The TiHAN Testbed evaluates Autonomous Emergency Braking (AEB) and Adaptive Cruise Control (ACC) through sensor integration, leveraging radar and cameras instead of standalone systems. Radar utilizes Doppler shift and Time-of-Flight (ToF) to provide
CV2X Connectivity and Mobility Solutions: Enhance road safety with advanced features like Emergency Vehicle Warning, Speed Limit Compliance, and Pothole Detection. These innovative solutions optimize traffic management, improve vehicle communication,
The TiHAN Testbed evaluates Advanced Driver Assistance Systems (ADAS) based on active safety standards. It tests features like Automatic emergency braking, Adaptive cruise control under real-world conditions. The testbed ensures these systems meet sa
This project explores leveraging edge computing and cloud integration for remotely operating autonomous UAVs. By processing data closer to the source on edge devices, the system reduces latency, enhances real-time decision-making, and improves UAV ef
Development of a multi-sensor fusion-based perception sensing system involves integrating data from multiple sensors to enhance environmental awareness, reliability, and accuracy. This advanced system combines complementary sensor technologies, enabl
The Alula Integrated Propeller, designed to enhance the thrust of drone propellers, has been tested in the lab, where an increase in lift was observed.
Cutting-edge integration of thermography with robotics enables precise anomaly detection in indoor air conditioning pipelines. Utilizing Unmanned Offroad Ground Vehicles (UGVs) equipped with thermal and RGB cameras, this breakthrough technology revol
Open Street Map (OSM)-based navigation integrates camera and IMU sensor fusion to enhance precision and reliability in autonomous systems. By combining real-time visual data with inertial measurements, this approach enables accurate localization, rou
Depth-Enhanced Neural State Machine for Accurate Traffic Light Detection and Contextual Decision-Making in Autonomous Vehicles" introduces a novel approach combining depth perception and neural modeling. This method improves traffic light recognition
The Advanced Real-time Traffic Sign Recognition and Integration System for Autonomous Vehicle (AV) navigation uses a combination of computer vision, machine learning, and sensor fusion to identify, interpret, and respond to traffic signs in diverse a
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