N.A Vidula

About

N.A. Vidula is an emerging researcher at the forefront of autonomous driving and computer vision, with a focus on developing cost-effective, vision-based solutions for real-world navigation. Their most notable contribution is the pioneering framework "A Low-Cost Vision-Based Framework for Autonomous Driving Using YOLO, MiDaS, and Stereo Vision" (2025), which integrates state-of-the-art object detection (YOLO), depth estimation (MiDaS), and stereo vision to enable robust perception without expensive LiDAR systems. This work addresses a critical barrier in autonomous vehicle deployment—cost—by leveraging accessible hardware and efficient deep learning models. Though early in its citation trajectory (2 citations), the paper signals Vidula’s potential to influence scalable, affordable autonomy. Their research bridges practical engineering and algorithmic innovation, targeting safer, more equitable transportation. Vidula’s work is particularly relevant for students and researchers exploring low-resource autonomous systems, offering a blueprint for integrating multiple vision modalities. As the field shifts toward democratized self-driving technology, Vidula’s contributions stand out for their pragmatic, systems-level thinking.

Research Focus

Key Achievements

Top Papers

1. 1
   A Low-Cost Vision-Based Framework for Autonomous Driving Using YOLO, MiDaS, and Stereo Vision

   2 citations · 2025

Key Collaborators

Contact & Links