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Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology InMotion Light-based communication between automated vehicles and other road users

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Page 1: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

InMotion Light-based communication between automated vehicles and other road users

Page 2: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

InMotion: „Development of light-based communication between automated vehicles and other road users “

Funding: BMVI, Total Budget 1,1 M € Duration: 10/2017 - 06/2020 (33 Month) Coordination: TUC (Matthias Beggiato) Consortium: Chemnitz University of Technology (Cognitive and

Engineering Psychology + Communications Engineering)

Ford Werke GmbH (Aachen)

Intenta GmbH Chemnitz (SME)

Page 3: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

Aims InMotion • Development of light-based communication between automated

vehicles and other road users, particularly Vulnerable Road User (VRU; cyclist and pedestrians)

• Analysis of existing communication procedures as basis • Develop and test Human-Machine Interfaces as external

communication solution (eHMI), user centered approach, Wizard-of-Oz study, Field study, Lab studies using augmented video

• Prototypical Hard- and Software solution and demonstrator vehicle with sensors, C2X-comm., light-based communication

• Focus on urban setting (low speed), 3 potential use cases: 1) VRU crossing at crosswalks, mixed traffic environment 2) Automated valet-parking, communication with user 3) Communication with passengers of automated taxis

Page 4: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

Work packages (01.10.2017 – 30.06.2020)

quarter

WP1: Analysis communication existing datasets

WP2: User studies eHMI (Lab, Field, Wizard-of-Oz)

WP3: Development and test sensors systems

WP4: Modelling communication

WP5: Setup demonstrator vehicle

Q7 Q8 Q9 Q10 Q11Q1 Q2 Q3 Q4 Q5 Q6

1) Wizard-of-Oz study 2) Field study Both with Light Bar Ford as eHMI

Page 5: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

Results WP 2:

Wizard-of-Oz study/ Field study Chemnitz, 19.11.2018 Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

Page 6: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

Wizard-of-Oz study

Page 7: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

• Aim: effects of light signals on uninformed passing pedestrians

• Setting: parking area on the campus of Chemnitz University of Technology

• Wizard-of-Oz technique (driver hidden by seat suit); between-subject-design

• Applied methods: questionnaires, interviews, videos

Method | study design

Use cases (color: turquoise)

Driv

ers‘

visi

bilit

y

No signal Automation mode

Crossing mode

Starting mode

Driver visible Video data Questionnaires, Interview data, video data Driver invisible

(seat suit)

Page 8: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

Method | use cases

Page 9: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

• Demographic data: age, gender

• Scale: • [1] „I completely disagree“ to • [7] „I completely agree“

• Subjective safety during the interaction

with vehicle1

• Usefulness of signals*2

• Trust in signals*2 • Comprehensibility of signals*2

Method | procedure and depended variables

*data also collected in the field study 1 uninformed 2 informed

Drive in respective mode (randomized light signals/

driver visibility)

Agreement for interview

1st part of the interview: Uninformed participants

2nd part of the interview: Informed participants

1

2

3

4

Page 10: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

• Video data: 98 drives • ≈ 6,5h • ≈ 1800 pedestrians

• Interview data: 173 participants • 113 (66.1%) men, 58 (33.9%) women • Age: M = 29 (SD = 10.65) • Completed questionnaires: N = 147

Results | sample and general data

Page 11: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

• Regarding subjective safety during the interaction with the vehicle there is no difference between the light signals (F(3,161) = 1.59, p = .193, ηp

2 = .03).

• The participants felt significantly safer during the interaction with the vehicle when the driver was visible (F(1,161) = 4.03, p = .046, ηp

2 = .02).

Safety When interacting with the vehicle I felt safe.

Agre

emen

t

N = 168

1

2

3

4

5

6

7

Drivervisible

Driverinvisible

Drivervisible

Driverinvisible

Drivervisible

Driverinvisible

Drivervisible

Driverinvisible

No signal Automation mode Crossing mode Starting mode

Safety in interaction

Error bars = 95% CI

Page 12: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

1

2

3

4

5

6

7

Driver visible Driver invisible Driver visible Driver invisible Driver visible Driver invisible

Automation mode Crossing mode Starting mode

Usefulness of the presented signals • The presented light signals

were only partially assessed as useful by the participants.

• Regarding the usefulness of the presented light signals there is no difference (F(2,126) = 2.91, p = .058 ηp

2 = .04).

• The presented light signals were assessed as equally useful by the participants despite driver’s visibility (F(1,126) = 0.28, p = .598, ηp

2 = .00).

Usefulness The presented signal is useful.

p < .05

N = 132

Agre

emen

t

Error bars = 95% CI

Page 13: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

1

2

3

4

5

6

7

Driver visible Driver invisible Driver visible Driver invisible Driver visible Driver invisible

Automation mode Crossing mode Starting mode

General usefulness of the signals • In general light signals as

external HMI were assessed as useful by the participants.

• Regarding the usefulness of the general signal use there is no difference between the light signals (F(2,140) = 1.26, p = .286, ηp

2 = .02).

• The general use of the light signals was assessed as equally useful by the participants despite driver’s visibility (F(1,140) = 2.39, p = .124, ηp

2 = .02).

Usefulness A Signal that indicates… is generally useful.

N = 146

Agre

emen

t

Error bars = 95% CI

Page 14: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

1

2

3

4

5

6

7

Driver visible Driver invisible Driver visible Driver invisible Driver visible Driver invisible

Automation mode Crossing mode Starting mode

Trust in the signals • In general the light signals were only partially assed as trustworthy by the participants.

• Regarding trust there is no difference between the light signals (F(2,127) = 0.04, p = .964, ηp

2 = .00).

• The light signals were assessed as equally trustworthy by the participants despite driver’s visibility (F(1,127) = 0.04, p = .838, ηp

2 = .00).

Trust – Overall item

N = 133

Agre

emen

t

Error bars = 95% CI

Page 15: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

1

2

3

4

5

6

7

Driver visible Driver invisible Driver visible Driver invisible Driver visible Driver invisible

Automation mode Crossing mode Starting mode

Comprehensibility of the signals • In general the light signals were assessed as not comprehensible by the participants.

• Regarding comprehensibility

there is no difference between the light signals (F(2,126) = 0.00, p = .997, ηp

2 = .00).

• The light signals were assessed

as equally (not) comprehensible by the participants despite driver’s visibility (F(1,126) = 0.03, p = .859, ηp

2 = .00).

Comprehensibility The signal is comprehensible.

N = 132

Agre

emen

t

Error bars = 95% CI

Page 16: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

Interview data Closed-ended questions

Yes No Did you perceive the light signal on top of the vehicle?

N = 133 (88.7%) N = 17 (11.3%)

The light signal was perceived by the majority of participants (88.7%).

Do you believe that the light signal was addressed to you?

N = 19 (14.3%) N = 114 (85.7%)

Despite the low spatial distance in the parking area setting the majority of participants (85.7%) did not believe that the light signal was addressed to themselves.

Did you see any driver inside the vehicle?

79.2% of participants did not see any driver when the seat suit was worn. When the driver was visible 51.6% of participants did see the driver (20.3% were unsure; 28.1% did not see any driver).

N = 133

N = 133

N = 170

Page 17: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

Interview data (including all conditions) Open-ended question: What do you think was indicated by the signal?

Ambulance

Communication to other drivers

Security transport

Mapping Indicating risks and hazards Communication to pedestrians

Sensor as driver assistance systems

Giving right of way

Communication in general

Illumination

Sensors in general

Detection and tracing of obstacles

Priority/ precedence/ privilege

Emergency/ rescue vehicle in general

Driverless vehicle

Attention

Warning/ caution

Police

No idea Type of vehicle

Communicaton partner Vehicle functions

Light bar functions Communication message

No information

Qualitative data analyzed over all conditions;

N = 138

No meaning

Page 18: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

Study 2: Evaluation of light signals

Page 19: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

• Aim: Evaluation of light signals to communicate between VRU and automated vehicle

• Setting: field study (Elsasser Straße, Chemnitz) • Independent variables:

3 light signals: AUTOMATED,

STARTING,

CROSSING 3 colors: WHITE, TURQUOISE, PURPLE • Dependent variables: visibility and trust, acceptance,

comprehensibility of signals, appropriateness of signal colors

• Applied methods: questionnaires, interviews, evaluation of visibility

• Sample: N = 38 (18 men, 20 women; mean age: 50 years (SD = 23.49))

Method | study design

Page 20: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

Method | procedure

Laboratory (uninformed) Test site (uninformed) Test site (informed)

Welcome and socio-

demographics

Visibility of colors (distance measure-

ment; 470m condition)

Visibility of colors (100m, 50m, 20m and

5m distance)

Assumed meaning of light signals (uninformed)

Evaluation of light signals; appropriateness

of colors (informed)

Closure and goodbye

1 1

2

3

1 6

1 5

1 4

3 times each 3 times each

Visibility of light colours

(distance measure-ment;

470m condition)

2

100m 50m

20m 5m

Page 21: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

RESULTS I Visibility of signals depending on color

Page 22: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

Participants detected the signal from significant different distances depending on signal color. Purple > Turquoise > White (* significant pairwise comparisons)

SIGNAL CONSTANT Visibility of signals depending on color

0

100

200

300

400

White Turquoise Purple

Dist

ance

in m

Distance signal detection

N = 38

*

* 1

3

5

7

White Turquoise Purple

Agre

emen

t

Visibility - 100m distance

N = 38

1

3

5

7

White Turquoise Purple

Agre

emen

t

Visibility - 50m distance

N = 38

1

3

5

7

White Turquoise Purple

Agre

emen

t

Visibility - 20m distance

N = 38

1

3

5

7

White Turquoise Purple

Agre

emen

t

Visibility - 5m distance

N = 38

For all distances analysis of subjective evaluations results in the same ranking: Purple > Turquoise > White (Single Item;)

Luminous flux for all conditions: 2 lumen

Error bars represent 95% CI

Page 23: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

RESULTS II Evaluation of signals & color

Page 24: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

heuristic analysis of interviews, frequent answers bold

AUTOMATED What do you think about the meaning of the signal? (uninformed)

Better visibility/part of daytime running light

Signal has no meaning

No idea

Warning/

caution

Problems/

hazards

Attention

Stop/ give right

of way

Road

closed

Vehicle is driving

particularly slow

Open way

Better visibility/ part of daytime running light

Stop/ give right of way

Signal has no meaning

No idea

Warning/

caution Emergency vehicle

Communication with

pedestrians/ other drivers

Vehicle is driving

particularly slow

Tuning

Attention

Problems/

hazards

Open way

Starting

mode

Open way/

pedestrians can cross

Warning/ caution

Problems/ hazards

Stop/ give right of way

Road closed

Attention

Better visibility/part of

daytime running light

Advertisement

Vehicle is driving

particularly slow

Lane

guidance

Emergency vehicle

No idea

Signal has no meaning

Joke

Page 25: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

heuristic analysis of interviews, frequent answers bold

STARTING What do you think about the meaning of the signal? (uninformed)

Attention

Warning/ caution

Emergency vehicle

Road closed

Hazard warning lights

Tuning/ fun

Problems/

hazards

Open way

Drive slowly

Stop

Advertisement

Drive aside

No idea

Page 26: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

heuristic analysis of interviews, frequent answers bold

CROSSING What do you think about the meaning of the signal? (uninformed)

Attention

Warning/ caution

Corridor for emergency vehicle access

Hazard

warning lights

Tuning/ fun

Problems/ hazards

Stop/ give

right of way

No meaning

Drive slowly

Emergency vehicle

Don‘t

overtake

Road closed

Page 27: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

The appropriateness of signal color is evaluated differently for the different signal types. (Single Item, * significant pairwise comparisons)

Signal & color Appropriateness of signal color (informed)

1

2

3

4

5

6

7

White Turquoise Purple

Agre

emen

t

Appropriateness of color for signal AUTOMATED

N = 38

*

Error bars represent 95% CI

*

*

1

2

3

4

5

6

7

White Torquoise Purple

Agre

emen

t

Appropriateness of color for signal STARTING

N = 38

1

2

3

4

5

6

7

White Turquoise Purple

Agre

emen

t

Appropriateness of color for signal CROSSING

N = 38 Error bars represent 95% CI

* *

Error bars represent 95% CI

Page 28: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

RESULTS III Evaluation of signals regardless of color

Page 29: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

On average participants agree to trust all of the presented signals of automated driving. (Trust Scale; Jian, Bisanz & Drury, 2000)

Signals regardless of color Trust and comprehensibility (informed)

1234567

Automated Starting Crossing

Agre

emen

t

Trust

N = 37

On average participants agree that all signals are comprehensible. (Single Item)

1234567

Automated Starting Crossing

Agre

emen

t

"The signal is comprehensible."

N = 37 Error bars represent 95% CI Error bars represent 95% CI

Page 30: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

Ratings of participants indicate a rather high acceptance of all presented signals. (Acceptance Scale, van der Laan, Heino & De Waard, 1997)

Signals regardless of color Acceptance of the signal (informed)

Page 31: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

On average participants agree.

Participants assess the presented signals to be useful. (Single Item)

Signals regardless of color Usefulness (informed)

1234567

Automated Starting Crossing

Agre

emen

t

"The presented signal is useful."

N = 38

1234567

Automated Starting Crossing

Agre

emen

t

"The signal is generally useful."

N = 37

Results reveal an averaged agreement for all 3 signals. Participants assess the presentation of signals for automated driving generally as useful. (Single Item)

Error bars represent 95% CI Error bars represent 95% CI

Page 32: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

Implications

• Differences between passing-by pedestrians and invited

participants regarding assessed usability, trust and comprehensibility potential reasons: amount of explanation, directedness

• The presented light signals are not comprehensible by intuition.

• In general, light signals as a form of communication in automated driving is evaluated as useful. possible form of external HMI in automated driving from user perspective

• Visibility: Clear ranking: purple > turquoise > white But: What is an optimal visibility in this context?

Page 33: InMotion - UNECE

KIVI - Cooperative interaction with vulnerable road users in automated driving

Beggiato, M., Witzlack, C., Springer, S. & Krems, J.F.

Chemnitz University of Technology

Cognitive and Engineering Psychology

[email protected]

Priority Program 1835 “Cooperative Interacting Cars” German Science Foundation KIVI: Kooperative Interaktion mit schwächeren Verkehrsteilnehmern im automatisierten Fahren

Page 34: InMotion - UNECE

Priority Program 1835 “Cooperative Interacting Cars” German Science Foundation KIVI: Kooperative Interaktion mit schwächeren Verkehrsteilnehmern im automatisierten Fahren

Observation of interaction behavior, video labeling and analysis of interaction sequences (Witzlack, Beggiato & Krems, 2016)

Video simulation studies to identify

parameters, e.g. expected moment of braking, perception of deceleration, influencing factors… (Beggiato, Witzlack, Springer & Krems, 2017a; 2017b)

Focus group discussions and video simulation

studies on explicit external communication, e.g. projections, displays... (Ackermann, Beggiato, Schubert & Krems, submitted)

On-road test with partner from communication engineering and automated BMW i3 (summer 2018)

Page 35: InMotion - UNECE

42

Method

Study design

7x2x2 mixed design:

IV1 within-subject: vehicle speed from 10 to 40 km/h in 7 steps of 5 km/h. Exactly manipulated by accelerating/decelerating video playback speed

IV2 within-subject: daytime, midday (11:13 AM) and dusk (19:25 PM at 2nd of April)

IV3 between-subject: age, two age groups from 20-30 and 50+ years

Each of the 14 within-conditions presented in randomized order, 3 repetitions to stabilize results (mean calculated) 42 trials per participant

DV: last accepted time gap in seconds of the oncoming vehicle, i.e. last moment of crossing comfortably before the vehicle

Instruction: press a defined key at the last moment, when you would cross the street comfortably (without running) before the vehicle.

video midday

(11:13 AM)

video dusk

(19:25 PM)

Page 36: InMotion - UNECE

43

Results – speed x age

10 15 20 25 30 35 40

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

6.0

6.5

vehicle speed (km/h)

acce

pte

d tim

e g

ap

(s)

older (> 50 years)

younger (< 30 years)

error bars: 1 SE

main effects and interaction ANOVA

F p η2p

speed F(1.59, 61.84) = 67.22 < .001 .633

age F(1, 39) = 4.46 .041 .103

speed × age F(1.62, 63.32) = 7.95 .002 .169

daytime × age F(1, 39) = 0.30 .590 .008

Page 37: InMotion - UNECE

44

Results – speed x daytime

main effects and interaction ANOVA

F p η2p

speed F(1.59, 61.84) = 67.22 < .001 .633

daytime F(1, 39) = 29.28 < .001 .429

speed × daytime F(4.86, 189.55) = 1.63 .155 .040

10 15 20 25 30 35 40

2.0

2.5

3.0

3.5

4.0

4.5

5.0

5.5

vehicle speed (km/h)

acce

pte

d tim

e g

ap

(s)

dusk

day

error bars: 1 SE

Page 38: InMotion - UNECE

45

Potential

Dedicated video simulation environment (progr. in LabView)

Exact play rate control of the videos, speed profiles, blanking, pedestrian overlay, logging of participant’s reactions, experimental control of instructions, randomized trials, messages etc.

Easy configuration by separate Excelfiles: videos, instructions, speed, randomization trials, speed profiles… also used in teaching for bachelor students

Variations of video simulation studies

Type/size of cars

TTA estimation by blanking the video (perception / estimation)

Augmented pedestrian controlled by study participants

Perception of braking / accelerating speed profile

Formal communication / HMI solutions, evaluated by participants

Page 39: InMotion - UNECE

46

Project publications KIVI DFG

Ackermann, C., Beggiato, M., Schubert, S., & Krems, J. F. (in press). An experimental study to investigate design and assessment criteria: what is important for communication between pedestrians and automated vehicles? Applied Ergonomics.

Ackermann, C., Beggiato, M., Blum, L.-F., & Krems, J. F. (2018). Vehicle Movement and its Potential asImplicit Communication Signal for Pedestrians and Automated Vehicles. In N. Van Nes & C. Voegelé (Eds.), Proceedings of the 6th HUMANIST Conference (pp. 86-92). Lyon: Humanist Publications. ISBN: 978-2-9531712-5-9.

Beggiato, M., Witzlack, C., & Krems, J. F. (2017). Gap Acceptance and Time-To-Arrival Estimates as Basis for Informal Communication between Pedestrians and Vehicles. In Proceedings of the 9th International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI '17). (pp. 50-57). New York, NY, USA: ACM. doi:10.1145/3122986.3122995.

Beggiato, M., Witzlack, C., Springer, S., & Krems, J. F. (2017). The Right Moment for Braking as Informal Communication Signal between Automated Vehicles and Pedestrians in Crossing Situations. In N. A. Stanton (Ed.), Advances in Human Aspects of Transportation. Proceedings of the AHFE 2017 Conference on Human Factors in Transportation, July 17-21, 2017, Los Angeles, California, USA (pp. 1072–1081). Springer Verlag. doi:10.1007/978-3-319-60441-1_101.

Witzlack, C., Beggiato, M., & Krems, J. F. (2016). Interaktionssequenzen zwischen Fahrzeugen und Fußgängern im Parkplatzszenario als Grundlage für kooperativ interagierende Automatisierung. In VDI (Eds.). Fahrerassistenz und automatisiertes Fahren, VDI-Berichte 2288 (p. 323-336). Düsseldorf: VDI-Verlag.

Page 40: InMotion - UNECE

Chemnitz University of Technology Department of Psychology Cognitive and Engineering Psychology

WP 2 external HMI Matthias Beggiato, Isabel Neumann, Ann-Christin Hensch

Thank you for your attention!

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Matthias Beggiato +49 371 531 38654 matthias.beggiato@ psychologie.tu-chemnitz.de

Isabel Neumann +49 371 531 37767 isabel.neumann@ psychologie.tu-chemnitz.de

Ann-Christin Hensch +49 371 531 35419 ann-christin.hensch@ psychologie.tu-chemnitz.de

Contact: