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Perceived collision with an obstacle in a virtual environment

Perceived collision with an obstacle in a virtual environment. Russell L Woods, Jennifer C Shieh Laurel Bobrow, Avni Vora , James Barabas, Robert B Goldstein and Eli Peli Schepens Eye Research Institute and Harvard Medical School, Boston, MA. ARVO 2003. How do you define a collision?.

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Perceived collision with an obstacle in a virtual environment

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  1. Perceived collision with an obstacle in a virtual environment Russell L Woods, Jennifer C ShiehLaurel Bobrow, Avni Vora, James Barabas, Robert B Goldstein and Eli Peli Schepens Eye Research Institute and Harvard Medical School, Boston, MA ARVO 2003

  2. How do you define a collision? In the literature: • Center to center • No consideration of the physical size of the observer or “safe distance” • Evaluated visual information (e.g. , TTC, heading perception) or cognitive issues (e.g. search) • Way-finding

  3. Cutting, Vishton & Braren (1995) • Collision detection from relative motion of obstacle and other objects • Stick figures, sparse environment • Simulated fixation task • Center to center • Angular perspective

  4. Are potential-collision decisions based on physical size? (i.e. how big you are)

  5. The task • Walk on a treadmill (self propelled) • Rear projected screen (77 cm, 95 degrees wide) • “infinite” shopping mall corridor • Obstacle appeared at 5m or 15m for 1 second • Square pillars with images of people (30cm or 70cm wide) • Task: Would you have collided with the obstacle? • New path before each obstacle • Random angular offsets of paths

  6. Closest distance to obstacle

  7. Some obstacles crossed the path

  8. Obstacle appearance distance

  9. The task • Walk on a treadmill (self propelled) • Rear projected screen (77 cm, 85 degrees wide) • “infinite” shopping mall corridor • Obstacle appeared at 5m or 15m for 1 second • Square pillars with images of people (30cm or 70cm wide) • Task: Would you have collided with the obstacle? • New path before each obstacle • Random angular offsets of paths

  10. small obstacle, 5m, 55cm

  11. large obstacle, 15m, 100cm

  12. The task • Walk on a treadmill (self propelled) • Rear projected screen (77 cm, 85 degrees wide) • “infinite” shopping mall corridor • Obstacle appeared at 5m or 15m for 1 second • Square pillars with images of people (30cm or 70cm wide) • Task: Would you have collided with the obstacle? • New path before each obstacle • Random angular offsets of paths

  13. “Yes, collision” responses against closest distance to obstacle

  14. Kappa coefficient of association How “good” a decision?Decision quality = maximum kappa (height) How “big” do you feel? Distance withoptimal decision (highest kappa)

  15. Collision envelope varied between subjects and with obstacle distance No effect of obstacle size Z19=3.44 p<0.001 22 subjects

  16. Better decisions at smaller obstacle distance Z19=4.07 p<0.0001 22 subjects

  17. Some subjects had great difficulty at 15m

  18. Do physical characteristics matter? • Preferred walking speed, stride length • Width at shoulder and of the arms • Age • Height, weight, body mass index (BMI)

  19. Collision envelope was not predicted by physical characteristics 5m rs = 0.02, p=0.92 15m rs = 0.01, p=0.99 22 subjects

  20. Collision envelope equals body width Collision envelope was (usually) larger than measured physical characteristics 5m rs = -0.26, p=0.25 15m rs = +0.03, p=0.92 22 subjects

  21. Further experiments • Repeatability • 15m, was task difficulty due to poor determination of heading? • Does physical size not matter at all?

  22. How repeatable were our results? 5m rs = 0.43, p=0.26 15m rs = 0.77, p=0.08 Compare distributions No significant differences (p>0.69) 8 subjects

  23. 15m obstacles: was task difficulty due to a problem determining heading?

  24. z4 = 1.15, p = 0.25 z4 = 2.37, p = 0.02 15m obstacles: providing heading information improved task performance 5 subjects

  25. Wings Does physical size not matter at all?

  26. Actual (half) width of the wings Does physical size matter? Yes z4 = 2.02, p = 0.04 z4 = 1.83, p = 0.07 5 subjects

  27. Review of main results • Effect of distance • collision envelope slightly larger; and • decision quality reduced at further distance • Heading perception seems a limiting factor • Physical characteristics not predictive, but • Collision envelope can be manipulated

  28. We evaluated …. • Collision detection • Subject’s perception of “size” (collision envelope or safety margin) While…. • Free viewing in “rich” virtual environment • Actually walking But … • Stationary obstacles only • Single obstacles only

  29. Thank you(for coming to the last presentation at ARVO 2003) Supported by NIH grant EY12890

  30. The collision envelope • We defined the collision envelope as the optimal decision point of the intra-class kappa coefficient • This assumes that the cost of a false positive (avoidance when no risk) is the same as a false negative (collision)

  31. The weighted kappa coefficient K0.1 places greater cost on false negative (collision)

  32. On a treadmill

  33. large obstacle, 5m, 25cm

  34. large obstacle, 15m, -15cm

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