Tek Raj Chhetri

About

Tek Raj Chhetri is a researcher working at the intersection of autonomous systems, multimodal sensing, and knowledge representation technologies. His most prominent work centers on developing robust frameworks for autonomous vehicles, particularly his influential research on end-to-end multimodal sensor dataset collection, which integrates camera, LiDAR, and radar systems to enhance perceptual reliability and safety in complex driving conditions. This work has garnered significant attention, accumulating over 20 citations across multiple publications and establishing Chhetri as a contributor to the growing field of autonomous driving infrastructure. Beyond vehicular perception, Chhetri has demonstrated a breadth of expertise in semantic web technologies, exploring how ontologies and knowledge graphs can enhance decision-making processes in intelligent systems — a contribution that bridges artificial intelligence with structured data reasoning. His 2021 work in this area highlights his interest in making machine decision-making more transparent and context-aware. Taken together, Chhetri's research reflects a unifying vision: building smarter, safer, and more semantically aware autonomous systems. His work is particularly valuable for researchers exploring sensor fusion pipelines, dataset standardization for self-driving vehicles, and the application of semantic technologies to real-world AI challenges.

Research Focus

Key Achievements

Top Papers

1. 1
   End-to-End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles

   13 citations · 2023
2. 2
   Improving Decision Making Using Semantic Web Technologies

   6 citations · 2021
3. 3
   End-to-End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles

   4 citations · 2023
4. 4
   End-to-End Multimodal Sensor Dataset Collection Framework for Autonomous Vehicles

   3 citations · 2023

Key Collaborators

Contact & Links