Car Parking Simulation using Machine Learning

Abstract

This research paper explores the field of artificial intelligence and its subfields, including reinforcement learning, which is used in the Unity ML Agents project to create autonomous parking models using the Proximity Policy Optimization (PPO) algorithm. The models use LIDAR sensors to navigate through the environment and avoid obstacles while searching for an optimal parking spot. The findings exhibit the effectiveness of the PPO algorithm and the importance of road safety. The development of autonomous parking models provides a steppingstone towards the creation of more advanced models that can be used in real-world applications such as self-driving cars. This project covers topics such as machine learning, reinforcement learning, LIDAR sensors, PPO algorithm, and road safety, contributing to future research and development in artificial intelligence.