LiDAR Segmentation-based Adversarial Attacks on Autonomous Vehicles

Document Type

Article

Authors

Blake Johnson, University of Minnesota MorrisFollow

Abstract

Autonomous vehicles utilizing LiDAR-based 3D perception systems are susceptible to adversarial attacks. This paper focuses on a specific attack scenario that relies on the creation of adversarial point clusters with the intention of fooling the segmentation model utilized by LiDAR into misclassifying point cloud data. This can be translated into the real world with the placement of objects (such as road signs or cardboard) at these adversarial point cluster locations. These locations are generated through an optimization algorithm performed on said adversarial point clusters that are introduced by the attacker.

Recommended Citation

Johnson, Blake (2023) "LiDAR Segmentation-based Adversarial Attacks on Autonomous Vehicles," Scholarly Horizons: University of Minnesota, Morris Undergraduate Journal: Vol. 10: Iss. 2, Article 2.
DOI: https://doi.org/10.61366/2576-2176.1128
Available at: https://digitalcommons.morris.umn.edu/horizons/vol10/iss2/2

Primo Type

Article