Self-Parking Car Simulation using Reinforcement Learning Approach for Moderate Complexity Parking Scenario

Abstract

Autonomous parking system is essential in reducing time for waiting to park in the parking spaces or looking for space, particularly in the smart city. We propose the auto-parking car simulation framework using proximal policy optimization (PPO) for deep reinforcement learning in a moderately complex parking scenario which comprises basic and challenging zones for parking. Different configurations are explored including sparse and dense rewards combining with checkpoints, orientation reward and stay-in-collision punishment. It shows high success parking rate in a basic parking zone up to at more than 95%. For the more difficult parking zone, the model works better when each zone is trained separately.