Customizable AI-Enabled ADAS Demonstrator
Supervisor : Mohamed Atia
Team size: Minimum , Maximum
| CSE | SE | Comm | Biomed | EE | Aero | Special |
|---|---|---|---|---|---|---|
| Yes | Yes | Yes | No | Yes | Yes | No |
Description
Design and implement a low-cost Advanced Driver Assistance System (ADAS) prototype using the Jetson Nano and sensors (GPS, Camera, Radar, LiDAR, and IMU). A smartphone app will display real-time safety alerts, such as lane departure warnings, blind spot detection, and collision warnings.
This project is intended as an educational and research demonstrator. Students will integrate hardware, AI, and mobile software while also considering safety, regulatory, and market aspects of ADAS technologies.
Objectives
- Develop a multi-sensor fusion pipeline on embedded hardware.
- Implement AI-based perception algorithms optimized for Jetson Nano.
- Build a smartphone GUI with minimal driver distraction.
- Explore commercial, legal, and social implications of aftermarket ADAS.
Required Skills
- Embedded programming (C/C++, Python, Linux)
- AI/computer vision (OpenCV, TensorFlow Lite/PyTorch)
- Sensor integration and fusion (GPS, Camera, Radar, LiDAR, IMU)
- Mobile app development (Android/iOS)
Expected Outcome
A working prototype and mobile app demonstrating ADAS-like functions, plus an analysis of technical, legal, and market considerations.
Prerequisites:
- Embedded programming (C/C++, Python, Linux)
- AI/computer vision (OpenCV, TensorFlow Lite/PyTorch)
- Sensor integration and fusion (GPS, Camera, Radar, LiDAR, IMU)
- Mobile app development (Android/iOS)