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Enhancing Autonomous Vehicles System Security : Advanced Attack Detection for Robust Safeguarding

Akbarian, Fatemeh LU ; Papageorgiou, Dimitrios ; Chamideh, Seyedezahra LU ; Mikkelsen, Jeppe Heini ; Karstensen, Peter Iwer Hoedt and Kihl, Maria LU (2024) 10th International Conference on Control, Decision and Information Technologies, CoDIT 2024 p.730-737
Abstract

The advances in highly automated and autonomous transportation systems over the last decade have generated great interest in topics in the safe navigation of land vehicles. With distributed control strategies employed in the majority of applications of autonomous vehicles, such as traffic and formation control, the much-required resilience takes the form of fault-tolerance with respect to information corruption, especially, when such information is utilized in closed-loop control. This study addresses the topic of detection of malicious attacks in a decentralized traffic control system for land vehicles. The proposed method employs trajectory prediction based on a hierarchical Model Predictive Control scheme, as well as,... (More)

The advances in highly automated and autonomous transportation systems over the last decade have generated great interest in topics in the safe navigation of land vehicles. With distributed control strategies employed in the majority of applications of autonomous vehicles, such as traffic and formation control, the much-required resilience takes the form of fault-tolerance with respect to information corruption, especially, when such information is utilized in closed-loop control. This study addresses the topic of detection of malicious attacks in a decentralized traffic control system for land vehicles. The proposed method employs trajectory prediction based on a hierarchical Model Predictive Control scheme, as well as, vehicle-to-vehicle communication in order to generate redundancy of collision risk information. The efficacy of the method is demonstrated in Eclipse Simulation of Urban Mobility (SUMO) considering the scenario of junction traffic management.

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Please use this url to cite or link to this publication:
author
; ; ; ; and
organization
publishing date
type
Chapter in Book/Report/Conference proceeding
publication status
published
subject
keywords
attack detection, autonomous vehicles, fault diagnosis, hierarchical model predictive control
host publication
10th 2024 International Conference on Control, Decision and Information Technologies, CoDIT 2024
pages
8 pages
publisher
IEEE - Institute of Electrical and Electronics Engineers Inc.
conference name
10th International Conference on Control, Decision and Information Technologies, CoDIT 2024
conference location
Valletta, Malta
conference dates
2024-07-01 - 2024-07-04
external identifiers
  • scopus:85208277977
ISBN
9798350373974
DOI
10.1109/CoDIT62066.2024.10708181
project
Optimization and control of networked systems for autonomous vehicle applications
6G wireless, sub-project: vehicular communications
Nordic University Hub on Internet of Things
language
English
LU publication?
yes
id
4ec01382-3c23-4aac-b1c2-ea291489c158
date added to LUP
2024-12-11 11:49:11
date last changed
2025-04-04 14:10:10
@inproceedings{4ec01382-3c23-4aac-b1c2-ea291489c158,
  abstract     = {{<p>The advances in highly automated and autonomous transportation systems over the last decade have generated great interest in topics in the safe navigation of land vehicles. With distributed control strategies employed in the majority of applications of autonomous vehicles, such as traffic and formation control, the much-required resilience takes the form of fault-tolerance with respect to information corruption, especially, when such information is utilized in closed-loop control. This study addresses the topic of detection of malicious attacks in a decentralized traffic control system for land vehicles. The proposed method employs trajectory prediction based on a hierarchical Model Predictive Control scheme, as well as, vehicle-to-vehicle communication in order to generate redundancy of collision risk information. The efficacy of the method is demonstrated in Eclipse Simulation of Urban Mobility (SUMO) considering the scenario of junction traffic management.</p>}},
  author       = {{Akbarian, Fatemeh and Papageorgiou, Dimitrios and Chamideh, Seyedezahra and Mikkelsen, Jeppe Heini and Karstensen, Peter Iwer Hoedt and Kihl, Maria}},
  booktitle    = {{10th 2024 International Conference on Control, Decision and Information Technologies, CoDIT 2024}},
  isbn         = {{9798350373974}},
  keywords     = {{attack detection; autonomous vehicles; fault diagnosis; hierarchical model predictive control}},
  language     = {{eng}},
  pages        = {{730--737}},
  publisher    = {{IEEE - Institute of Electrical and Electronics Engineers Inc.}},
  title        = {{Enhancing Autonomous Vehicles System Security : Advanced Attack Detection for Robust Safeguarding}},
  url          = {{http://dx.doi.org/10.1109/CoDIT62066.2024.10708181}},
  doi          = {{10.1109/CoDIT62066.2024.10708181}},
  year         = {{2024}},
}