Uday Uthkota

About

Uday Uthkota is a researcher specializing in computer vision and autonomous systems, with a focused expertise in intelligent transportation and self-driving vehicle technologies. His most recognized contribution centers on advancing lane detection methodologies, where he proposed a novel and pragmatic approach leveraging Convolutional Neural Networks (CNNs) built upon the SegNet encoder-decoder architecture. By integrating Google Street View data with deep learning techniques, Uthkota demonstrated a dynamic, real-world applicable solution to one of the most critical challenges in autonomous navigation — accurate and reliable lane identification across diverse road conditions. His 2019 paper on this topic has garnered nearly 30 citations, reflecting meaningful engagement from the broader autonomous systems and machine learning research communities. The work stands out for its practical orientation, bridging the gap between theoretical deep learning models and deployable navigation systems. Uthkota's research contributes to the growing body of knowledge enabling fully assistive and autonomous vehicles, addressing safety-critical infrastructure that underpins next-generation transportation. His focus on scalable, data-driven perception systems positions him as a contributor to the rapidly evolving field of intelligent mobility and computer vision applications.

Research Focus

Key Achievements

Top Papers

1. 1
   Dynamic Approach for Lane Detection using Google Street View and CNN

   22 citations · 2019
2. 2
   Dynamic Approach for Lane Detection using Google Street View and CNN

   6 citations · 2019

Key Collaborators

Contact & Links