Evaluating end-to-end autonomous driving architectures: a proximal policy optimization approach in simulated environments
Abstract Autonomous driving systems (ADS) are at the forefront of technological innovation, promising enhanced safety, efficiency, and convenience in transportation. This study investigates the potential of end-to-end reinforcement learning (RL) architectures for ADS, specifically focusing on a Go-T...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Springer
2025-07-01
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| Series: | Autonomous Intelligent Systems |
| Subjects: | |
| Online Access: | https://doi.org/10.1007/s43684-025-00102-3 |
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