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Prediction of Communication Delays in Connected Vehicles and Platoons

Fulltext:


Authors:

Shahriar Hasan, Joseba Gorospe , Arrate Alonso Gómez , Svetlana Girs, Elisabeth Uhlemann

Research group:

Data Communication

Publication Type:

Conference/Workshop Paper

Venue:

2023 IEEE 97th Vehicular Technology Conference

Abstract

Automated vehicles connected through vehicle-to-vehicle (V2V) communications can use onboard sensor information from adjacent vehicles to provide higher traffic safety or passenger comfort. In particular, automated vehicles forming a platoon can enhance traffic safety by communicating before braking hard. It can also improve fuel efficiency by enabling reduced aerodynamic drag through short gaps. However, packet losses may increase the delay between periodic beacons, especially for the rear vehicles in a platoon. If the Lead Vehicle (LV) can forecast link quality, it can assign different platoon performance levels in terms of inter-vehicle distances and also facilitate the designing of safer braking strategies. This paper proposes a strategy for incorporating Machine Learning (ML) algorithms into, e.g., the LV of a platoon to enable online training and real-time prediction of communication delays incurred by connected vehicles during runtime. The prediction accuracy and its suitability for making safety-critical decisions during, e.g., emergency braking have been evaluated through rigorous simulations.

Bibtex

@inproceedings{Hasan6700,
author = {Shahriar Hasan and Joseba Gorospe and Arrate Alonso G{\'o}mez and Svetlana Girs and Elisabeth Uhlemann},
title = {Prediction of Communication Delays in Connected Vehicles and Platoons},
pages = {1--6},
month = {August},
year = {2023},
booktitle = {2023 IEEE 97th Vehicular Technology Conference},
url = {http://www.es.mdu.se/publications/6700-}
}