publication . Other literature type . Conference object . Preprint . 2015

DeepDriving: Learning Affordance for Direct Perception in Autonomous Driving

Chen, Chenyi; Seff, Ari; Kornhauser, Alain; Xiao, Jianxiong;
  • Published: 01 May 2015
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Abstract
Today, there are two major paradigms for vision-based autonomous driving systems: mediated perception approaches that parse an entire scene to make a driving decision, and behavior reflex approaches that directly map an input image to a driving action by a regressor. In this paper, we propose a third paradigm: a direct perception approach to estimate the affordance for driving. We propose to map an input image to a small number of key perception indicators that directly relate to the affordance of a road/traffic state for driving. Our representation provides a set of compact yet complete descriptions of the scene to enable a simple controller to drive autonomous...
Subjects
free text keywords: Perception, media_common.quotation_subject, media_common, Parsing, computer.software_genre, computer, Affordance, Control theory, Convolutional neural network, Abstraction, Computer science, Computer vision, Artificial intelligence, business.industry, business, Source code, Computer Science - Computer Vision and Pattern Recognition
Related Organizations
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