Towards Explainable AI System For Traffic Sign Recognition And Deployment In A Simulated Environment

Abstract

1. Introduction

Recent advances in Machine Learning are pushing the boundaries of real world AI applications and are becoming more and more integrated in our daily lives. As research advances, the long-term goal of autonomous driving becomes rather reality than fiction, but brings with it numerous challenges for our society and the system engineers. Topics like ethical AI, privacy and transparency are becoming increasingly relevant and have to be considered when designing AI systems with real world applications. In this project we present our first attempt at tackling the problem of traffic sign recognition by taking a systems engineering approach. We focus on the development of an explainable and transparent system with the use of concept whitening layers inside our deep learning models, which allow us to train our models with handcrafted features and make predictions based on predefined concepts. We also introduce a simulation environment for traffic sign recognition, which can be used for model deployment, performance benchmarking, data generation and further domain model research.

2. Objective

This project is part of the CS course 'Systems Engineering Meets Life Sciences I' at Goethe University Frankfurt. In this Computer Vision project, we present our first attempt at tackling the problem of traffic sign recognition using a systems engineering approach.

This project demonstrates how C#, Computer Vision, PyTorch can be applied to a real-world AI & Machine Learning problem.

4. Technology Stack

• Python 3
• PyTorch Framework

5. System Requirements

General requirements for this technology stack — check the README for exact versions.

• .NET SDK / Visual Studio
• Python 3.8 or later
• pip / virtualenv for dependencies
• VS Code, PyCharm or Jupyter Notebook
• Python 3.8 or later with Jupyter Notebook / JupyterLab (or Google Colab)
• pip for dependencies
• Git (to clone the repository)

6. Installation & Setup

git clone https://github.com/alen-smajic/Towards-Explainable-AI-System-for-Traffic-Sign-Recognition-and-Deployment-in-a-Simulated-Environment.git
cd Towards-Explainable-AI-System-for-Traffic-Sign-Recognition-and-Deployment-in-a-Simulated-Environment

Full setup instructions are in the project README.

7. Future Enhancements

Suggested extensions you can add to make this your own project.

• Deploy the model as a web app with Streamlit, Flask or FastAPI
• Compare against an additional model and report the metric difference
• Add explainability (SHAP / Grad-CAM)

8. Viva / Review Questions

Common questions examiners ask for projects in this domain.

1. What dataset does the project use and how was it pre-processed?
2. Which algorithm / model architecture is used and why was it chosen over alternatives?
3. How are training and testing data split, and how is overfitting avoided?
4. Which evaluation metrics (accuracy, precision, recall, F1) are reported and what do they mean here?
5. How would you deploy this model for real users?

9. Source Code & License

This project is developed by alen-smajic and published on GitHub under the MIT License. Please follow the license terms and credit the original author when you use or modify this code.