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Type Conference or Workshop Paper
Scope Discipline-based scholarship
Published in Proceedings Yes
Title Event-Based Vision Meets Deep Learning on Steering Prediction for Self-Driving Cars
Organization Unit
Authors
  • Ana I. Maqueda
  • Antonio Loquercio
  • Guillermo Gallego
  • Narciso Garcia
  • Davide Scaramuzza
Presentation Type paper
Item Subtype Original Work
Refereed Yes
Status Published in final form
Language
  • English
ISBN 978-1-5386-6420-9
Page Range 5419 - 5427
Event Title 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Event Type conference
Event Location Salt Lake City, UT
Event Start Date July 18 - 2018
Event End Date July 23 - 2018
Publisher IEEE
Abstract Text Event cameras are bio-inspired vision sensors that naturally capture the dynamics of a scene, filtering out redundant information. This paper presents a deep neural network approach that unlocks the potential of event cameras on a challenging motion-estimation task: prediction of a vehicle’s steering angle. To make the best out of this sensor–algorithm combination, we adapt state-of-the-art convolutional architectures to the output of event sensors and extensively evaluate the performance of our approach on a publicly available large scale event-camera dataset (1000 km). We present qualitative and quantitative explanations of why event cameras allow robust steering prediction even in cases where traditional cameras fail, e.g. challenging illumination conditions and fast motion. Finally, we demonstrate the advantages of leveraging transfer learning from traditional to event-based vision, and show that our approach outperforms state-of-the-art algorithms based on standard cameras.
Official URL http://rpg.ifi.uzh.ch/docs/CVPR18_Maqueda.pdf
Digital Object Identifier 10.1109/cvpr.2018.00568
Other Identification Number merlin-id:18682
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