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Erschienen in:
2023 | OriginalPaper | Buchkapitel
Deep Reinforcement Learning for Automated Car Parking
verfasst von : Luca Lazzaroni, Francesco Bellotti, Alessio Capello, Marianna Cossu, Alessandro De Gloria, Riccardo Berta
Erschienen in: Applications in Electronics Pervading Industry, Environment and Society
Verlag: Springer Nature Switzerland
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Abstract
This article explores the development of a Deep Reinforcement Learning (DRL) -based agent able to perform both path planning and trajectory execution, processing sensor perception information and directly controlling the steering wheel and the acceleration, like a normal driver. As a preliminary investigation, we limit our research to low-speed manoeuvers, in a challenging narrow drivable area. The vehicle’s agent completely relies on the real-time information from the sensors, thus avoiding the need of a map. We show the validity of the proposed system in a simulated car parking test, in which the agent has been able to achieve high target reach rates, with a limited number of manoeuvers (gear inversion rate), outperforming the well-established Hybrid A-Star path planning algorithm in both the metrics. Further research is needed for improving the generalization ability of the agent and its application in more dynamic driving environments.