Paul T Sheeba
Papers
1
Total Citations
15
H-Index
1
About
Paul T Sheeba is a researcher at the forefront of intelligent transportation systems, with a primary focus on deep learning and computer vision for advanced driver assistance (ADAS). Her most influential work, "DeepDrive: A braking decision making approach using optimized GAN and Deep CNN for advanced driver assistance systems" (2023), has already garnered 15 citations, signaling its early impact in the field. In this study, Sheeba pioneered a novel framework that integrates Generative Adversarial Networks (GANs) with optimized Deep Convolutional Neural Networks (CNNs) to enhance real-time braking decisions—a critical component for autonomous vehicle safety. By addressing the challenge of precise, context-aware braking in complex driving scenarios, her contribution helps bridge the gap between simulation and real-world deployment of ADAS technologies. Sheeba’s work stands out for its innovative fusion of generative and discriminative models, offering a robust solution to reduce false positives and improve response times. As a rising voice in vehicular AI, her research not only advances autonomous driving safety but also inspires new directions in sensor fusion and decision-making algorithms. With a growing citation record, Sheeba is poised to become a key figure in the evolution of intelligent, safer roads.
Research Focus
Key Achievements
Top Papers
- 1