Abdelrahman Khalil
Papers
9
Total Citations
112
H-Index
7
About
Abdelrahman Khalil is a pioneering researcher at the intersection of autonomous vehicle safety, structural health monitoring, and intelligent transportation systems. His work centers on developing robust fault detection, localization, and mitigation frameworks for connected autonomous vehicle (CAV) platoons — networks of self-driving vehicles that communicate cooperatively to maintain safe, coordinated motion. Khalil's most significant contribution is his innovative application of **transmissibility operators** to vehicle health monitoring — a powerful output-only mathematical approach that identifies faults without requiring knowledge of external excitations or full system dynamics. This elegant methodology, detailed in his most-cited works (26 and 21 citations respectively), enables real-time fault management even in complex, mixed traffic environments involving unpredictable human drivers. His research on detecting impaired drivers within autonomous platoons using only velocity measurements further demonstrates the practical safety implications of his work. Khalil has also pioneered machine learning integration into CAV health monitoring, employing supervised learning and multi-head attention architectures to classify faults rapidly and accurately. Collectively accumulating over 110 citations, his body of work addresses critical cybersecurity and physical vulnerability challenges that must be solved before autonomous vehicles achieve widespread deployment, making him a significant emerging voice in intelligent transportation safety research.
Research Focus
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Top Papers
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