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.
Deep Reinforcement Learning for Automated Car Parking
Lazzaroni L.;Bellotti F.;Capello A.;Cossu M.;De Gloria A.;Berta R.
2023-01-01
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.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.