perceived collision with an obstacle in a virtual environment

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

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

• 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)

How “good” a decision? Decision quality = maximum kappa (height)

Kappa coefficient of association

Collision envelope varied between subjects and with obstacle distance

Z19=3.44

p<0.001

No effect of obstacle size

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

22 subjects

5m rs = 0.02, p=0.92

15m rs = 0.01, p=0.99

Collision envelope was (usually) larger than measured physical characteristics

22 subjects

Collision envelope equals body width

5m rs = -0.26, p=0.25

15m rs = +0.03, p=0.92

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

8 subjects

Compare distributionsNo significant differences (p>0.69)

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 = 0.025 subjects

0 20 40 60 80

first trial

with path

collision envelope (cm)

0.0 0.2 0.4 0.6 0.8 1.0

first trial

with path

kappa coefficient

Does physical size not matter at all?

Wings

0.0 0.2 0.4 0.6 0.8 1.0

first trial

wings

kappa coefficient0 20 40 60 80

first trial

wings

collision envelope (cm)

Does physical size matter?

z4 = 2.02, p = 0.04 z4 = 1.83, p = 0.07

Yes

5 subjects

Actual (half) width of the wings

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 …. • 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 K0.1 places greater cost on false negative (collision)

K0.1 K0.5

5m 46cm 37cm

15m 65cm 46cm

On a treadmill

large obstacle, 5m, 25cm

large obstacle, 15m, -15cm