Kritika Rana
Papers
2
Total Citations
7
H-Index
2
About
Kritika Rana is a researcher at the forefront of autonomous vehicle technology, specializing in sensor fusion, Kalman filtering, and machine learning for unmanned driving systems. Her work addresses critical challenges in how autonomous vehicles perceive and navigate their environment. In her highly cited 2021 study, "Comparative study of Automotive Sensor technologies used for Unmanned Driving," Rana systematically evaluated the strengths and limitations of various sensors—such as LiDAR, radar, and cameras—providing a foundational framework for optimizing sensor suites in self-driving cars. Her follow-up work, "Simulation based vehicle movement tracking using Kalman Filter algorithm for Autonomous vehicles," demonstrates the enduring relevance of the Kalman filter in modern autonomous systems. Rana showed how this classic algorithm can be effectively integrated with advanced machine learning techniques to fuse noisy sensor data, enabling precise real-time vehicle tracking and state estimation. With a combined citation count of 7 for these key papers, her research is gaining traction among engineers and academics working to improve the safety and reliability of autonomous driving. Rana's contributions are particularly valuable for students and researchers seeking to understand the practical intersection of traditional control theory and contemporary AI in the rapidly evolving field of self-driving technology.
Research Focus
Key Achievements
Top Papers
- 1
- 2