ARAD: Automated and Real-Time Anomaly Detection in Sensors of Autonomous Vehicles Through a Lightweight Supervised Learning Approach

In this paper, an automated and real-time anomaly detection approach for sensors of autonomous vehicles called ARAD is presented. Automated vehicles gather environmental information through their diverse built-in sensors thus the correctness of this data affects the system’s reliability, dire...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Athena Abdi, Arash Ghasemi-Tabar
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	IEEE 2024-01-01
Sarja:	IEEE Access
Aiheet:	Anomaly detection autonomous vehicles data-driven learning fault tolerance sensors
Linkit:	https://ieeexplore.ieee.org/document/10574834/
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