Effects of perturbed depth sensors in autonomous ground vehicles

Anthony J. Pinar; Adam J. Webb; Jessica L. Brown; Timothy C. Havens; Brendan Alvey; Guilherme N. DeSouza; Derek T. Anderson; Stanton R. Price

doi:10.1117/12.2587515

12 April 2021 Effects of perturbed depth sensors in autonomous ground vehicles

Anthony J. Pinar, Adam J. Webb, Jessica L. Brown, Timothy C. Havens, Brendan Alvey, Guilherme N. DeSouza, Derek T. Anderson, Stanton R. Price

Author Affiliations +

Proceedings Volume 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III; 117461F (2021) https://doi.org/10.1117/12.2587515
Event: SPIE Defense + Commercial Sensing, 2021, Online Only

Abstract

Cybersecurity of autonomous vehicles is a pertinent concern both for defense and also civilian systems. From self-driving cars to autonomous Navy vessels, malfunctions can have devastating consequences, including losses of life and infrastructure. Autonomous ground vehicles use a variety of sensors to image their environment: passive sensors such as RGB(-D) and thermal cameras, and active sensors such as LIDAR, radar, and sonar. These sensors are either used alone or fused to accomplish the basic mobile autonomy tasks: obstacle avoidance, localization, mapping, and, subsequently, path-planning. In this paper, we will provide a qualitative and quantitative analysis of the effect of perturbed sensing capability of depth sensing, focusing on LIDAR, and the subsequent effects on navigation and path planning in the presence of obstacles. Aspects that will be investigated include complexity of the perturbation and effect on the autonomous operations. This work will lay a foundation for developing robust autonomy algorithms that are secure against possible degraded or inoperable sensors.

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Citation Download Citation

Anthony J. Pinar, Adam J. Webb, Jessica L. Brown, Timothy C. Havens, Brendan Alvey, Guilherme N. DeSouza, Derek T. Anderson, and Stanton R. Price "Effects of perturbed depth sensors in autonomous ground vehicles", Proc. SPIE 11746, Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications III, 117461F (12 April 2021); https://doi.org/10.1117/12.2587515

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