Driver-Activity Recognition in the Context of Conditionally Autonomous Driving

This paper presents a novel approach to automated recognition of the driver's activity, which is a crucial factor for determining the take-over readiness in conditionally autonomous driving scenarios. Therefore, an architecture based on head-and eye-tracking data is introduced in this study and several features are analyzed. The proposed approach is evaluated on data recorded during a driving simulator study with 73 subjects performing different secondary tasks while driving in an autonomous setting. The proposed architecture shows promising results towards in-vehicle driver-activity recognition. Furthermore, a significant improvement in the classification performance is demonstrated due to the consideration of novel features derived especially for the autonomous driving context.

Language

  • English

Media Info

  • Media Type: Web
  • Features: References;
  • Pagination: pp 1652-1657
  • Monograph Title: 18th International IEEE Conference on Intelligent Transportation Systems (ITSC 2015)

Subject/Index Terms

Filing Info

  • Accession Number: 01603009
  • Record Type: Publication
  • ISBN: 9781467365956
  • Files: TRIS
  • Created Date: May 2 2016 3:17PM