Please use this identifier to cite or link to this item: https://knowledgecommons.lakeheadu.ca/handle/2453/5254
Title: Detection, identification, and mitigation of false data injection attacks and faults in vehicle platooning
Authors: Ahmed, Najeebuddin
Keywords: Vehicle-to-Vehicle (V2V) communication;Vehicle-to-Infrastructure (V2I) communication;Vehicle platooning
Issue Date: 2023
Abstract: Smart vehicles are designed leveraging advanced hardware, including sensors, and cuttingedge technologies, such as artificial intelligence, Vehicle-to-Vehicle (V2V) communication, and Vehicle-to-Infrastructure (V2I) communication. These components are seamlessly integrated through software systems, empowering vehicle to make intelligent decisions and navigate safely in diverse environments. V2V communication plays a vital role in facilitating information exchange among vehicles, encompassing crucial parameters, such as speed, acceleration, location, and vehicle size. One of the key applications of V2V communication is vehicle platooning, where a group of vehicles travels closely together, forming a cohesive convoy. Platooning has garnered significant attention due to its potential to revolutionize road safety, fuel efficiency, and traffic flow. By reducing the distance between vehicles, platooning minimizes aerodynamic drag, leading to improved fuel efficiency and reduced emissions. Moreover, the tight coordination and communication between vehicles enable enhanced safety through faster reaction times and response capabilities. Additionally, platooning optimizes road capacity and traffic flow, potentially alleviating congestion and enhancing overall transportation efficiency. [...]
URI: https://knowledgecommons.lakeheadu.ca/handle/2453/5254
metadata.etd.degree.discipline: Engineering : Electrical & Computer
metadata.etd.degree.name: Master of Science
metadata.etd.degree.level: Master
metadata.dc.contributor.advisor: Ameli, Amir
Naser, Hassan
metadata.dc.contributor.committeemember: Akilan, Thangarajah
Ejaz, Waleed
Appears in Collections:Electronic Theses and Dissertations from 2009

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