The main aim of this project is to build a smart navigation system for autonomous vehicles that can safely and efficiently operate in urban environments using Deep Q-Learning, a form of reinforcement learning. Unlike traditional rule-based driving systems or simpler machine learning models, this approach uses real-time traffic data and simulation environments to help the vehicle adapt dynamically to complex driving situations such as lane merging, obstacle avoidance, and decision-making at intersections. This project seeks to enhance the intelligence of self-driving vehicles by enabling them to learn from experience and improve over time. By the end of the project, students will have built a prototype that simulates urban driving behavior using reinforcement learning techniques, contributing to the broader field of autonomous systems.
The project is structured over a twelve-week timeline. In the initial weeks, students will study the fundamentals of Deep Q-Learning and reinforcement learning algorithms. They will then install the necessary tools such as Python, TensorFlow, or PyTorch and create or use a simulation environment to mimic urban traffic scenarios. A camera module or sensor-based simulation may also be used to emulate real-world inputs.
Once the environment is ready, students will develop and train a reinforcement learning agent to make navigation decisions, learning from simulated rewards and penalties based on driving outcomes. As the system evolves, students will improve the model’s ability to generalize across different traffic patterns. The final stages involve testing the system, documenting the project, and demonstrating the model’s ability to navigate urban environments safely and efficiently. Ethical considerations, safety protocols, and legal constraints will be part of the system design and development guidelines.
