Analyzing crowdsourced ratings of speech-based take-over requests for automated driving

Bazilinskyy, Pavlo; Winter, Joost de

doi:10.1016/j.apergo.2017.05.001

Cited by 33 publications

(14 citation statements)

References 70 publications

(73 reference statements)

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“…For example, only one male voice and one female voice were provided, which means that results may be contingent on the voice generator, accent, pitch, etc. (for a similar discussion on TORs see Bazilinskyy & De Winter, 2017, in which participants rated a number of male and female voices). Also the five traffic situations are merely a selection of possible scenarios in actual automated driving.…”

Section: Limitations and Future Workmentioning

confidence: 99%

Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays

Bazilinskyy

Petermeijer

Petrovych

et al. 2018

Transportation Research Part F: Traffic Psychology and Behaviou

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116

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An important research question in the domain of highly automated driving is how to aid drivers in transitions between manual and automated control. Until highly automated cars are available, knowledge on this topic has to be obtained via simulators and self-report questionnaires. Using crowdsourcing, we surveyed 1692 people on auditory, visual, and vibrotactile take-over requests (TORs) in highly automated driving. The survey presented recordings of auditory messages and illustrations of visual and vibrational messages in traffic scenarios of various urgency levels. Multimodal TORs were the most preferred option in high-urgency scenarios. Auditory TORs were the most preferred option in lowurgency scenarios and as a confirmation message that the system is ready to switch from manual to automated mode. For low-urgency scenarios, visual-only TORs were more preferred than vibration-only TORs. Beeps with shorter interpulse intervals were perceived as more urgent, with Stevens' power law yielding an accurate fit to the data. Spoken messages were more accepted than abstract sounds, and the female voice was more preferred than the male voice. Preferences and perceived urgency ratings were similar in middle-and high-income countries. In summary, this international survey showed that people's preferences for TOR types in highly automated driving depend on the urgency of the situation.

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Section: Limitations and Future Workmentioning

confidence: 99%

Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays

Bazilinskyy

Petermeijer

Petrovych

et al. 2018

Transportation Research Part F: Traffic Psychology and Behaviou

Self Cite

116

View full text Add to dashboard Cite

show abstract

“…Both the ISO standard and SAE reports reject the use of the term "take-over" (or "takeover") to address the whole transition process. On the contrary, in literature, it has been found that the term "take-over" has been used to identify the whole transition requiring a TOR (Gonçalves et al 2015;Schwalk et al 2015;Bahram et al 2015;Melcher et al 2015;Zeeb et al 2016;Van Den Beukel et al 2016;Wright et al 2016;Körber et al 2016;Petermeijer et al 2017;Bazilinskyy and De Winter 2017;Borojeni et al 2017b;Forster et al 2017;Zeeb et al 2017;Madigan et al 2018;Van Dintel 2019;Zhang et al 2019;Clark et al 2019;Kraus et al 2020), as inherited from Gold et al (2013).…”

Section: What Does All This Leave Us With?mentioning

confidence: 99%

When terminology hinders research: the colloquialisms of transitions of control in automated driving

et al. 2022

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During the last 20 years, technological advancement and economic interests have motivated research on automated driving and its impact on drivers’ behaviour, especially after transitions of control. Indeed, once the Automated Driving System (ADS) reaches its operational limits, it is forced to request human intervention. However, the fast accumulation and massive quantity of produced studies and the gaps left behind by standards have led to an imprecise and colloquial use of terms which, as technology and research interest evolve, creates confusion. The goal of this survey is to compare how different taxonomies describe transitions of control, address the current use of widely adopted terms in the field of transitions of control and explain how their use should be standardized to enhance future research. The first outcome of this analysis is a schematic representation of the correspondence among the elements of the reviewed taxonomies. Then, the definitions of “takeover” and “handover” are clarified as two parallel processes occurring in every transition of control. A second set of qualifiers, which are necessary to unequivocally define a transition of control and identify the agent requesting the transition and the agent receiving the request (ADS or the driver), is provided. The “initiator” is defined as the agent requesting the transition to take place, and the “receiver” is defined as the agent receiving that request.

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“…People are more likely to accept takeover requests issued by the multi-modal interface because the multi-modal interface allows the driver to respond more quickly 31 . Bazilinsky et al 32 found that drivers have the highest preference for warning interfaces that include all three modalities (auditory, tactile, and visual). The current research conclusions on multimodal takeover interface are not uniform, and there is a lack of research on trains.…”

Section: Level 3 Highmentioning

confidence: 99%

Influence of Multi-Modal Warning Interface on Takeover Efficiency of Autonomous High-Speed Train

Jing

Dai²,

Yao

et al. 2022

Preprint

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As a large-scale public transport mode, the driving safety of high-speed rail has a profound impact on public health. In this study, we determined the most efficient multi-modal warning interface for automatic driving of a high-speed train and put forward suggestions for optimization and improvement. Forty-eight participants were selected, and a simulated 350 km/h high-speed train driving experiment equipped with a multi-modal warning interface was carried out. Then, the parameters of eye movement and behavior were analyzed by independent sample Kruskal-Wallis test and one-way analysis of variance. The results showed that the current level 3 warning visual interface of a high-speed train had the most abundant warning graphic information, but it failed to increase the takeover efficiency of the driver. The visual interface of the level 2 warning was more likely to attract the attention of drivers than the visual interface of the level 1 warning, but it still needs to be optimized in terms of the relevance of and guidance between graphic-text elements. The multi-modal warning interface had a faster response efficiency than the single-modal warning interface. The auditory-visual multi-modal interface had the highest takeover efficiency and was suitable for the most urgent (level 3) high-speed train warning. The introduction of an auditory interface could increase the efficiency of a purely visual interface, but the introduction of a tactile interface did not improve the efficiency. These findings can be used as a basis for the interface design of automatic driving high-speed trains and help improve the active safety of automatic driving high-speed trains, which is of great significance to protect the health and safety of the public.

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Analyzing crowdsourced ratings of speech-based take-over requests for automated driving

Cited by 33 publications

References 70 publications

Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays

Take-over requests in highly automated driving: A crowdsourcing survey on auditory, vibrotactile, and visual displays

When terminology hinders research: the colloquialisms of transitions of control in automated driving

Influence of Multi-Modal Warning Interface on Takeover Efficiency of Autonomous High-Speed Train

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