1. Vehicle Characteristics and Car Following
George J. Andersen
Department of Psychology
University of California, Riverside
Funded by NIH AG PATH Project MOU 4220

2. Perceptual Tasks in Driving
Collision Detection
Obstacle avoidance
Longitudinal control (car following)
Lateral control (steering)

3. Perceptual Tasks in Driving
Collision Detection
Obstacle avoidance
Longitudinal control (car following)
Lateral control (steering)

4. Driving is a skill dependent on visual information
Use of simulators requires accurate presentation
of visual information used by drivers

5. Complexity of Collision Detection: Event Specification
Vehicle motion
Speed
Constant or varying (accelerating/decelerating)
Path
Straight or curved
Object motion

6. Complexity of Collision Detection:
Model (Andersen & Sauer, 2004) based on analysis of visual information available to driver
Use of 5 parameters t
dt/dt a
da/dt
ddiff

7. t specifies the time to contact during constant velocity collisions
Front View
Top View
t=1
t=0 q
t=q/Dq
t specifies the time to contact during constant velocity
collisions

8. dt/dt used during deceleration (braking control)
t =dt/dt
dt/dt used during deceleration (braking control)
When dt/dt = -0.5 vehicle will reach zero velocity at obstacle

9. a is the position of
object in visual field
When a = 0 object is on a collision path
Useful when path of motion is linear

10. da/dt is the change in
position of object in
visual field
When da/dt = k object is on a collision path
Useful when path of motion is curvilinear

11. ddiff is comparison of two distance estimates:
dv – distance vehicle will traverse before reaching zero velocity
ds – distance of collision object
ddiff = dv – ds
dv = 1.5v2/a
v = edge rate (number of texture elements that
pass position in visual field)
a = change in number of texture
Elements that pass position in visual field
ds = (s)tan-1 q
s = size of object
q = visual angle of object

12. Size information and safe deceleration to a stop

13. Edge rate information and safe deceleration to a stop

14. Vehicle Motion No F/S V/S F/C V/C dt/dt, ds dt/dt No F/S Object Motiona
dt/dt, ds
da/dt, t
da/dt, t dt/dt, ds
da/dt, ds
dt/dt
da/dt, ds dt/dt
da/dt No
F/S
Object
Motion
V/S
F/C
V/C
F = Fixed Speed V = Variable Speed
S = Straight Path C = Curved Path

15. Optical Information for Car Following
Information for specifying distance and change in distance
Information for specifying speed and change in speed

16. Da associated with change in distance due to change in speed
Top View
t=1
t=0 a
Front View
Da associated with change in distance due to change in speed

17. Parameters of Car Following Model
Initial visual angle of lead vehicle a
Current visual angle
da/dt
Instantaneous change in visual angle
J, k
Weighting scalar constants

18. Acceleration (km/hr2)
acceleration

19. 2 m a’
Desired time gap = 1.1s
W = width of lead vehicle
FVv = following vehicle (driver) speed
Lead Car
Distance headway α
Driver

20. Human Factors Experiments
Maintain distance behind lead vehicle that varied speed
- sine function
- ramp function
- sum of sines
function

23. 006

24. Do drivers use visual information other than visual angle?

25. Edge Rate Information:
Used for Perceived
Driver (following vehicle)
speed

26. Edge Rate and Collision Detection: Moving Objects
da/dt
t =

27. Edge Rate and Collision detection during braking: Static objects
t =dt/dt

28. Car Following and edge rate Experiment
Task: Car following to sine wave function
Independent Variables: Presence or absence of scene
Frequency and amplitude of lead vehicle speed
Prediction:
If edge rate used then more accurate tracking performance when scene present as compared to scene absent

30. Ongoing Research: Car Following in Traffic

31. Edge Rate and Moving Vehicles
Dual task performance
car following
Detect Light Change
Edge Rate Information
Presence of other moving
vehicles

32. Edge Rate and Reduced Visibility
Dual task performance
car following
Detect Light Change
Edge Rate Information
Presence of Fog

35. Simulation Design Issues and Recommendations
Simulation displays should be designed to optimize use of visual information
Understanding how best to do this requires understanding what are the sources of information

36. Simulation Design Issues and Recommendations
Factors that directly affect availability of information sources
Display characteristics (e.g., frame rate, spatial resolution, monitor update and flicker)
3D model characteristics (e.g., complexity of world model, lighting, and texturing)
Viewing characteristics
(e.g., conflicting accommodation, eye vergence)
Viewing from design eye of simulation