External Human–Machine Interfaces for Autonomous Vehicle-to-Pedestrian Communication: A Review of Empirical Work

Rouchitsas, Alexandros; Alm, Håkan

doi:10.3389/fpsyg.2019.02757

Cited by 86 publications

(24 citation statements)

References 43 publications

(49 reference statements)

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“…As also stated in the review by Rouchitas et al [29], these results cannot be determined in real field tests with the Wizard of Oz approach. Here the critical gap is measured via button presses, which is the gap below the subjects will not begin to cross the road [13]- [16].…”

Section: B Decision Times Of Subjectsmentioning

confidence: 87%

“…Rouchitsas et al [29] published a detailed summary of empirical studies regarding automated vehicular communication interfaces and concluded that participants clearly benefit from additional communication interfaces. This is especially true in virtual reality studies in which the decision-making of crossings in front of AVs is facilitated, and the perceived safety is higher when additional information is shown.…”

Section: Related Workmentioning

confidence: 99%

See 1 more Smart Citation

Displaying the Driving State of Automated Vehicles to Other Road Users: An International, Virtual Reality-Based Study as a First Step for the Harmonized Regulations of Novel Signaling Devices

Singer

Kobbert

Zandi

et al. 2022

IEEE Trans. Intell. Transport. Syst.

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Additional signaling devices for highly automated vehicles (AVs) that can communicate their driving state to other road users can simplify the integration process in existing road traffic. This paper presents the results of an international, virtual reality-based study conducted in China, South Korea and the USA in which subjects assume the role of a pedestrian and are placed in direct encounter situations with an AV in a parking lot. A novel communication interface consisting of three displays is attached to the AV's front and used to show additional information about its driving state. In total, three encounter scenarios are investigated: the AV approaches from the left, front and right outside of the pedestrian's line of sight. The influence of different symbol types on the subject's moving behavior, recognition of intention and perceived safety is investigated. The results show that additional signals ensure a better perception of the AV's intention and increase the perceived safety. The moving behavior of subjects is significantly changed when additional signals are used during driving tasks compared to the same tasks without such signals. The change of moving behaviour is similar in encounter situations where the AV approaches from the left and front but differs in encounter situations from the right. These results could equally be proven for all nationalities, which shows that a uniform, international solution for additional signaling devices of highly automated vehicles is possible.

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Section: B Decision Times Of Subjectsmentioning

confidence: 87%

Section: Related Workmentioning

confidence: 99%

Displaying the Driving State of Automated Vehicles to Other Road Users: An International, Virtual Reality-Based Study as a First Step for the Harmonized Regulations of Novel Signaling Devices

Singer

Kobbert

Zandi

et al. 2022

IEEE Trans. Intell. Transport. Syst.

View full text Add to dashboard Cite

show abstract

“…Also, the e-HMI background color was green or red. I previous research, it was found that color convention helped pedestrians understand the FAV intention; that is, a green e-HMI indicated it was s safe to cross, and the red implies that it was unsafe to cross ( Rouchitsas and Alm, 2019 ; Bazilinskyy et al, 2020 ). Besides, baseline images without the e-HMI were created, with a variation of car size and crossing distance (for the content of the entire images, see Supplementary Appendix 1 ).…”

Section: Methodsmentioning

confidence: 99%

Pedestrians’ Understanding of a Fully Autonomous Vehicle’s Intent to Stop: A Learning Effect Over Time

Parmet

Oron-Gilad

2020

Front. Psychol.

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This study explored pedestrians’ understanding of Fully Autonomous Vehicles (FAVs) intention to stop and what influences pedestrians’ decision to cross the road over time, i.e., learnability. Twenty participants saw fixed simulated urban road crossing scenes with a single FAV on the road as if they were pedestrians intending to cross. Scenes differed from one another in the FAV’s, distance from the crossing place, its physical size, and external Human-Machine Interfaces (e-HMI) message by background color (red/green), message type (status/advice), and presentation modality (text/symbol). Eye-tracking data and decision measurements were collected. Results revealed that pedestrians tend to look at the e-HMI before making their decision. However, they did not necessarily decide according to the e-HMIs’ color or message type. Moreover, when they complied with the e-HMI proposition, they tended to hesitate before making the decision. Overall, a learning effect over time was observed in all conditions regardless of e- HMI features and crossing context. Findings suggest that pedestrians’ decision making depends on a combination of the e-HMI implementation and the car distance. Moreover, since the learning curve exists in all conditions and has the same proportion, it is critical to design an interaction that would encourage higher probability of compatible decisions from the first phase. However, to extend all these findings, it is necessary to further examine dynamic situations.

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“…Standardized methods already exist for other research areas of traffic psychology, e.g., for the evaluation of the in-vehicle HMIs of vehicles with automated driving systems [31] or to measure the eyes-off-road time as an indicator of distraction potential when interacting with in-vehicle information systems [32,33]. In their review article on the current state of research on eHMIs, Rouchitsas and Alm [34] declared that the "standardization of relevant procedures is a fundamental requirement for effective interface evaluations and meaningful comparisons. Therefore, future conceptual and empirical work in the field should primarily be concerned with producing standardized procedures for evaluating and comparing different implementations"(p. 10).…”

Section: Introductionmentioning

confidence: 99%

Standardized Test Procedure for External Human–Machine Interfaces of Automated Vehicles

et al. 2020

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Research on external human–machine interfaces (eHMIs) has recently become a major area of interest in the field of human factors research on automated driving. The broad variety of methodological approaches renders the current state of research inconclusive and comparisons between interface designs impossible. To date, there are no standardized test procedures to evaluate and compare different design variants of eHMIs with each other and with interactions without eHMIs. This article presents a standardized test procedure that enables the effective usability evaluation of eHMI design solutions. First, the test procedure provides a methodological approach to deduce relevant use cases for the evaluation of an eHMI. In addition, we define specific usability requirements that must be fulfilled by an eHMI to be effective, efficient, and satisfying. To prove whether an eHMI meets the defined requirements, we have developed a test protocol for the empirical evaluation of an eHMI with a participant study. The article elucidates underlying considerations and details of the test protocol that serves as framework to measure the behavior and subjective evaluations of non-automated road users when interacting with automated vehicles in an experimental setting. The standardized test procedure provides a useful framework for researchers and practitioners.

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External Human–Machine Interfaces for Autonomous Vehicle-to-Pedestrian Communication: A Review of Empirical Work

Cited by 86 publications

References 43 publications

Displaying the Driving State of Automated Vehicles to Other Road Users: An International, Virtual Reality-Based Study as a First Step for the Harmonized Regulations of Novel Signaling Devices

Displaying the Driving State of Automated Vehicles to Other Road Users: An International, Virtual Reality-Based Study as a First Step for the Harmonized Regulations of Novel Signaling Devices

Pedestrians’ Understanding of a Fully Autonomous Vehicle’s Intent to Stop: A Learning Effect Over Time

Standardized Test Procedure for External Human–Machine Interfaces of Automated Vehicles

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