Evaluating end-to-end autonomous driving architectures: a proximal policy optimization approach in simulated environments

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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Bibliographic Details
Main Authors: Ângelo Morgado, Kaoru Ota, Mianxiong Dong, Nuno Pombo
Format: Article
Language:English
Published: Springer 2025-07-01
Series:Autonomous Intelligent Systems
Subjects:
Autonomous Driving Systems (ADS)
End-to-End Architecture
Software System Architecture
Proximal Policy Optimization (PPO)
Real-Time Embedded Systems
Simulation Framework
Online Access:https://doi.org/10.1007/s43684-025-00102-3
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https://doi.org/10.1007/s43684-025-00102-3

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