Alexander Quezada
Papers
1
Total Citations
6
H-Index
1
About
Alexander Quezada is a rising researcher at the intersection of autonomous vehicle control and computer vision, with a particular focus on making self-driving technology more robust and accessible. His most cited work, "Developing, Analyzing, and Evaluating Vehicular Lane Keeping Algorithms Using Electric Vehicles" (2022), stands out not only for its technical contributions but for its unique origin: it was led by a team of four undergraduate students as part of a Research Experience for Undergraduates (REU) program. This paper, which has garnered 6 citations, systematically designed and evaluated multiple lane-following algorithms using computer vision on electric vehicles, addressing one of the core challenges in automated driving—reliable lane keeping under real-world conditions. Quezada’s work demonstrates that high-impact research can emerge from undergraduate-led initiatives, and his approach emphasizes hands-on, experimental validation over purely theoretical modeling. By empowering early-career students to tackle complex control problems, he is helping to democratize autonomous vehicle research and train the next generation of engineers. His contributions are particularly notable for bridging the gap between algorithm development and practical deployment on electric vehicle platforms.
Research Focus
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Top Papers
- 1