Designing the Communication with Automated Vehicles: The Case of Elderly Pedestrians

Abstract

To communicate perception of and intent to other road users, implicit and explicit forms of communication for automated vehicles (AVs) are currently under research and development. Despite being a relevant group for road safety, the requirements of elderly pedestrians are not sufficiently reflected in current communication concepts. Age-related impairments of sensory, cognitive and motor abilities of elderly pedestrians are presented and their relevance for design criteria of implicit and explicit forms of communication for AVs derived. The specification of design criteria presented in this paper allows further research to examine the design of implicit and explicit communication for AVs with elderly pedestrians.

Full text

Designing the Communication with Automated Vehicles: The Case of Elderly
Pedestrians
PHILIP JOISTEN, NINA THEOBALD, SARAH S. SCHWINDT, JONAS WALTER, and BETTINA
ABENDROTH, Technical University of Darmstadt, Germany
To communicate perception of and intent to other road users, implicit and explicit forms of communication for automated vehicles
(AVs) are currently under research and development. Despite being a relevant group for road safety, the requirements of elderly
pedestrians are not suciently reected in current communication concepts. Age-related impairments of sensory, cognitive and motor
abilities of elderly pedestrians are presented and their relevance for design criteria of implicit and explicit forms of communication for
AVs derived. The specication of design criteria presented in this paper allows further research to examine the design of implicit and
explicit communication for AVs with elderly pedestrians.
Additional Key Words and Phrases: elderly pedestrians, automated vehicles, human-machine interaction, communication, age-related
impairments
1 BACKGROUND
As the proportion of elderly people (age 65 and older) in western countries is increasing [
38
], there is a growing
interest regarding the mobility needs of this demographic group [
57
]. Walking as a pedestrian is one common mode of
transportation for elderly people [
33
]. Among all road users, pedestrians represent an especially vulnerable group in
road trac [
37
]. While elderly people represent 20% of the EU population they account for 47% of all pedestrians’ deaths
in the EU [
3
], making them a critical age group regarding road safety. Aging brings greater diculties in crossing the
road especially in complex trac scenarios such as two-way roads [
17
,
19
,
40
]. Diculties in road-crossing behavior of
the elderly have been attributed to age-related declines in sensory, cognitive and/or motor abilities [16,41].
Today, road crossings by pedestrians can be accompanied by the interaction with human drivers, which is character-
ized by an exchange of implicit (e.g. deceleration, gait) and explicit (e.g. hand gestures) signals [
14
,
55
]. In doing so road
users communicate perception of and intention to other road users in their environment [
35
]. With automated vehicles
(AVs) in the urban transportation system new challenges arise [5], one of them being the communication of AVs with
other road users [
42
,
55
]. While research has focused on designing implicit and explicit forms of communication for
AVs [e.g. 1,48], elderly people have only seldom been the user group of design and evaluation [e.g. 26,34,39,44].
This paper highlights the gap between current design concepts of implicit and explicit communication for AVs
and the requirements of elderly pedestrians. Therefore, age-related impairments of elderly pedestrians are described
(section 2) and their relevance for current developments in implicit and explicit forms of communication for AVs derived
(section 3). This (brief) review shall inform further research examining the design of communication between elderly
people and AVs by deriving research gaps in literature (section 4).
2 AGE-RELATED IMPAIREMENTS OF ELDERLY PEDESTRIANS
To ensure safe road-crossing decisions, pedestrians must “share their attention, select the most appropriate information
and inhibit the information that is non relevant” [
16
, p. 136], followed by the execution of an action to cross the road.
Authors’ address: Philip Joisten, p.joisten@iad.tu-darmstadt.de; Nina Theobald, n.theobald@stud.tu-darmstadt.de; Sarah S. Schwindt, s.schwindt@iad.tu-
darmstadt.de; Jonas Walter, j.walter@iad.tu-darmstadt.de; Bettina Abendroth, abendroth@iad.tu-darmstadt.de, Technical University of Darmstadt,
Department of Mechanical Engineering, Institute of Ergnomics and Human Factors, Otto-Berndt-Straße 2, Darmstadt, 64287, Germany.
1

2 Joisten et al.
Thus, participation as a pedestrian in road trac requires the integration of sensory, cognitive and motor abilities
[16,60].
2.1 Sensory abilities
The visual and auditory perception are of great relevance in road-crossing decisions of pedestrians [
43
]. Known age-
related deteriorations of visual perception are the decline of central and dynamic visual acuity [
8
,
24
] with the latter
being of particular relevance for motion perception [
54
]. Moreover, the aging eyes’ impaired accommodation hinders
their ability to adapt between focusing near and far [
8
]. While contrast sensitivity as well as color discrimination
are reduced, glare sensitivity is increased [
8
,
24
]. Since the interaction of the foveal and peripheral visual eld forms
the basis for visual orientation, age-related narrowing of the “Useful Field of View” must be considered as well [
52
].
With regard to auditory perception, the elderly person’s ability to perceive and locate acoustic signals and to lter out
unwanted sounds is hampered [18,51].
Age-related declines in sensory abilities have been shown to impact road-crossing decisions of pedestrians [
15
,
16
].
The visual perception is necessary to perceive objects at a distance, to recognize signs, signals and other road users and
to correctly estimate speeds. Because of their limited sensory abilities, elderly people have diculties in estimating the
time-of-arrival (TTA) of approaching objects and cars [4,50], potentially leading to dangerous crossing decisions.
2.2 Cognitive abilities
Cognitive abilities refer to skills such as attention, information processing and the ability to reect and represent
memory content [
47
]. Elderly people need more time to assess a stimulus’ contextual relevance and are easier and longer
distracted by irrelevant stimuli due to impaired inhibition [
22
,
25
]. Having diculty in exibly distributing attention
between two tasks, maintaining a prioritized focus and switching between them, situations requiring divided attention
pose problems for the elderly [
23
,
53
]. Combined with the age-related reduction of a person’s limited working memory
[
7
] and the reduced speed of information processing [
49
], the search for target stimuli in complex environments under
time constraints is impeded [59].
Due to age-related declines in cognitive abilities, elderly pedestrians are more likely to have diculties in the decision
making process when crossing a road, especially under time pressure [
60
]. Related gap-selection issues of elderly
pedestrians [
32
,
41
] are also attributed to decreasing cognitive abilities that pedestrians need to focus on relevant
information and to make timely, correct decisions [16].
2.3 Motor abilities
Changes in the bone, joint, ligament and muscle apparatus have eects on mobility, speed of movement, balance,
coordination and strength [
10
]. A decrease of muscle strength of up to 30-40% over the lifespan [
46
] and a decrease
in mobility of about 3-5% per decade [
56
] reduce the elderly person’s ability, power, controllability and precision of
movement execution [
46
]. With increasing age, sensomotoric tasks like everyday movement patterns require more
conscious control and cognitive resources, limiting the capacity to perform multiple activities simultaneously [31].
Elderly pedestrians display slower walking speeds while crossing a road [
32
,
40
] whereby walking time is a relevant
factor to predict the safety of pedestrian crossing behavior [
27
]. Diculties to adapt their walking speed to prevailing
trac conditions further explain gap-selection problems of the elderly [15,16].

Designing the Communication with Automated Vehicles: The Case of Elderly Pedestrians 3
3 DESIGN CONSIDERATIONS FOR THE COMMUNICATION BETWEEN AUTOMATED VEHICLES AND
ELDERLY PEDESTRIANS
While research on age-related impairments and their inuence on the behavior of elderly pedestrians exists, little
research has been done regarding the communication and interaction between AVs and elderly pedestrians. Nevertheless,
initial studies have shown that light signals on AVs (light bar on the rooftop) were assessed more positively by elderly
pedestrians compared to younger pedestrians (aged 21-30 years) in terms of usefulness and satisfaction [
26
]. Another
study identied the preference of elderly people for multimodal designs (combination of visual and auditory signals) of
external Human-Machine Interfaces (eHMIs, e.g. light signals or displays on AVs) but could not nd any dierence
in reported user experience (using the UEQ [
29
]) between younger (20-30 years old) and elderly pedestrians [
39
].
Furthermore, a video analysis revealed dierences in road user behavior of older people when interacting with an
AV, with older pedestrians (aged 55 years and above) stopping more often to give priority to the AV [
34
]. This result
was supported by a simulation experiment in which older pedestrians (aged 40-69 years) were more hesitant about
interacting with an AV when crossing a road [44].
While these studies show that there are age-related dierences both in subjective assessment as well as behavior
when interacting with AVs, none of the above-mentioned research explicitly considered age-related impairments
of pedestrians in the design of implicit and explicit forms of communication for AVs. Table 1compiles age-related
impairments and resulting diculties of elderly pedestrians and matches them with the most relevant design criteria of
current developments for the communication design of AVs. Research on older drivers served as a basis to assign design
criteria to age-related impairments of pedestrians [e.g.
11
]. Further, the relevance of age-related impairments, resulting
diculties of elderly pedestrians and signicance of design criteria were discussed in two structured feedbacks during
the preparation of this position paper.
Table 1. Age-related impairments of elderly pedestrians and their relevance for designing pedestrian-AV-communication
Age-related impairments Diculties of elderly pedestrians Most relevant design criteria
Sensory abilities
Central acuity Object perception, sign and signal recognition
Dynamic acuity Motion perception, TTA and speed estimation
Accommodation Change of focus between near and far objects
Contrast sensitivity Distinguish between objects and backgrounds Modality, coding, position
Color discrimination Distinguish between colors of signals
Glare sensitivity Loss of central acuity in bright light
Hearing Hearing loss, locating of acoutic signals
Cognitive abilities
Inhibition Suppression of irrelevant information
Selective attention Concentration on a certain stimuli in the environment
Divided attention Attend dierent stimuli at the same time Content, coding,
Working memory Amount of available cognitive resources to store information perspective, timing
Speed of information processing Making timely decisions
Decision making under time pressure Making correct decisions (e.g. gap-selection)
Motor abilities
Movement execution Speed of walking and head rotation Content, timing, position

4 Joisten et al.
Relevant design criteria determining the communication between AVs and pedestrians are the content road users
are exchanging [
6
,
21
] and the timing of the communication [
1
,
12
], e.g. the starting point of deceleration to convey a
signal [
1
]. Furthermore, explicit forms of communication (via eHMIs) include the criteria of modality [
9
], perspective
(e.g. ego- vs. allocentric) [
6
], coding of information (e.g. form, size, color, frequency and amplitude) [
2
,
6
,
13
,
30
,
58
] and
position on the vehicle [2,20].
Because of declines in sensory abilities elderly pedestrians have diculties to perceive objects at a distance and
correctly estimate speeds [
16
,
41
]. However, adaptation in speed is a main transmitter of implicit forms of communication
of vehicles [
1
]. Having diculties with this communication form, elderly pedestrians might benet more from eHMIs
(e.g. visual and/or auditory stimuli). But also in the design of explicit forms of communication of dierent modalities,
coding and positioning of information transmission must be adapted to age-related impairments of sensory abilities
(e.g. decline of central acuity).
Due to declines in cognitive abilities, elderly pedestrians have diculties to focus on relevant information, to exibly
distribute their attention and to make timely, correct road-crossing decisions [
16
]. To enable elderly pedestrians to
process the information conveyed correctly and in a timely manner, the information must be presented in an easily
graspable form being the result of careful decisions in the relevant design criteria of content, coding and perspective.
Another important design criteria to be considered here is the timing of communication [
1
,
12
]. Elderly pedestrians
could benet of an early communication onset, relieving them from decision making under time pressure.
Declines in motor abilites of elderly pedestrians are related to diculties of adapting a chosen road crossing strategy
[
32
,
41
]. In terms of content, AVs should therefore avoid communication that forces elderly pedestrians to (rapidly) adjust
their current road crossing strategy. In addition, the immobility of the elderly people’s neck must be considered when
determining the information position. Finally, an AV needs to have a high contextual understanding of its environment
in order to take the elderly pedestrians’ lower walking speeds into account and to give them enough time to execute
their preferred strategy.
4 CONCLUSION
Despite being a relevant group for pedestrian road safety, current developments of implicit and explicit forms of
communication for AVs have neglected the requirements of elderly pedestrians. Age-related impairments contribute to
diculties of elderly pedestrians when crossing a road [
16
,
17
] but this has not been cooperated yet in any communication
designs for AVs. The specication of design criteria presented in this paper allows further research to examine the
design of implicit and explicit communication for AVs with elderly pedestrians.
Elderly pedestrians seem to perceive AVs as useful [
45
] or even less risky than being around human-operated trac
[
28
]. In order to increase the chances of improving road safety of elderly pedestrians, the human-oriented design
approach for the elderly pedestrian population should be enhanced and pursued. Further research could investigate
compensation strategies for age-related impairments [
41
] and self-regulation behavior of elderly pedestrians [
36
] in
their interaction with AVs.
ACKNOWLEDGMENTS
This research was funded by research project @CITY-AF, carried out at the request of the Federal Ministry for Economic
Aairs and Energy (BMWi), under research project No. 19A18003M. The authors are solely responsible for the content.

Designing the Communication with Automated Vehicles: The Case of Elderly Pedestrians 5
REFERENCES
[1]
Claudia Ackermann, Matthias Beggiato, Luka-Franziska Bluhm, Alexandra Löw, and Josef F. Krems. 2019. Deceleration parameters and their
application as informal communication signal between pedestrians and automated vehicles. Transp Res Part F Trac Psychol Behav 62 (Apr 2019),
757–768. https://doi.org/10.1016/j.trf.2019.03.006
[2]
Claudia Ackermann, Matthias Beggiato, Sarah Schubert, and Josef F. Krems. 2019. An experimental study to investigate design and assessment
criteria: What is important for communication between pedestrians and automated vehicles? Appl Ergon 75 (Feb 2019), 272–282. https://doi.org/10.
1016/j.apergo.2018.11.002
[3]
Dovilé Adminaité-Fodor and Graziella Jost. 2020. How Safe is Walking and Cycling in Europe? PIN Flash Report 38. European Transport Safety
Council.
[4]
Georg J. Andersen and AnnJudel Enriquez. 2006. Aging and the detection of observers and moving object collisions. Psychol. Aging 21, 1 (Mar 2006),
74–85. https://doi.org/10.1037/0882-7974.21.1.74
[5]
Saeed A. Bagloee, Madjid Tavana, Mohsen Asadi, and Tracey Oliver. 2016. Autonomous vehicles: challenges, opportunities, and future implications
for transportation policies. J. Mod. Transport. 24, 4 (Dec 2016), 285–303. https://doi.org/10.1007/s40534- 016-0117- 3
[6]
Pavlo Bazilinskyy, Dimitra Dodou, and Joost de Winter. 2019. Survey on eHMI concepts: The eect of text, color, and perspective. Transp Res Part F
Trac Psychol Behav 67 (Nov 2019), 175–194. https://doi.org/10.1016/j.trf.2019.10.013
[7]
Erika Borella, Barbara Carretti, and Rossana De Beni. 2008. Working memory and inhibition across the adult life-span. Acta Psychol. 128, 1 (May
2008), 33–44. https://doi.org/10.1016/j.actpsy.2007.09.008
[8]
Amos S. Cohen. 2008. Wahrnehmung als Grundlage der Verkehrsorientierung bei nachlassender Sensorik während der Alterung. In Leistungsfähigkeit
und Mobilität im Alter, B. Schlag (Ed.). TÜV Media GmbH, Cologne, Germany, 65–80.
[9]
Mark Colley, Marcel Walch, Jan Gugenheimer, and Enrico Rukzio. 2019. Including People with Impairments from the Start: External Communication
of Autonomous Vehicles. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular Applications:
Adjunct Proceedings (AutomotiveUI ’19). September 21 - 25, 2019, Utrecht, Netherlands. ACM Inc., New York, NY, 307–314. https://doi.org/10.1145/
3349263.3351521
[10]
Maricarmen Cruz-Jimenez. 2017. Normal Changes in Gait and Mobility Problems in the Elderly. Phys Med Rehabil Clin N Am. 28, 4 (Nov 2017),
713–725. https://doi.org/10.1016/j.pmr.2017.06.005
[11]
Ragnhild J. Davidse. 2006. Older Drivers and ADAS. Which Systems Improve Road Safety? IATSS Res. 30, 1 (2006), 6–20. https://doi.org/10.1016/S0386-
1112(14)60151-5
[12]
Koen de Clerq, Andre Dietrich, Juan Pablo Núnez Velasco, Joost de Winter, and Riender Happee. 2019. External Human-Machine Interfaces on
Automated Vehicles: Eects on Pedestrian Crossing Decisions. Hum Factors 61, 8 (Dec 2019), 1153–1370. https://doi.org/10.1177/0018720819836343
[13]
Debargha Dey, Azra Habibovic, Bastian Peging, Marieke Martens, and Jacques Terken. 2020. Color and Animation Preferences for a Light Band
eHMI in Interactions Between Automated Vehicles and Pedestrians. In Proceedings of the 2020 CHI Conference on Human Factors in Computing
Systems (CHI ’20). April 25 - 30, 2020, Honolulu, HI, USA. ACM Inc., New York, NY, 1–13. https://doi.org/10.1145/3313831.3376325
[14]
Debargha Dey and Jacques Terken. 2017. Pedestrian Interaction with Vehicles: Roles of Explicit and Implicit Communication. In Proceedings of the
9th ACM International Conference on Automotive User Interfaces and Interactive Vehicular Applications (AutomotiveUI ’17). September 24 - 27, 2017,
Oldenburg, Germany. ACM Inc., New York, NY, USA, 109–113. https://doi.org/10.1145/3122986.3123009
[15]
Aurélie Dommes and Viola Cavallo. 2011. The role of perceptual, cognitive, and motor abilities in street-crossing decisions of young and older
pedestrians. Ophthal Physl Opt 31, 3 (Mar 2011), 292–301. https://doi.org/10.1111/j.1475-1313.2011.00835.x
[16] Aurélie Dommes, Viola Cavallo, and Jennifer Oxley. 2013. Functional declines as predictors of risky street-crossing decisions in older pedestrians.
Accid. Anal. Prev. 59 (Oct 2013), 135–143. https://doi.org/10.1016/j.aap.2013.05.017
[17]
Aurélie Dommes, Tristan Le Ley, Fabrice Vienne, Nguyen-Thong Dang, Alexandra Perrot Beaudoin, and Manh Cuong Do. 2015. Towards an
explanation of age-related diculties in crossing a two-way street. Accid. Anal. Prev. 85 (Dec 2015), 229–238. https://doi.org/10.1016/j.aap.2015.09.022
[18]
Werner Draeger and Dorothée Klöckner. 2001. Ältere Menschen zu Fuß und mit dem Fahrrad unterwegs. In Mobilität älterer Menschen, A. Flade,
M. Limbourg, and B. Schlag (Eds.). Springer Fachmedien, Wiesbaden, Germany, 41–67. https://doi.org/10.1007/978-3-663- 10820-7_4
[19]
George Dunbar. 2012. The relative risk of nearside accidents is high for the youngest and oldest pedestrians. Accid. Anal. Prev. 45 (Mar 2012),
517–521. https://doi.org/10.1016/j.aap.2011.09.001
[20]
Yke B. Eisma, S. van Bergen, S.M. ter Brake, M.T.T. Hensen, Willem J. Tempelaar, and Joost C.F. de Winter. 2020. External Human-Machine Interfaces:
The Eect of Display Location on Crossing Intentions and Eye Movements. Information 11, 1 (Jan 2020), 1–18. https://doi.org/10.3390/info11010013
[21]
Stefanie M. Faas, Lesley-Ann Mathis, and Martin Baumann. 2020. External HMI for self-driving vehicles: Which information shall be displayed?
Transp Res Part F Trac Psychol Behav 68 (Jan 2020), 171–186. https://doi.org/10.1016/j.trf.2019.12.009
[22]
Michael Falkenstein, Jörg Hoormann, and Joachim Hohnsbein. 2002. Inhibition-related ERP components: variation with age and time-on task.
Journal of Psychophysiology 16, 3 (2002), 167–175. https://doi.org/10.1027//0269-8803.16.3.167
[23]
Myra A. Fernandes, Anda Pacurar, Morris Moscovitch, and Cheryl Grady. 2006. Neural correlates of auditory recognition under full and divided
attention in younger and older adults. Neuropsychologia 44, 12 (2006), 2452–2464. https://doi.org/10.1016/j.neuropsychologia.2006.04.020
[24]
Gunilla Haegerstrom-Portnoy, Marilyn E. Schneck, and John A. Brabyn. 1999. Seeing into Old Age: Vision Function Beyond Acuity. Optom. Vis. Sci.
76, 3 (Mar 1999), 141–158. https://doi.org/10.1097/00006324- 199903000-00014

6 Joisten et al.
[25]
Melanie Hahn, Nele Wild-Wall, and Michael Falkenstein. 2011. Age-related dierences in performance and stimulus processing in dual task situation.
Brain Res 1414, 26 (Sep 2011), 66–76. https://doi.org/10.1016/j.brainres.2011.07.051
[26]
Ann-Cristin Hensch, Isabel Neumann, Matthias Beggiato, Josephine Halama, and Josef F. Krems. 2019. Steady, ashing, sweeping – An exploratory
evaluation of light signals as an eHMI in automated driving. In Poster presented at the Human Factors and Ergonomics Society Europe Chapter 2019
Annual Conference. HFES Europe Chapter, Nantes, France.
[27]
Carol Holland and Ros Hill. 2010. Gender dierences in factors predicting unsafe crossing decisions in adult pedestrians across the lifespan: A
simulation study. Accid. Anal. Prev. 42, 4 (Jul 2010), 1097–1106. https://doi.org/10.1016/j.aap.2009.12.023
[28]
Lynn M. Hulse, Hui Xie, and Edwin R. Galea. 2018. Perceptions of autonomous vehicles: Relationships with road users, risk, gender and age. Saf Sci
102 (Feb 2018), 1–13. https://doi.org/10.1016/j.ssci.2017.10.001
[29]
Bettina Laugwitz, Theo Held, and Martin Schrepp. 2008. Construction and Evaluation of a User Experience Questionnaire. In Proceedings of the 4th
Symposium of the Workgroup Human-Computer Interaction and Usability Engineering of the Austrian Computer Society (USAB 2008). November 20 -
21, 2008, Graz Austria. Springer, Berlin, Heidelberg, Germany, 63–76. https://doi.org/10.1007/978-3-540- 89350-9_6
[30]
Andreas Löcken, Carmen Golling, and Andreas Riener. 2019. How Should Automated Vehicles Interact with Pedestrians? A Comparative Analysis
of Interaction Concepts in Virtual Reality. In Proceedings of the 11th International Conference on Automotive User Interfaces and Interactive Vehicular
Applications (AutomotiveUI ’19). September 21 - 25, 2019, Utrecht, Netherlands. ACM Inc., New York, NY, 262–274. https://doi.org/10.1145/3342197.
3344544
[31]
Ulman Lindenberger, Michael Marsiske, and Paul B. Baltes. 2000. Memorizing while walking: increase in dual-task costs from young adulthood to
old age. Psychol Aging 15, 3 (Sep 2000), 417–436. https://doi.org/10.1037//0882- 7974.15.3.417
[32]
Régis Lobjois and Viola Cavallo. 2009. The eects of aging on street-crossing behavior: From estimation to actual crossing. Anal. Prev. 41, 2 (Mar
2009), 259–267. https://doi.org/10.1016/j.aap.2008.12.001
[33]
Sebastien Lord and Nicolas Luxembourg. 2007. The mobility of elderly residents living in suburban territories: mobility experiences in Canada and
France. J. Hous. Elder. 20, 4 (Oct 2007), 130–121. https://doi.org/10.1300/J081v20n04_07
[34]
Ruth Madigan, Sina Nordho, Charles Fox, Roja E. Amini, Tyron Louw, Marc Wilbrink, Anna Schieben, , and Natasha Merat. 2019. Understanding
interactions between Automated Road Transport Systems and other road users: A video analysis. Transp Res Part F Trac Psychol Behav 66 (Oct
2019), 196–213. https://doi.org/10.1016/j.trf.2019.09.006
[35]
Karthik Mahadevan, Sowmya Somanath, and Ehud Sharlin. 2018. Communication Awareness and Intent in Autonomous Vehicle-Pedestrian
Interaction. In Proceedings of the 2018 CHI Conference on Human Factors in Computing Systems (CHI ’18). April 21 - 26, 2018, Montréal, QC, Canda.
ACM Inc., New York, NY, USA, 1–12. https://doi.org/10.1145/3173574.3174003
[36]
Yoshinori Nakagawa. 2019. Elderly pedestrians’ self-regulation failures and crash involvement: The development of typologies. Accid. Anal. Prev.
133, Article 105281 (Dec 2019), 13 pages. https://doi.org/10.1016/j.aap.2019.105281
[37]
Tobias Niebuhr, Mirko Junge, and Erik Rosén. 2016. Pedestrian injury risk and the eect of age. Accid. Anal. Prev. 86 (Jan 2016), 121–128.
https://doi.org/10.1016/j.aap.2015.10.026
[38] OECD. 2020. Elderly population (indicator). Retrieved July 30, 2020 from https://data.oecd.org/pop/elderly- population.htm#indicator-chart
[39]
Ina Othersen, Antonia S. Conti-Kufner, André Dietrich, Philipp Maruhn, and Klaus Bengler. 2018. Designing for Automated Vehicle and Pedestrian
Communication: Perspectives on eHMIs from Older and Younger Persons. In Proceedings of the Human Factors and Ergonomics Society Europe
Chapter 2018 Annual Conference. HFES Europe Chapter, Berlin,Germany, 135–148.
[40]
Jennie Oxley, Brian Fildes, Elfriede Ihsen, Judight Charlton, and Ross Day. 1997. Dierences in trac judgements between young and old adult
pedestrians. Accid. Anal. Prev. 29, 6 (Nov 1997), 839–847. https://doi.org/10.1016/S0001-4575(97)00053-5
[41]
Jennifer A. Oxley, Elfriede Ihsen, Brian N. Fildes, Judith L. Charlton, and Ross H. Day. 2005. Crossing roads safely: An experimental study of age
dierences in gap selection by pedestrians. Accid. Anal. Prev. 37 (Sep 2005), 962–971. https://doi.org/10.1016/j.aap.2005.04.017
[42]
John Parkin, Benjamin Clark, William Clayton, Miriam Ricci, and Graham Parkhurst. 2018. Autonomous vehicle interactions in the urban street
environment: a research agenda. P I CIVIL ENG-MUNIC 171, 1 (Mar 2018), 15–25. https://doi.org/10.1680/jmuen.16.00062
[43]
Brian J. Pugliese, Benjamin K. Barton, Shane J. Davis, and Gerardo Lopez. 2020. Assessing pedestrian safety across modalities via a simulated vehicle
time-to-arrival task. Accid. Anal. Prev. 134, Article 105344 (Jan 2020), 10 pages. https://doi.org/10.1016/j.aap.2019.105344
[44]
Solmaz Razmi Rad, Concalo Homem de Almeida Correia, and Marjan Hagenzieker. 2020. Pedestrians‘ road crossing behaviour in front of automated
vehicles: Results from a pedestrian simulation experiment using agent-based modelling. Transp Res Part F Trac Psychol Behav 69 (Feb 2020),
101–119. https://doi.org/10.1016/j.trf.2020.01.014
[45]
Md Mahmudur Rahman, Shuchisnigdha Deb, Lesley Strawderman, Reuben Burch, and Brian Smith. 2019. How the older population perceives
self-driving vehicles. Transp Res Part F Trac Psychol Behav 65 (Aug 2019), 242–257. https://doi.org/10.1016/j.trf.2019.08.002
[46]
G. Rinkenauer. 2008. Motorische Leistungsfähigkeit im Alter. In Leistungsfähigkeit und Mobilität im Alter, B. Schlag (Ed.). TÜV Media GmbH,
Cologne, Germany, 143–180.
[47]
Peter Robinson. 2001. Abilities to Learn: Cognitive Abilities. In Encyclopedia of the Sciences of Learning, Norbert M. Seel (Ed.). Springer, Boston.
https://doi.org/10.1007/978-1- 4419-1428- 6_620
[48]
Alexandros Rouchitsas and Hakan Alm. 2019. External Human-Machine Interfaces for Autonomous Vehicle-to-Pedestrian Communication: A
Review of Empirical Work. Front. Psychol. 10, 2757 (Dec 2019), 1–12. https://doi.org/10.3389/fpsyg.2019.02757

Designing the Communication with Automated Vehicles: The Case of Elderly Pedestrians 7
[49]
Timothy A. Salthouse. 1996. The processing-speed theory of adult age dierences in cognition. Psychol Rev. 103, 3 (Jul 1996), 403–428. https:
//doi.org/10.1037/0033-295x.103.3.403
[50]
William Schi, Rivka Oldak, and Varsha Shah. 1992. Aging person’s estimates of vehicular motion. Psychol. Aging 7, 4 (Dec 1992), 518–525.
https://doi.org/10.1037//0882-7974.7.4.518
[51]
Bernhard Schlag. 2008. Wie sicher sind die Älteren im Straßenverkehr? In Leistungsfähigkeit und Mobilität im Alter, B. Schlag (Ed.). TÜV Media
GmbH, Cologne, Germany, 19–36.
[52]
Allison B. Sekuler, Patrick J. Bennett, and Mortimer Mamelak. 2000. Eects of aging on the useful eld of view. Experimental aging research 26, 2
(Apr 2000), 103–120. https://doi.org/10.1080/036107300243588
[53]
Ka-Chun Siu, Li-Shan Chou, Ulrich Mayr, Paul van Donkelaar, and Marjorie H. Woollacott. 2008. Does inability to allocate attention contribute to
balance constrains during gait in older adults? J Gerontol A Biol Sci Med Sci. 63, 12 (Dec 2008), 1364–1369. https://doi.org/10.1093/gerona/63.12.1364
[54]
Robert J. Snowden and Emma Kavanagh. 2006. Motion perception in the aging visual system: minimum motion, motion coherence, and speed
discrimination thresholds. Perception 35, 1 (Jan 2006), 9–24. https://doi.org/10.1068/p5399
[55]
Sergiu C. Stanciu, David W. Eby, Lisa J. Molnar, Renée M. St. Louis, Nicole Zanier, and Lidia P. Kostyniuk. 2018. Pedestrians/Bicyclists and
Autonomous Vehicles: How Will They Communicate? Transp. Res. Rec. 2672, 22 (Dec 2018), 58–66. https://doi.org/10.1177/0361198118777091
[56]
Susanne Tittlbach. 2002. Entwicklung der körperlichen Leistungsfähigkeit. Eine prospektive Längsschnittstudie mit Personen im mittleren und späteren
Erwachsenenalter. Ph.D. Dissertation. Faculty of Humanities and Social Sciences, Karlsruhe Institute of Technology.
[57]
Isabelle Tournier, Aurélie Dommes, and Viola Cavallo. 2016. Review of safety and mobility issues among older pedestrians. Accid. Anal. Prev. 91 (Jun
2016), 24–35. https://doi.org/10.1016/j.aap.2016.02.031
[58]
Annette Werner. 2018. New Colours for Autonomous Driving: An Evaluation of Chromaticities for the External Lighting Equipment of Autonomous
Vehicles. Colour Turn 1 (2018), 1–14. https://doi.org/10.25538/tct.v0i1.692
[59]
Nele Wild-Wall, Joachim Hohnsbein, and Michael Falkenstein. 2007. Eects of aging on cognitive task preparation as reected by event-related
potentials. Clin Neurophysiol. 118, 3 (Mar 2007), 558–569. https://doi.org/10.1016/j.clinph.2006.09.005
[60]
Giuseppe A. Zito, Dario Cazzoli, L. Scheer, Michael Jäger, René Müri, Urs P. Mosimann, T. Nyeler,Fred W. Mast, and Tobias Nef. 2015. Street crossing
behavior in younger and older pedestrians: an eye- and head-tracking study. BMC Geriatr 15, 176 (Dec 2015), 1–10. https://doi.org/10.1186/s12877-
015-0175- 0
A VIDEO
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