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Regulation of Speed in Car Following. George J. Andersen Craig Sauer Asad Saidpour Department of Psychology University of California, Riverside. PATH Project MOU 4220. Utility of Human Driver Model. Warning Systems Autonomous/semi-autonomous Vehicle Control Traffic Flow Models. ROAD

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regulation of speed in car following

Regulation of Speed in Car Following

George J. Andersen

Craig Sauer

Asad Saidpour

Department of Psychology

University of California, Riverside

PATH Project MOU 4220

utility of human driver model
Utility of Human Driver Model
  • Warning Systems
  • Autonomous/semi-autonomous Vehicle Control
  • Traffic Flow Models
slide3

ROAD

ENVIRONMENT

Strategic

Perception

Tactical

Management and control

Operational

Execution

Emergency Management

react collision detection model
REACT – Collision Detection Model
  • Model based on analysis of visual information available to driver
    • Use of 5 parameters

t

dt/dt

a

da/dt

ddiff

slide5

TopView

FrontView

t=0

t=1

q

t=q/Dq

t specifies the time to contact during constant velocity

collisions

slide6

t =dt/dt

dt/dt used during deceleration

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

slide7

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

slide8

da/dt is the change in

position of object in

visual field

When da/dt approaches zero object is on a collision path

Useful when path of motion is curvilinear

slide9

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 = 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

slide10

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

slide11

Select

Object

Derive a, d/dt

(angular direction)

Is a=0? Or d/dt0?(constant angular direction)

Yes

No

Derive t

Is t>0?

(expansion)?

Derive

dv-ds

Derive

dt/dt

Yes

Is dt/dt =0?

(Constant expansion)

Is dv-ds =0?

(Constant expansion)

Yes

Collision Object

No

No

Is dt/dt >=-0.5?

Yes

Is dv-ds >0?

No

Non-collision Object

limitations of react parameters
Limitations of REACT Parameters

t, dt/dt, and ddiff only useful when a collision is impending

a and da/dt only useful for recovering collision path information

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
slide15

TopView

t=0

t=1

q

FrontView

Dq associated with change in distance due to change in speed

parameters of car following model
Parameters of Car Following Model

q0

  • Initial visual angle on target vehicle

q

  • Current visual angle

dq/dt

  • Instantaneous change in visual angle

k1, k2

  • Constant determined by small or large velocity change
slide17

Derive q – q0, dq/dt

-

+

Sign dq/dt

Increasing Distance

Decreasing Distance

-

-

+

+

Sign q - q0

Sign q - q0

Too close

Too close

Too far

Too far

Small (k1)

Acceleration

Small (k1)

Deceleration

Large (k2)

Acceleration

Large (k2)

Deceleration

how is change in speed determined

Dq(t)

Velocity

change

Velocity Scalar

k

=

q(t)

t

How is change in speed determined?
human factors experiments
Human Factors Experiments
  • Maintain distance behind target vehicle that varied speed

- sine function

- ramp function

slide24

GAIN

PHASE

Frequency

Frequency

Amplitude

Amplitude

0.0513

0.0513

0.1111

0.1111

CAR ONLY

CAR ONLY

5 k/h

5 k/h

1.701

-20.088

-62.28

2.039

15 k/h

15 k/h

-24.936

1.435

-68.964

1.230

25 k/h

25 k/h

-24.491

1.373

0.982

-67.776

CAR WITH SCENE

CAR WITH SCENE

5 k/h

5 k/h

-24.795

1.426

1.755

-72.356

15 k/h

15 k/h

-26.585

1.075

-67.248

0.961

25 k/h

25 k/h

-28.228

0.982

0.793

-63.183

conclusions
Conclusions
  • Presented a model of car following
    • Based on visual angle information of target vehicle
  • Performance of model good fit to performance of drivers in car following study
  • Examining role of speed information from surrounding scene