A
Amin Tahmasbi-Sarvestani
Researcher at West Virginia University
Publications - 20
Citations - 386
Amin Tahmasbi-Sarvestani is an academic researcher from West Virginia University. The author has contributed to research in topics: Cooperative Adaptive Cruise Control & Situation awareness. The author has an hindex of 8, co-authored 20 publications receiving 245 citations. Previous affiliations of Amin Tahmasbi-Sarvestani include Honda & Honda R&D Americas.
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Journal ArticleDOI
Utilizing Model-Based Communication and Control for Cooperative Automated Vehicle Applications
Ehsan Moradi-Pari,Hossein Nourkhiz Mahjoub,Hadi Kazemi,Yaser P. Fallah,Amin Tahmasbi-Sarvestani +4 more
TL;DR: This work utilizes the recently introduced concept of model-based communication and design a new strategy based on small- and large-scale modeling of vehicle movement dynamics, which is compared with the conventional design of cooperative adaptive cruise control.
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Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application
Amin Tahmasbi-Sarvestani,Hossein Nourkhiz Mahjoub,Yaser P. Fallah,Ehsan Moradi-Pari,Oubada Abuchaar +4 more
TL;DR: An end-to-end Vehicle- to-Pedestrian (V2P) framework to provide situational awareness and hazard detection based on the most common and injury-prone crash scenarios and a mitigating solution for congestion and power consumption issues in such systems.
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A Learning-Based Stochastic MPC Design for Cooperative Adaptive Cruise Control to Handle Interfering Vehicles
TL;DR: In this paper, a neural-network-based cut-in detection and trajectory prediction scheme is proposed for cooperative adaptive cruise control (CACC) which is an automated application, providing drivers with extra benefits such as traffic throughput maximization and collision avoidance.
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A Hybrid Systems Approach to Modeling Real-Time Situation-Awareness Component of Networked Crash Avoidance Systems
TL;DR: A new approach is introduced for modeling the estimation and networking processes of networked crash/collision avoidance systems (NCAS) in a single framework that employs probabilistic timed automata to model the networking component and a hybrid automata is used to combine and model both networking and estimation components in asingle framework.
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Network-Aware Double-Layer Distance-Dependent Broadcast Protocol for VANETs
TL;DR: A network-aware double-layer distance-dependent protocol for fast broadcasting of aggregated traffic information over multiple hops that considerably reduce the overall latency of information while also improving the scalability and robustness of the system.