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Author (up) Yi Xiao; Felipe Codevilla; Christopher Pal; Antonio Lopez edit   pdf
openurl 
  Title Action-Based Representation Learning for Autonomous Driving Type Conference Article
  Year 2020 Publication Conference on Robot Learning Abbreviated Journal  
  Volume Issue Pages  
  Keywords  
  Abstract Human drivers produce a vast amount of data which could, in principle, be used to improve autonomous driving systems. Unfortunately, seemingly straightforward approaches for creating end-to-end driving models that map sensor data directly into driving actions are problematic in terms of interpretability, and typically have significant difficulty dealing with spurious correlations. Alternatively, we propose to use this kind of action-based driving data for learning representations. Our experiments show that an affordance-based driving model pre-trained with this approach can leverage a relatively small amount of weakly annotated imagery and outperform pure end-to-end driving models, while being more interpretable. Further, we demonstrate how this strategy outperforms previous methods based on learning inverse dynamics models as well as other methods based on heavy human supervision (ImageNet).  
  Address virtual; November 2020  
  Corporate Author Thesis  
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  Language Summary Language Original Title  
  Series Editor Series Title Abbreviated Series Title  
  Series Volume Series Issue Edition  
  ISSN ISBN Medium  
  Area Expedition Conference CORL  
  Notes ADAS; 600.118 Approved no  
  Call Number Admin @ si @ XCP2020 Serial 3487  
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