Advantages in Crash Severity Prediction Using Vehicle to Vehicle Communication

Boehmlaender, Dennis; Hasirlioglu, Sinan; Yano, Vitor; Lauerer, Christian; Brandmeier, Thomas; Zimmer, Alessandro

doi:10.1109/dsn-w.2015.23

Cited by 8 publications

(3 citation statements)

References 14 publications

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“…It uses least amount of energy, making the Internet of Things more energy efficient (IoT). Vehicular interaction is a unique implementation discovered in vehicle crashes alert system [34], brake collaborating mechanisms, and advanced driver assistance systems [35]. D2D services have the potential to become a crucial element of public protection and disaster relief (PPDR) and national security and public safety (NSPS) solutions [36].…”

Section: Implementation Challenges In D2d Communicationmentioning

confidence: 99%

Survey on 5G Security Challenges in Device to Device Communication

Kumar

2023

IJRASET

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Mobile communication networks have evolved from first generation to fifth generation as a result of a continuing requirement for enhancing network’s capacity to satisfy the expanding needs of customers (5G). In following years, the no. of linked devices is estimated to grow to over fifty billion, according to estimates. The heterogeneity of such a vast number of connections is expected, necessitating faster data rates, shorter latency, more system capacity, and higher throughput. Mobile networks will have to be expanded to meet up with increasing expectations. In order to meet consumers' expanding expectations and make efficient use of limited sources, D2D communication is regarded as an innovative technique for existing and future wireless networks. It permits users to connect with one another, resulting in increased energy efficiency and system throughput. This study provides a thorough examination of D2D communications, as well as the security challenges that D2D must address in order to become a viable wireless network paradigm.

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Section: Implementation Challenges In D2d Communicationmentioning

confidence: 99%

Survey on 5G Security Challenges in Device to Device Communication

Kumar

2023

IJRASET

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“…V2V communication facilitates vehicles to wirelessly exchange information relevant to their location, speed and direction. The technology that is used in V2V communication permits the vehicles up to 10 times/sec messages thus establishing a 360 degree "awareness" of the other vehicles in propinquity [61]. Vehicles that have appropriate software (or safety applications) can utilize the messages from surrounding vehicles for determining the possible crash threats that they can face [62].…”

Section: Vehicle To Vehicle (V2v)mentioning

confidence: 99%

New Trends in Location-Based Services Technology

Ahmad¹,

Abd‐Alhameed²

2022

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9

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Because of technological advancements, location-based services are always evolving with the passage of time. The current study discussed significant developments in the field of locationbased services technology. LBS Indoor Systems, LBS Satellite Systems, LBS GPS System, and Outdoor Systems employing Terrestrial BSs are all part of the localization infrastructure. The report also discusses the benefits and drawbacks of various outdoor systems that employ Terrestrial Base Stations, such as the Global System for Mobile Communications. WCDMA (with IPDL, TA-IPDL, and OTDOA-PE), Smart Antenna Systems, and Distributed Antenna Systems, GSM (with E-OTD), GSM (with CELL-ID), CDMA/GPRS (with A-GPS), CDMA/GPRS (with A-GPS), CDMA/GPRS (with A-GPS), CDMA/GPRS (with A-GPS), CDMA/GPRS (with A-GP (with A-GPS) -AFLT+AGPS, Wireless Assisted GPS and AFLT: GPS One A Hybrid Solution, WCDMA (with IP), WCDMA (with IP), WCDMA (with IP), WCDMA (with IP), WCDMA (with IP), WCDMA (with IP), WCDMA (DAS). Some of the techniques discussed in this paper are localization by Cell-ID, localization by Prediction, localization by Angle of Arrival (AOA), localization by Time of Arrival (TOA) and Observed Time Difference of Arrival (TDOA), localization by Finger Printing, and Hybrid Localization AOA-TOA Based. Low-power wide-area networks (LPWANs), cellular 5Gbased positioning technologies, ZigBee (Mesh Protocols), and WiFi6 have all been studied in relation to the Internet of Things (IOT). This document also covers NLBS (which includes Bluetooth Low Energy technology, WLAN, and Infrared), UWB, and Vehicle to Vehicle Communications.

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“…Other different prediction models have been examined with regards to traffic collisions, such as artificial neural networks and support vector machine for predicting collision duration [21]; decision trees, Naïve Bayes, KNN and AdaBoost for predicting collision occurrence [22]; binary and skewed logistic regression [23], decision trees, multilayer perceptron [24], probabilistic neural net, Random Forests [25], and Bayesian networks [26] for predicting injury severity. Sensors and vehicle-to-vehicle communication [27], as well as genetic programming [28], are also investigated in the literature in the context of real-time collision prediction. Some studies have also taken a time series approach to analyse the fatalities in traffic collisions [29,30].…”

Section: Introductionmentioning

confidence: 99%

Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

Shanshal

Babaoglu

Başar

2020

PROMET

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Traffic-related deaths and severe injuries may affect every person on the roads, whether driving, cycling or walking. Toronto, the largest city in Canada and the fourth largest in North America, aims to eliminate traffic-related fatalities and serious injuries on city streets. The aim of this study is to build a prediction model using data analytics and machine learning techniques that learn from past patterns, providing additional data-driven decision support for strategic planning. A detailed exploratory analysis is presented, investigating the relationship between the variables and factors affecting collisions in Toronto. A learning-based model is proposed to predict the fatalities and severe injuries in traffic collisions through a comparison of two predictive models: Lasso Regression and Random Forest. Exploratory data analysis results reveal both spatio-temporal and behavioural patterns such as the prevalence of collisions in intersections, in the spring and summer and aggressive driving and inattentive behaviours in drivers. The prediction results show that the best predictor of injury severity for drivers, cyclists and pedestrians is Random Forest with an accuracy of 0.80, 0.89, and 0.80, respectively. The proposed methods demonstrate the effectiveness of machine learning application to traffic and collision data, both for exploratory and predictive analytics.

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Advantages in Crash Severity Prediction Using Vehicle to Vehicle Communication

Cited by 8 publications

References 14 publications

Survey on 5G Security Challenges in Device to Device Communication

Survey on 5G Security Challenges in Device to Device Communication

New Trends in Location-Based Services Technology

Prediction of Fatal and Major Injury of Drivers, Cyclists, and Pedestrians in Collisions

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