Simulation-based reinforcement learning for real-world autonomous driving

• URL: http://arxiv.org/abs/1911.12905v4
• Date: Wed, 3 Apr 2024 14:35:49 GMT
• Title: Simulation-based reinforcement learning for real-world autonomous driving
• Authors: Błażej Osiński, Adam Jakubowski, Piotr Miłoś, Paweł Zięcina, Christopher Galias, Silviu Homoceanu, Henryk Michalewski,
• Abstract summary: We use reinforcement learning in simulation to obtain a driving system controlling a full-size real-world vehicle. The driving policy takes RGB images from a single camera and their semantic segmentation as input. We use mostly synthetic data, with labelled real-world data appearing only in the training of the segmentation network.
• Score: 9.773015744446067
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We use reinforcement learning in simulation to obtain a driving system controlling a full-size real-world vehicle. The driving policy takes RGB images from a single camera and their semantic segmentation as input. We use mostly synthetic data, with labelled real-world data appearing only in the training of the segmentation network. Using reinforcement learning in simulation and synthetic data is motivated by lowering costs and engineering effort. In real-world experiments we confirm that we achieved successful sim-to-real policy transfer. Based on the extensive evaluation, we analyze how design decisions about perception, control, and training impact the real-world performance.

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