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Vehicle Characteristics and Car Following. George J. Andersen Department of Psychology University of California, Riverside. Funded by NIH AG13419-06 PATH Project MOU 4220. Perceptual Tasks in Driving. Collision Detection Obstacle avoidance Longitudinal control (car following)

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vehicle characteristics and car following

Vehicle Characteristics and Car Following

George J. Andersen

Department of Psychology

University of California, Riverside

Funded by NIH AG13419-06PATH Project MOU 4220

perceptual tasks in driving
Perceptual Tasks in Driving
  • Collision Detection
  • Obstacle avoidance
  • Longitudinal control (car following)
  • Lateral control (steering)
perceptual tasks in driving1
Perceptual Tasks in Driving
  • Collision Detection
  • Obstacle avoidance
  • Longitudinal control (car following)
  • Lateral control (steering)
slide4

Driving is a skill dependent on visual information

Use of simulators requires accurate presentation

of visual information used by drivers

complexity of collision detection event specification
Complexity of Collision Detection: Event Specification
  • Vehicle motion
    • Speed
      • Constant or varying (accelerating/decelerating)
    • Path
      • Straight or curved
  • Object motion
    • Speed
      • Constant or varying (accelerating/decelerating)
    • Path
      • Straight or curved
complexity of collision detection
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

slide7

TopView

FrontView

t=0

t=1

q

t=q/Dq

t specifies the time to contact during constant velocity

collisions

slide8

t =dt/dt

dt/dt used during deceleration (braking control)

When dt/dt = -0.5 vehicle will reach zero velocity at obstacle

slide9

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

slide10

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

slide11

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

slide14

Vehicle Motion

No

F/S

V/S

F/C

V/C

No

F/S

Object

Motion

V/S

F/C

V/C

F = Fixed Speed V = Variable Speed

S = Straight Path C = Curved Path

optical information for car following
Optical Information for Car Following
  • Information for specifying distance and change in distance
  • Information for specifying speed and change in speed
slide16

t=0

t=1

a

FrontView

TopView

Da associated with change in distance due to change in speed

parameters of car following model
Parameters of Car Following Model

a’

  • Initial visual angle of lead vehicle

a

  • Current visual angle

da/dt

  • Instantaneous change in visual angle

J, k

  • Weighting scalar constants
slide18

acceleration

Acceleration (km/hr2)

slide19

a’

Desired time gap = 1.1s

W = width of lead vehicle

FVv = following vehicle (driver) speed

2 m

Lead Car

Distance headway

α

Driver

human factors experiments
Human Factors Experiments
  • Maintain distance behind lead vehicle that varied speed

- sine function

- ramp function

- sum of sines

function

slide25

Edge Rate Information:

Used for Perceived

Driver (following vehicle)

speed

slide26

a

t =

da/dt

Edge Rate and Collision Detection: Moving Objects

slide27

t =dt/dt

Edge Rate and Collision detection during braking: Static objects

slide28

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

edge rate and moving vehicles
Edge Rate and Moving Vehicles

Dual task performance

car following

Detect Light Change

Edge Rate Information

Presence of other moving

vehicles

edge rate and reduced visibility
Edge Rate and Reduced Visibility

Dual task performance

car following

Detect Light Change

Edge Rate Information

Presence of Fog

simulation design issues and recommendations
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
simulation design issues and recommendations1
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
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