Online Driver Distraction Detection Using Long Short-Term Memory

Lane-keeping assistance systems for vehicles may be more acceptable to users if the assistance was adaptive to the driver's state. To adapt systems in this way, a method for detection of driver distraction is needed. A novel technique is proposed for online detection of driver's distraction, modeling the long-range temporal context of driving and head tracking data. Long short-term memory (LSTM) recurrent neural networks enable a reliable subject-independent detection of inattention with an accuracy of up to 96.6 percent. The LSTM framework significantly outperforms conventional approaches such as support vector machines.

• Record URL:
  https://doi.org/10.1109/TITS.2011.2119483
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/oclc/41297384
• Supplemental Notes:
  • Abstract reprinted with permission of IEEE.
• Authors:
  • Wollmer, Martin
  • Blaschke, Christoph
  • Schindl, Thomas
  • Schuller, Bjorn
  • Farber, Berthold
  • Mayer, Stefan
  • Trefflich, Benjamin
• Publication Date: 2011

Language

• English

Media Info

• Media Type: Web
• Features: Figures; References; Tables;
• Pagination: pp 574-582
• Serial:
  • IEEE Transactions on Intelligent Transportation Systems
  • Volume: 12
  • Issue Number: 2
  • Publisher: Institute of Electrical and Electronics Engineers (IEEE)
  • ISSN: 1524-9050
  • Serial URL: http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=6979

Subject/Index Terms

• TRT Terms: Accuracy; Distraction; Driver support systems; High risk drivers; Lane lines; Memory; Neural networks; Traffic lanes
• Subject Areas: Data and Information Technology; Highways; I71: Traffic Theory;

Filing Info

• Accession Number: 01352222
• Record Type: Publication
• Source Agency: UC Berkeley Transportation Library
• Files: TLIB
• Created Date: Sep 21 2011 7:13AM