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? 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

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)

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

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

Closest distance to obstacle

Some obstacles crossed the path

Obstacle appearance distance

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

small obstacle, 5m, 55cm

large obstacle, 15m, 100cm

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

“Yes, collision” responses against closest distance to obstacle

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)

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

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

Some subjects had great difficulty at 15m

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

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

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

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

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

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

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

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

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

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

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

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

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)

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

On a treadmill

large obstacle, 5m, 25cm

large obstacle, 15m, -15cm