Sensor-based assessment of the in-situ quality of human computer interaction in the cars : final research report.

Details

• Creators:
  Kim, SeungJun ; Dey, Anind K.
• Corporate Creators:
  Carnegie-Mellon University. Technologies for Safe and Efficient Transportation University Transportation Center ; University of Pennsylvania
• Corporate Contributors:
  United States. Dept. of Transportation. Research and Innovative Technology Administration ; University Transportation Centers Program (U.S.)
• Subject/TRT Terms:
  • Attention
  • Automatic data collection systems
  • Cognition
  • Drivers
  • Evaluation and assessment
  • Human machine systems
  • Sensors
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final research report
• Corporate Publisher:
  Carnegie-Mellon University. Technologies for Safe and Efficient Transportation University Transportation Center
• Abstract:
  Human attention is a finite resource. When interrupted while performing a task, this ; resource is split between two interactive tasks. People have to decide whether the benefits ; from the interruptive interaction will be enough to offset the loss of attention from the ; ongoing task. ; The issue of dealing with self-interruptions and external interruptions is particularly critical ; in driving situations. In general, interruptions result in a time lag before users resume their ; primary task, increase mental workload, and thus decrease primary task performance. ; Therefore, being able to identify when a driver is interruptible is critical for building ; systems that can mediate these interruptions. ; In order to identify situations in which drivers enter either low or high cognitive load states ; during naturalistic dring (i.e., opportune moments for driver interruption – e.g., more ; interruptible states vs. less interruptible states), we have examined a broad range of sensor ; data streams to understand real-time driver/driving states (e.g., motion capture, peripheral ; interaction monitoring, psycho-physiological responses, etc.), and presented a modelbased ; driver/driving assessment by using machine learning technology.
  More +
• Format:
  PDF
• Funding:
  DTRT12GUTG11
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/31305/dot_31305_DS1.pdf
• File Type:
  [PDF - 3.57 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:600e8734335e60bb081060920c6ea4b27e4f5e0cbe9e4c5725c38633058078cc