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Jeroen Hogema. Driving behaviour effects of the Chauffeur Assistant . Overview. Background Method TNO driving simulator Simulating the CA Experimental design Results Conclusions consequences for traffic simulation model. Dutch Evaluation of the Chauffeur Assistant (DECA).

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Jeroen hogema

Jeroen Hogema

Driving behaviour effects of the Chauffeur Assistant


Overview
Overview

  • Background

  • Method

    • TNO driving simulator

    • Simulating the CA

    • Experimental design

  • Results

  • Conclusions

    • consequences for traffic simulation model


Dutch evaluation of the chauffeur assistant deca
Dutch Evaluation of the Chauffeur Assistant (DECA)

  • Chauffeur Assistant

    • Adaptive Cruise Control

    • Lane Keeping System

  • Follow-up of Lane Departure Warning Assistant FOT

  • Transport Research Centre (TRC)

    • Ministry of Transport, Public Works, and Water Management


Chauffeur assistant questions
Chauffeur Assistant: Questions

Individual driver level

  • driving behaviour

  • workload

  • acceptance

    traffic flow level

  • traffic performance

  • safety indicators


DECA

MIXIC

DRIVING SIMULATOR

behaviour

driver

CA

CA

workload

MIXIC simulations

acceptance

interpretation

report

TNO

TRC


Method driving simulator
Method – Driving simulator

  • visual

  • audio

  • steering force

  • motion


Method driving simulator1
Method – Driving simulator

  • DAF 95XF lorry

  • Mass 20500 kg (fully loaded)

  • Maximum engine power: 350 kW

  • Parameter set from DAF trucks


Method simulating the ca
Method – Simulating the CA

  • Adaptive Cruise Control

    DC specifications

  • Distance law for car-following

    • Dref = 6.0 + 1.3 * v

    • Dref = ACC's intended following distance (m)

    • v=current speed (m/s)

  • Braking: max. -3 m/s2


Method simulating the ca1
Method – Simulating the CA

  • ACC controller

    • structure from earlier work

  • parameters from recent ACC work by TNO Automotive


Method simulating the ca2
Method – Simulating the CA

ACC reference scenarios

  • approaching

  • braking lead car

  • accelerating lead car

  • cut-in

    Dynamic behaviour of

  • reference model

  • driving simulator CA

  • MIXIC CA


Method simulating the ca3
Method – Simulating the CA

LKS

  • noise added to obtain realistic servo performance

  • SDLP about 10 cm


Method experimental design 1
Method - Experimental design (1)

  • with vs without CA

  • traffic volume

    • low (3400/u)

    • high (6000/u)

  • 3-lane motorway, 3.5 m wide lanes

    ACC headway

    • Dref = 6.0 + tk * v

    • tk = 1.0 – 1.3 – 1.6 s

  • 1 preferred setting selected by each driver prior to experiment


Method experimental design 2
Method - Experimental design (2)

Scenarios

  • car-following (overtaking possible)

  • braking lead car

    • 3 m/s2

    • 4 m/s2

      Subjects

  • 18, professional truck drivers

  • at least 5 years 'groot rijbewijs'

  • age between 25-55

  • paid for their participation


Human machine interface
Human Machine Interface

  • driver turns CA turns on/off

    • switches

    • brake pedal

  • driver sets ACC speed

  • buzzer at maximum braking

    display

  • ACC set speed on speedometer

  • symbol: headway control or speed control


Results preferred ca time headway
Results – preferred CA time headway

1.0 s 1 x

1.3 s 8 x

1.6 s 9 x

Total 18 x





Results lane change frequency
Results – lane change frequency

  • effect of CA on edge of marginal significance [p<.11]


Braking lead car lane change response
Braking lead car: lane change response

lane change reaction of subject

Fewer lane changes with CA


Braking lead car braking response
Braking lead car: braking response

braking reaction of subject

lower deceleration levels with CA


Results mental effort
Results – mental effort

  • Rating Scale of Mental Effort

  • effect of CA


Acceptance 1
Acceptance (1)

  • -2..2 scales:


Acceptance 2
Acceptance (2)

Underlying variables

  • USEFULNESS: + 0.93

  • SATISFACTION: + 1.10


Summary of results 1
Summary of results (1)

With Chauffeur Assistant…

  • reduced SD of lateral position

  • higher Time to Line Crossings

  • less short time headways

  • reduced Mental Effort

  • (fewer changes with CA?)


Summary of results 2
Summary of results (2)

  • Acceptance: positive

    • except “sleep-inducing”

      Lane changes

  • fewer changes with CA?

    Braking lead car

  • fewer lane changes with CA

  • less critical behaviour with CA (maximum deceleration, minimum TTC)

No effects on:

  • mean, s.d. speed

  • lane use (% right lane)

  • mean lateral position

  • mean time headway


Chauffeur assistant in mixic
Chauffeur Assistant in MIXIC

driver

vehicle

CA


Chauffeur assistant in mixic1
Chauffeur Assistant in MIXIC

DRIVER

LONGITUDINAL

car following

VEHICLE

free driving

LATERAL

CA

lane change model

CA

settings

transitions


Mixic driver model
MIXIC driver model

Driver – CA

  • CA settings

    • CA reference speed = driver’s intended speed

    • CA reference headway: 50% 1.3 s; 50% 1.6 s

  • CA off when:

    • CA is braking hard AND driver would brake harder

    • starting lane-change manoeuvre

  • CA on when:

    • “possible”


Mixic driver model1
MIXIC driver model

Lane change behaviour

  • small effects

  • nature of effects unknown

    • tactical level: avoid getting 'stuck' in car-following in a 'slow' lane

    • driver-state related: reduced alertness, complacency, less 'active' driving

  • => no changes in lane change model


Conclusions
Conclusions

Chauffeur Assistant – ACC + LKS:

  • Behaviour

  • Workload effects in line with ACC orLKS research

  • Acceptance

}

contribution of ACC and LKS unknown

Minor modifications to MIXIC->driver->ACC model


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