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PAF Update 11/2/05. New Results since last time. New addition: Manos Pontikakis Further refined data from “dark drowsy driver study” Parsing out Usable vs. Nonusable video data Objective techniques to improve the video signal itself New types of learning algorithms

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Presentation Transcript
new results since last time
New Results since last time
  • New addition: Manos Pontikakis
  • Further refined data from “dark drowsy driver study”
    • Parsing out Usable vs. Nonusable video data
    • Objective techniques to improve the video signal itself
    • New types of learning algorithms
    • New types of inputs to the algorithms
    • Differently timed PAFs
  • New study, 20 Stanford Undergraduates
    • Brightly lit conditions
    • Much more usable data
    • Very reliably getting about 8 percentage points above chance with PAF
    • Trying different temporal windows (10-20 seconds working best)
future directions
Future Directions
  • Run more subjects under different conditions
    • Lighting, camera type, driving course details, etc.
  • Refine algorithm
    • Replicate
    • Test for individual differences
    • Test in “real time”
  • Different Subject Groups?
  • Focus more on within-driver
    • Bring back the same people?
  • Test awareness of PAF
    • Less accidents?
  • Subjective of videos
  • Other person-attribute driving behaviors for paf