Self-Driving Vehicles and Pedestrian Interaction: Does an External Human-Machine Interface Mitigate the Threat of a Tinted Windshield or a Distracted Driver?

Faas, Stefanie M.; Stange, Vanessa; Baumann, Martin

doi:10.1080/10447318.2021.1886483

Cited by 30 publications

(8 citation statements)

References 40 publications

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“…A recent study by Faas et al [58] appears to confirm our findings using a Wizard-of-Oz method. More specifically, they concluded that 'without an eHMI, pedestrians felt significantly less safe if the windshield was tinted or the driver was distracted as compared to an attentive driver' (p. 1364).…”

Section: Novel Findingssupporting

confidence: 90%

Blinded windows and empty driver seats: The effects of automated vehicle characteristics on cyclists’ decision‐making

Bazilinskyy

Dodou

Eisma

et al. 2022

IET Intelligent Trans Sys

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Automated vehicles (AVs) may feature blinded (i.e. blacked-out) windows and external human-machine interfaces (eHMIs), and the driver may be inattentive or absent, but how these features affect cyclists is unknown. In a crowdsourcing study, participants viewed images of approaching vehicles from a cyclist's perspective and decided whether to brake. The images depicted different combinations of traditional vehicles versus AVs, eHMI presence, vehicle approach direction, driver visibility/window-blinding, visual complexity of the surroundings, and distance to the cyclist (urgency). The results showed that the eHMI and urgency level had a strong impact on crossing decisions, whereas visual complexity had no significant influence. Blinded windows caused participants to brake for the traditional vehicle. A second crowdsourcing experiment aimed to clarify the findings of Experiment 1 by also requiring participants to detect the vehicle features. It was found that the eHMI 'GO' and blinded windows yielded high detection rates and that driver eye contact caused participants to continue pedalling. To conclude, blinded windows increase the probability that cyclists brake, and driver eye contact stimulates cyclists to continue cycling. Our findings, which were obtained with large international samples, may help elucidate how AVs (in which the driver may not be visible) affect cyclists' behaviour.

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Section: Novel Findingssupporting

confidence: 90%

Blinded windows and empty driver seats: The effects of automated vehicle characteristics on cyclists’ decision‐making

Bazilinskyy

Dodou

Eisma

et al. 2022

IET Intelligent Trans Sys

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“…Moreover, one could surmise that the effect of the state of the human driver on eye contact communication would be modulated by the presence of the eHMI (H3), as suggested by previous work (Faas et al, 2021;Bazilinskyy et al, 2022). Hence, we predicted an interaction effect between these two variables with eye communication being lessened in the presence of an attentive drivenger when the approaching car exhibited an eHMI signal (Experiments A-C).…”

Section: Introductionmentioning

confidence: 75%

“…Furthermore, the participants in this study reported a more negative experience, interpreted as distress, when imagining themselves crossing in front of a vehicle in which the driver was reading a newspaper compared to talking on the phone, with both being worse than when the driver was attentive to the road with her hands on the steering wheel ( Lagström and Lundgren, 2015 ). Interestingly, Faas et al (2021) showed that pedestrians’ degraded perception of safety in front of inattentive drivers could be mitigated with the help of an eHMI ( Faas et al, 2021 ). In the authors’ study, the use of an eHMI in the form of continuously lit strip bands placed on the AV sufficed to reinforce the participants’ perceived safety when they had to cross in front of a drivenger reading a newspaper or even a tinted-windshield-AV.…”

Section: Introductionmentioning

confidence: 99%

Crossing the street in front of an autonomous vehicle: An investigation of eye contact between drivengers and vulnerable road users

Sahaï

Labeye²,

Caroux³

et al. 2022

Front. Psychol.

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Communication between road users is a major key to coordinate movement and increase roadway safety. The aim of this work was to grasp how pedestrians (Experiment A), cyclists (Experiment B), and kick scooter users (Experiment C) sought to visually communicate with drivengers when they would face autonomous vehicles (AVs). In each experiment, participants (n = 462, n = 279, and n = 202, respectively) were asked to imagine themselves in described situations of encounters between a specific type of vulnerable road user (e.g., pedestrian) and a human driver in an approaching car. The human driver state and the communicative means of the approaching car through an external Human-Machine Interface (eHMI) were manipulated between the scenarios. The participants were prompted to rate from “never” to “always” (6-point Likert scale) the frequency with which they would seek eye contact with the human driver either in order to express their willingness to cross or to make their effective decision to cross. Our findings revealed that a passive human driver in an AV with no visual checking on the road triggered a decline in vulnerable road users’ desire to communicate by eye contact (Experiments A–C). Moreover, the results of Experiment C demonstrated that the speed screen, the text message screen, and the vibrating mobile app eHMI signals diminished kick scooter users’ desire to communicate visually with the human driver, with some age-based differences. This suggested a better comprehension of the approaching car’s intentions by the kick scooter users, driven by the features of the eHMI.

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“…From a pedestrian perspective, the impact of a distracted driver of an automated vehicle has already been studied. When the driver was distracted (e.g., reading a newspaper or talking on the phone), pedestrians showed a decreased willingness to cross (Lundgren et al, 2017) or felt less safe when crossing in front of the automated vehicle (Faas et al, 2021).…”

Section: Influence Of Crossing Pedestrian's Smartphone Usage and Situation Complexitymentioning

confidence: 99%

Intelligent Mobility in the City: The Influence of System and Context Factors on Drivers’ Takeover Willingness and Trust in Automated Vehicles

Lanzer¹,

Stoll²,

Colley

et al. 2021

Front. Hum. Dyn.

Self Cite

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Automated driving in urban environments not only has the potential to improve traffic flow and heighten driver comfort but also to increase traffic safety, particularly for vulnerable road users such as pedestrians. For these benefits to take effect, drivers need to trust and use automated vehicles. This decision is influenced by both system and context factors. However, it is not yet clear how these factors interact with each other, especially for automated driving in city scenarios with crossing pedestrians. Therefore, we conducted an online experiment in which participants (N = 68) experienced short automated rides from the driver’s perspective through an urban environment. In each of the presented videos, a pedestrian crossed the street in front of the automated vehicle while system and context factors were varied: 1) the crossing pedestrian’s intention was either visualized correctly (as crossing) or incorrectly (visualization missing) by the automated vehicle (system factor), 2) the pedestrian was either distracted by using a smartphone while crossing or not (context factor), and 3) the scenario was either more or less complex depending on the number of other vehicles and pedestrians being present (context factor). In situations with a system malfunction where the crossing pedestrian’s intention was not visualized, participants perceived the situation as more critical, had less trust in the automated system, and a higher willingness to take over control regardless of any context factors. However, when the system worked correctly, the crossing pedestrian’s smartphone usage came into play, especially in the less complex scenario. Participants perceived situations with a distracted pedestrian as more critical, trusted the system less, indicated a higher willingness to take over control, and were more uncertain about their decision. As this study demonstrates the influence of distracted pedestrians, more research is needed on context factors and their inclusion in the design of interfaces to keep drivers informed during automated driving in urban environments.

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Self-Driving Vehicles and Pedestrian Interaction: Does an External Human-Machine Interface Mitigate the Threat of a Tinted Windshield or a Distracted Driver?

Cited by 30 publications

References 40 publications

Blinded windows and empty driver seats: The effects of automated vehicle characteristics on cyclists’ decision‐making

Blinded windows and empty driver seats: The effects of automated vehicle characteristics on cyclists’ decision‐making

Crossing the street in front of an autonomous vehicle: An investigation of eye contact between drivengers and vulnerable road users

Intelligent Mobility in the City: The Influence of System and Context Factors on Drivers’ Takeover Willingness and Trust in Automated Vehicles

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