Intelligent Safety Message Dissemination with Vehicle Trajectory Density Predictions in VANETs

Karimzadeh Motallebiazar, Mostafa; Mariano de Souza, Allan; Zhao, Zhongliang; Braun, Torsten; Villas, Leandro; Sargento, Susana; Loureiro, Antonio A. F. (28 February 2019). Intelligent Safety Message Dissemination with Vehicle Trajectory Density Predictions in VANETs (Submitted) Future generation computer systems: Elsevier

[img] Text
mostafa-allan.pdf - Submitted Version
Restricted to registered users only
Available under License Publisher holds Copyright.

Download (4MB) | Request a copy

Integration of wireless communication systems and machine learning techniques are generating new applications and services in vehicle ad-hoc networks (VANETs). By analyzing data transmission in vehicle-to-vehicle (V2V) communications and vehicle-to-infrastructure (V2I) communications, an intelligent transportation system (ITS) can provide better safety applications. This work explores machine learning approaches to estimate vehicle density on predicted trajectories, which is further utilized to provide intelligent safety message dissemination. With our approach, the traffic safety message, such as accident notifications, will only be disseminated to relevant vehicles that are predicted to pass by the accident areas. Depending on the network connectivity, our system adaptively chooses vehicle-to-vehicle (V2V), vehicle-to-infrastructure (V2I) or hybrid communications to disseminate a message to relevant vehicles. We evaluate the system by using real-world VANET mobility datasets, and experiment results show that our system outperforms other mechanisms without considering predicted vehicle trajectory density information.

Item Type:

Working Paper

Division/Institute:

08 Faculty of Science > Institute of Computer Science (INF)
08 Faculty of Science > Institute of Computer Science (INF) > Communication and Distributed Systems (CDS)

UniBE Contributor:

Karimzadeh Motallebiazar, Mostafa; Mariano de Souza, Allan; Zhao, Zhongliang and Braun, Torsten

Subjects:

000 Computer science, knowledge & systems
500 Science > 510 Mathematics

ISSN:

0167-739X

Publisher:

Elsevier

Language:

English

Submitter:

Eirini Kalogeiton

Date Deposited:

07 Mar 2019 10:58

Last Modified:

24 Oct 2019 11:38

Uncontrolled Keywords:

Vehicle Trajectory Density Prediction, Congestion Prediction, Intelligent Transport System, Safety Data Dissemination

BORIS DOI:

10.7892/boris.127318

URI:

https://boris.unibe.ch/id/eprint/127318

Actions (login required)

Edit item Edit item
Provide Feedback