Developing a Simple Autonomous Vehicle Simulation

For a mini project focused on developing a simple autonomous vehicle simulation, you can create a basic environment using Python and libraries like Pygame or Unity for visualization. Start by designing a 2D or 3D map with obstacles, roads, and traffic signals. Implement a simple agent representing the vehicle, utilizing machine learning algorithms such as Q-learning for decision-making. The vehicle should learn to navigate the environment by receiving rewards for reaching its destination while avoiding collisions. Incorporate sensors that simulate LIDAR or cameras to detect nearby obstacles, feeding data into the AI model to improve navigation strategies. To enhance realism, you can introduce varying traffic patterns or dynamic obstacles. Finally, visualize the vehicle’s path and learning progress, allowing for adjustments and iterations on the model based on performance metrics. This project provides a hands-on approach to understanding autonomous navigation and machine learning principles.