Victor-Emil Neagoe

Papers

Total Citations

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About

Victor-Emil Neagoe is a pioneering researcher in autonomous vehicle navigation and neural network architectures, with a focus on visual road-following systems. His most influential work centers on the development of the Concurrent Self-Organizing Maps (CSOM) classifier, a novel neural network approach that enables autonomous vehicles to visually identify road direction with high accuracy. In his 2008 paper, Neagoe introduced the CSOM as a winner-takes-all collection of neural modules, demonstrating its effectiveness for road following—a contribution that has garnered 5 citations and laid the groundwork for subsequent advances. His 2010 follow-up study further refined this method, comparing CSOM against other neural classifiers like the Multilayer Perceptron, solidifying its role as a robust solution for real-time navigation. Neagoe’s work is notable for its practical impact on autonomous driving technology, offering a computationally efficient alternative to traditional approaches. With a career dedicated to bridging neural computation and vehicular autonomy, his research continues to inspire innovations in intelligent transportation systems, making him a key figure in the evolution of self-driving vehicle perception.