Determinants of take-over time from automated driving: A meta-analysis of 129 studies

Zhang, Bo; Winter, Joost de; Varotto, S.F.; Happee, Riender; Martens, Marieke

doi:10.1016/j.trf.2019.04.020

Cited by 248 publications

(214 citation statements)

References 120 publications

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“…Potentially, the engagement in concurrent tasks prevented participants of both groups from showing signs of drowsiness (Miller et al, 2015) and they were equally quick to react. The recorded TTT values correspond to data recorded by other research projects (Zhang, Varotto, & Happee, 2018).…”

Section: Hypothesis 2: Takeover Performancesupporting

confidence: 82%

Automation transparency: implications of uncertainty communication for human-automation interaction and interfaces

et al. 2019

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words)Operators of highly automated driving systems may exhibit behaviour characteristic for overtrust issues due to an insufficient awareness of automation fallibility. Consequently, situation awareness in critical situations is reduced and safe driving performance following emergency takeovers is impeded. A driving simulator study was used to assess the impact of dynamically communicating system uncertainties on monitoring, trust, workload, takeovers, and physiological responses. The uncertainty information was conveyed visually using a stylised heart beat combined with a numerical display and users were engaged in a visual search task.Multilevel analysis results suggest that uncertainty communication helps operators calibrate their trust and gain situation awareness prior to critical situations, resulting in safer takeovers.Additionally, eye tracking data indicate that operators can adjust their gaze behaviour in correspondence with the level of uncertainty. However, conveying uncertainties using a visual display significantly increases operator workload and impedes users in the execution of nondriving related tasks. Keywords: vehicle ergonomics; attitudes and behaviour; attention and vigilance; humancomputer-interaction; information displaysPractitioner summary (50 words): This paper illustrates how the communication of system uncertainty information helps operators calibrate their trust in automation and, consequently, gain situation awareness. Multilevel analysis results of a driving simulator study affirm the benefits for trust calibration and highlight that operators adjust their behaviour according to multiple uncertainty levels.

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Section: Hypothesis 2: Takeover Performancesupporting

confidence: 82%

Automation transparency: implications of uncertainty communication for human-automation interaction and interfaces

et al. 2019

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“…The reference lists of all the studies that met the search criteria were reviewed to retrieve other relevant studies. In line with Zhang et al (2018), we retrieved all types of studies, including journal publications, articles from conference proceedings, theses, reports, posters and presentation slides. This enables the identification of grey literature records and minimises publication bias.…”

Section: Information Sources and Search Strategymentioning

confidence: 99%

“…Trust has been considered a valid foundation for human-machine interaction (Hengstler et al 2016), and plays a leading role in determining the willingness of humans to rely on automated systems (Hoff and Bashir 2015), and accept AVs (Haspiel et al 2018;Molnar et al 2018;Wintersberger, Frison, and Riener 2018;Zhang et al 2018). Choi and Ji (2015) observed that individual generic trust levels positively influenced the perceived usefulness of AVs and the intentions to use them, while it reduced any related perceived risks.…”

Section: Personalitymentioning

confidence: 99%

A multi-level model on automated vehicle acceptance (MAVA): a review-based study

Nordhoff

Kyriakidis

Arem

et al. 2019

Theoretical Issues in Ergonomics Science

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136

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Automated vehicle acceptance (AVA) is a necessary condition for the realisation of higher-level objectives such as improvements in road safety, reductions in traffic congestion and environmental pollution. On the basis of a systematic literature review of 124 empirical studies, the present study proposes MAVA, a multi-level model to predict AVA. It incorporates a process-oriented view on AVA, considering acceptance as the result of a four-stage decision-making process that ranges from the exposure of the individual to automated vehicles (AVs) in Stage 1, the formation of favourable or unfavourable attitudes towards AVs in Stage 2, making the decision to adopt or reject AVs in Stage 3, to the implementation of AVs into practice in Stage 4. MAVA incorporates 28 acceptance factors that represent seven main acceptance classes. The acceptance factors are located at two levels, i.e., micro and meso. Factors at the micro-level constitute individual difference factors (i.e., sociodemographics, personality and travel behaviour). The meso-level captures the exposure of individuals to AVs, instrumental domain-specific, symbolic-affective and moral-normative factors of AVA. The literature review revealed that 6% of the studies investigated the exposure of individuals to AVs (i.e., knowledge and experience). 22% of the studies investigated domain-specific factors (i.e., performance and effort expectancy, safety, facilitating conditions, and service and vehicle characteristics), 4% symbolic-affective factors (i.e., hedonic motivation and social influence), and 12% moral-normative factors (i.e., perceived benefits and risks). Factors related to a person's socio-demographic profile, travel behaviour and personality were investigated by 28%, 15% and 14% of the studies, respectively. We recommend that future studies empirically verify MAVA using longitudinal or experimental studies.

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“…Despite the potential issues with interpreting RTs there are some fairly concrete findings emerging from comparing RTs across different scenarios and pooling the results (Zhang et al, 2018): Transition…”

Section: Comparing Existing Transition Evidence To the Perceptual-motmentioning

confidence: 99%

Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving

Mole

Lappi

Giles³

et al. 2019

Hum Factors

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Objective: To present a structured, narrative review highlighting research into human perceptual-motor coordination that can be applied to automated vehicle (AV)–human transitions. Background: Manual control of vehicles is made possible by the coordination of perceptual-motor behaviors (gaze and steering actions), where active feedback loops enable drivers to respond rapidly to ever-changing environments. AVs will change the nature of driving to periods of monitoring followed by the human driver taking over manual control. The impact of this change is currently poorly understood. Method: We outline an explanatory framework for understanding control transitions based on models of human steering control. This framework can be summarized as a perceptual-motor loop that requires (a) calibration and (b) gaze and steering coordination. A review of the current experimental literature on transitions is presented in the light of this framework. Results: The success of transitions are often measured using reaction times, however, the perceptual-motor mechanisms underpinning steering quality remain relatively unexplored. Conclusion: Modeling the coordination of gaze and steering and the calibration of perceptual-motor control will be crucial to ensure safe and successful transitions out of automated driving. Application: This conclusion poses a challenge for future research on AV-human transitions. Future studies need to provide an understanding of human behavior that will be sufficient to capture the essential characteristics of drivers reengaging control of their vehicle. The proposed framework can provide a guide for investigating specific components of human control of steering and potential routes to improving manual control recovery.

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Determinants of take-over time from automated driving: A meta-analysis of 129 studies

Cited by 248 publications

References 120 publications

Automation transparency: implications of uncertainty communication for human-automation interaction and interfaces

Automation transparency: implications of uncertainty communication for human-automation interaction and interfaces

A multi-level model on automated vehicle acceptance (MAVA): a review-based study

Getting Back Into the Loop: The Perceptual-Motor Determinants of Successful Transitions out of Automated Driving

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