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Abdelrahman Khalil

4 accepted papers

2023

Multi-Head Attention Machine Learning for Fault Classification in Mixed Autonomous and Human-Driven Vehicle Platoons

ICRA 2023poster

Connected Autonomous Vehicle (CAV) platoons have been extensively studied to protect against cyber and physical vulnerabilities. Faults can occur in all layers of the platoon system or could be introduced by impaired human drivers. Since different types of faults may require different fault resoluti…

Cited by 2SourceScholar
2022

Transmissibility-based DAgger For Fault Classification in Connected Autonomous Vehicles

IROS 2022poster

Fault mitigation in Connected Autonomous Vehicle (CAV) platoons is faster and more reliable if the fault structure is known. In this paper we propose using transmissibility operators, which are relationships that relate a set of velocities with another in the platoon, to classify the faults. Transmi…

Cited by 0SourceScholar
2021

On Fault Classification in Connected Autonomous Vehicles Using Supervised Machine Learning

IROS 2021poster

Different health-monitoring techniques were considered in the literature to enhance the safety and stability of Connected Autonomous Vehicle (CAV) platoons. The health-monitoring processes include fault detection, localization, and mitigation. It is evident that mitigating these faults is faster and…

Cited by 10SourceScholar
2020

Output-Only Fault Detection and Mitigation of Networks of Autonomous Vehicles

IROS 2020poster

An autonomous vehicle platoon is a network of autonomous vehicles that communicate together to move in a desired way. One of the greatest threats to the operation of an autonomous vehicle platoon is the failure of either a physical component of a vehicle or a communication link between two vehicles.…

Cited by 12SourceScholar