1. 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

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
Cutting, Vishton & Braren (1995)

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. How “big” do you feel? Distance with optimal decision (highest kappa)
Kappa coefficient of association
How “good” a decision? Decision quality = maximum kappa (height)
How “big” do you feel? Distance with optimal 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 was (usually) larger than measured physical characteristics
Collision envelope equals body width
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. 15m obstacles: providing heading information improved task performance
z4 = 1.15, p = 0.25
z4 = 2.37, p = 0.02
5 subjects

25. Does physical size not matter at all?
Wings
Does physical size not matter at all?

26. Does physical size matter?
Yes
Actual (half) width of the wings
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 …. While…. But … 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

31. 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)

32. The weighted kappa coefficient K0
The weighted kappa coefficient K0.1 places greater cost on false negative (collision)
K0.1
K0.5 5m
46cm
37cm
15m
65cm

33. On a treadmill

34. large obstacle, 5m, 25cm

35. large obstacle, 15m, -15cm