. The obtained results shows how D-RSU is able to reduce the required number of RSUs, as well as the accident notification time. Index TermsVehicular networks, Road Side Unit, vehicle-to-infrastructure communication, road safety.

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Section: D-rsu Deployment Policymentioning

confidence: 99%

Road Side Unit Deployment: A Density-Based Approach

Barrachina

Garrido

Fogue

et al. 2013

IEEE Intell. Transport. Syst. Mag.

Self Cite

131

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“…Through software programming, to realize the output of the image motion parameters x  , y  , k and  . According to the above processing, the two-dimensional signal model of image is built, in the gray histogram of the image, gray sliders with three angles traverse the circular area, image stratified [14][15][16][17] , to layered purify and enhance the image, the process description of feature key points expression in the image histogram is shown in figure 1.…”

Section: Advances In Computer Science Research Volume 74mentioning

confidence: 99%

Adaptive Image Enhancement Algorithm Based on Matching Pyramid Decomposition

Ling¹

2017

Proceedings of the 2nd International Conference on Computer Engineering, Information Science &Amp; Application Technology (ICCI

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Abstract. Image enhancement technology is targeted to highlight the overall and local useful information characteristics of the images, expand the differences between different feature points in the image, rich image information to meet the application needs of different environments. The current image enhancement algorithm uses corner point extraction and feature matching technology, information loss and distortion can occur during SIFT feature point matching, the performance is not good. An image enhancement algorithm based on adaptive projection matching and pyramid decomposition is proposed, feature extraction and multi-layer purification of blurred images are performed on the scale translational plane, the discrete dopplerlet transform is used to reconstruct the image, the high frequency image is denoised by adaptive matching projection filtering, and the grey pixel value of image was enhanced by adaptive pyramidal matching projection decomposition. Simulation results show that the proposed algorithm is effective to remove or reduce the noise, and the image enhancement effect is good, and the peak signal-to-noise ratio is improved.

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“…We simulated a frontal impact scenario where two vehicles are involved. The warning messages exchanged between vehicles and RSUs are built according the Vehicular Accident Ontology (VEACON) (Barrachina et al, 2012b), which provides a standard structure which enables data interoperability among all the different entities involved in transportation systems.…”

Section: Simulation Environmentmentioning

confidence: 99%

A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

Barrachina

Garrido

Fogue

et al. 2015

Wireless Pers Commun

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Road traffic is experiencing a drastic increase in recent years, thereby increasing the every day traffic congestion problems, especially in metropolitan areas. Governments are making efforts to alleviate the increasing traffic pressure, being vehicular density one of the main metrics used for assessing the road traffic conditions. However, vehicle density is highly variable in time and space, making it difficult to be estimated accurately. Currently, most of the existing vehicle density estimation approaches, such as inductive loop detectors, or traffic surveillance cameras, require very specific infrastructure to be installed on the road. In this paper, we present a novel solution to accurately estimate the density of vehicles in urban scenarios. Our proposal, that has been specially designed for Vehicular Networks, allows Intelligent Transportation Systems to continuously estimate vehicular density by accounting for the number of beacons received per Road Side Unit (RSU), and also considering the roadmap topology where the RSUs are located. Simulation results reveal that, unlike previous proposals solely based on the number of beacons received, our approach accurately estimates the vehicular density, and therefore our approach can be integrated within automatic traffic controlling systems to predict traffic jams, and thus introducing countermeasures.

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VEACON: A Vehicular Accident Ontology designed to improve safety on the roads

Cited by 48 publications

References 19 publications

Road Side Unit Deployment: A Density-Based Approach

Road Side Unit Deployment: A Density-Based Approach

Adaptive Image Enhancement Algorithm Based on Matching Pyramid Decomposition

A V2I-Based Real-Time Traffic Density Estimation System in Urban Scenarios

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