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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| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Tsinghua University Press
2024-09-01
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| Series: | Journal of Intelligent and Connected Vehicles |
| Subjects: | |
| Online Access: | https://www.sciopen.com/article/10.26599/JICV.2023.9210039 |
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