Autonomous car parking Model for different types of parking lots using deep reinforcement learning
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Дата
2025
Автори
Науковий керівник
Назва журналу
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Назва тому
Видавець
КПІ ім. Ігоря Сікорського
Анотація
This article explores the simulation of automated parking in a virtual environment with various types of parking lots. The objective of this research is to develop an intelligent model for autonomous parking that achieves high efficiency under simulated conditions across a broad range of common parking lot types. We use a deep reinforcement learning approach using the Proximal Policy Optimization (PPO) algorithm, complemented by Behavioural Cloning and Generative Adversarial Imitation Learning. Our fine-tuned model achieves state-of-the-art parking accuracy, ranging from 96.3% to 99.34%, depending on the type of parking lot. The developed simulation environment, based on the Unity game engine and the MLAgents plugin, enables high-quality visualization, simulation, and modelling capabilities, making it valuable for both educational and research purposes.
Опис
Ключові слова
automatic parking, self-driving car, deep reinforcement learning, Proximal Policy Optimization, ML-Agents, Unity, virtual environment
Бібліографічний опис
Oliinyk, V. Autonomous car parking Model for different types of parking lots using deep reinforcement learning / V. Oliinyk, Y. Danyliuk // Адаптивні системи автоматичного управління : міжвідомчий науково-технічний збірник. – 2025. – № 1 (46). – С. 237-246. – Бібліогр.: 18 назв.