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    Driving Policy Transfer via Modularity and Abstraction

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    Name:
    1804.09364v1.pdf
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    4.429Mb
    Format:
    PDF
    Description:
    Preprint
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    Type
    Preprint
    Authors
    Müller, Matthias
    Dosovitskiy, Alexey
    Ghanem, Bernard cc
    Koltun, Vladen
    KAUST Department
    Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
    Electrical Engineering Program
    Visual Computing Center (VCC)
    Date
    2018-04-25
    Permanent link to this record
    http://hdl.handle.net/10754/632519
    
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    Abstract
    End-to-end approaches to autonomous driving have high sample complexity and are difficult to scale to realistic urban driving. Simulation can help end-to-end driving systems by providing a cheap, safe, and diverse training environment. Yet training driving policies in simulation brings up the problem of transferring such policies to the real world. We present an approach to transferring driving policies from simulation to reality via modularity and abstraction. Our approach is inspired by classic driving systems and aims to combine the benefits of modular architectures and end-to-end deep learning approaches. The key idea is to encapsulate the driving policy such that it is not directly exposed to raw perceptual input or low-level vehicle dynamics. We evaluate the presented approach in simulated urban environments and in the real world. In particular, we transfer a driving policy trained in simulation to a 1/5-scale robotic truck that is deployed in a variety of conditions, with no finetuning, on two continents. The supplementary video can be viewed at https://youtu.be/BrMDJqI6H5U
    Publisher
    arXiv
    arXiv
    1804.09364
    Additional Links
    http://arxiv.org/abs/1804.09364v1
    http://arxiv.org/pdf/1804.09364v1
    Collections
    Preprints; Electrical Engineering Program; Visual Computing Center (VCC); Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

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