COHORT 4

The Office of Research, Innovation, and Commercialization (ORIC) is pleased to announce the selection of various projects for funding, with each project receiving a grant of PKR 50,000. These projects have been chosen based on their potential impact, innovation, and alignment with our mission to foster commercialization. Detailed information about each funded project, showcasing the diverse range of ideas and initiatives that contribute to our community’s growth and advancement, is provided below.

The Office of Research, Innovation, and Commercialization (ORIC) is pleased to announce the selection of various projects for funding, with each project receiving a grant of PKR 50,000. These projects have been chosen based on their potential impact, innovation, and alignment with our mission to foster commercialization. Detailed information about each funded project, showcasing the diverse range of ideas and initiatives that contribute to our community’s growth and advancement, is provided below.

Group Member Names

Muhammad Mudassar (BSE213195)
Muhammad Faizan Satti (BSE213168)
Muhammad Waleed (BSE213196)

Supervisor Name: Mr. Syed Awais Haider

Project Description

The Smart Solar Vehicle is an eco-friendly, autonomous prototype designed to tackle campus transportation challenges using solar energy. It integrates Raspberry Pi 4, ultrasonic sensors, a GPS module, and a camera for real-time path planning and obstacle detection. The system operates through a Flask-based interface enabling manual control and live monitoring. YOLOv11, a deep-learning model, allows the vehicle to identify and respond to pedestrians and road conditions effectively. The goal is to promote green mobility solutions by combining embedded systems, computer vision, and sustainable energy. The project demonstrates a practical model for localized, low-speed transportation in universities and industrial parks.

Features

1. Autonomous navigation with GPS support
2. Obstacle detection using ultrasonic sensors
3. Real-time object detection using YOLOv11
4. Solar energy-powered operation
5. Web-based control interface via Flask
6. Sensor data logging and live tracking
7. Secure access via password-protected dashboard
8. Modular system architecture for scalability

Unique Features

1. Integration of YOLOv11 for real-time pedestrian recognition
2. Kalman Filter implementation for sensor data accuracy
3. Solar panel-powered system with energy efficiency modeling
4. Web-based live camera stream and sensor monitoring
5. Multithreaded control to manage real-time sensor and vision inputs
6. Use of lightweight, weatherproof ultrasonic sensors (AJ-SR04M)
7. Fused sensor and vision-based decision making for obstacle avoidance

Technical Description

The hardware includes Raspberry Pi 4 Model B as the central processor, interfacing with AJ-SR04M and HC-SR04 ultrasonic sensors, a 5MP Pi Camera, and a NEO-6M GPS module. Power is supplied by a 50W solar panel and a rechargeable battery setup. The software stack includes Python 3.9 with OpenCV for image processing, Flask for dashboard interface, and TensorFlow Lite for YOLOv5 object detection. The control logic is divided into modular services for vision, sensors, and navigation. The system logs data using SQLite and displays real-time values through a web dashboard. The prototype is tested under real-world scenarios with active path detection and live sensor fusion using Kalman Filters.