Analysis of Sensor Attacks Against Autonomous Vehicles

Søren Jakobsen, Kenneth Knudsen, Birger Andersen

2023

Abstract

Fully Autonomous Vehicles (AVs) are estimated to reach consumers widely in the near future. The manufacturers need to be completely sure that AVs can outperform human drivers, which first of all requires a solid model of the world surrounding the car. Emerging trends for perception models in the automobile industry are towards combining the data from LiDAR and camera in Multi-Sensor Fusion (MSF). Making the perception model reliable in the event of unforeseen real world circumstances is tricky enough, but the real challenge comes from the security issue that arises when ill-intentioned people try to attack sensors. We analyse possible attacks and countermeasures for LiDAR and camera. We discuss it in context of MSF, and provide a simple framework for further analysis, which we conclude will be required to conceptualise a truly safe AV.

Paper Citation

in Harvard Style

Jakobsen S., Knudsen K. and Andersen B. (2023). Analysis of Sensor Attacks Against Autonomous Vehicles. In Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS, ISBN 978-989-758-643-9, SciTePress, pages 131-139. DOI: 10.5220/0011841800003482

in Bibtex Style

@conference{iotbds23,
author={Søren Jakobsen and Kenneth Knudsen and Birger Andersen},
title={Analysis of Sensor Attacks Against Autonomous Vehicles},
booktitle={Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,},
year={2023},
pages={131-139},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011841800003482},
isbn={978-989-758-643-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Internet of Things, Big Data and Security - Volume 1: IoTBDS,
TI - Analysis of Sensor Attacks Against Autonomous Vehicles
SN - 978-989-758-643-9
AU - Jakobsen S.
AU - Knudsen K.
AU - Andersen B.
PY - 2023
SP - 131
EP - 139
DO - 10.5220/0011841800003482
PB - SciTePress