Development of deep-learning-based autonomous agents for low-speed maneuvering in Unity

Development of deep-learning-based autonomous agents for low-speed maneuvering in Unity

This study provides a systematic analysis of the resource-consuming training of deep reinforcement-learning (DRL) agents for simulated low-speed automated driving (AD). In Unity, this study established two case studies: garage parking and navigating an obstacle-dense area. Our analysis involves trai...

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Bibliographic Details
Main Authors: Riccardo Berta, Luca Lazzaroni, Alessio Capello, Marianna Cossu, Luca Forneris, Alessandro Pighetti, Francesco Bellotti
Format: Article
Language:English
Published: Tsinghua University Press 2024-09-01
Series:Journal of Intelligent and Connected Vehicles
Subjects:
automated driving
autonomous agents
deep reinforcement learning
curriculum learning
modeling and simulation
Online Access:https://www.sciopen.com/article/10.26599/JICV.2023.9210039
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https://www.sciopen.com/article/10.26599/JICV.2023.9210039

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