Papers
14
Total Citations
151
H-Index
6
About
Azim Eskandarian is a prominent researcher specializing in autonomous and connected vehicles, cooperative control systems, and intelligent transportation. His work spans several decades, beginning with foundational contributions in neural network-based robotic dynamics modeling in the 1990s and evolving into cutting-edge research on connected autonomous vehicles (CAVs) and multi-agent systems. Eskandarian's most impactful contributions center on Cooperative Adaptive Cruise Control (CACC), where his team developed robust control frameworks — including adaptive Kalman filter approaches to handle temporary communication loss — earning over 55 citations for a single study. His research on cooperative perception addresses a critical limitation of autonomous vehicles: sensor occlusion and blind spots, tackled through vehicle-to-vehicle communication strategies. A hallmark of his methodology is the use of scaled experimental platforms, including mobile robot testbeds and the XTENTH-CAR all-terrain vehicle, to safely validate real-world connected autonomy concepts. More recently, he has explored deep reinforcement learning for cooperative formation control in multi-agent systems, reflecting his commitment to advancing next-generation autonomous intelligence. With contributions spanning vehicle platooning, sensor fusion, and experimental CAV platforms, Eskandarian's body of work provides both theoretical foundations and practical tools that continue to shape the future of intelligent transportation systems.
Research Focus
Key Achievements
Top Papers
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- 3Dynamics modeling of robotic manipulators using an artificial neural network14 citations · 1994
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- 9An Improved Small-Scale Connected Autonomous Vehicle Platform4 citations · 2019
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