From manual to automated driving: how does trust evolve?

Manchon, J. B.; Bueno, Mercedes; Navarro, Jordan

doi:10.1080/1463922x.2020.1830450

Cited by 34 publications

(21 citation statements)

References 75 publications

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“…The factors related to the dynamic learned trust then becomes more influent during the interaction, when the operator gains knowledge and experience with the automation. As automated driving technologies are progressively becoming available, it is of particular interest to investigate the specificities of drivers' Trust in Automated Driving (TiAD), in order to understand the cognitive processes underlying automated vehicles adoption (Manchon et al, 2020). TiAD has been studied during the last years (e.g., Beller et al, 2013;Kraus, Scholz, Messner, et al, 2020;Payre et al, 2016;Schwarz et al, 2019), but little is known about its early calibration (Hergeth et al, 2016;Körber, Baseler, et al, 2018) and its evolution over time (Hartwich, Witzlack, et al, 2018;Walker et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

“…It should be noted that this effect was observed during a 45-minute single session of video-simulation, with no possible interactions between drivers and AD system, and ultimately no Take-Over Requests (TOR). TiAD has been mostly evaluated through questionnaires (Manchon et al, 2020). However, several studies also revealed a link between drivers' trust in automation and (a) visual monitoring strategies (Hergeth et al, 2016;Navarro et al, 2019;Walker et al, 2019) and (b) driver's activities (Manchon et al, submitted) during automated driving.…”

Section: Introductionmentioning

confidence: 99%

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Calibration of Trust in Automated Driving: A Matter of Initial Level of Trust and Automation Driving Style?

Manchon¹,

Bueno²,

Navarro³

2021

Preprint

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Automated driving is becoming a reality, such technology raises new concerns about human-machine interaction on-road. Sixty-one drivers participated in an experiment aiming to better understand the influence of initial level of trust (Trustful vs Distrustful) on drivers’ behaviors and trust calibration during simulated Highly Automated Driving (HAD). The automated driving style was manipulated as positive (smooth) or negative (abrupt) to investigate human-machine early interactions. Trust was assessed over time through questionnaires. Drivers’ visual behaviors and take-over performances during an unplanned take-over request were also investigated. Results showed an increase of trust in automation over time, for both Trustful and Distrustful drivers regardless the automated driving style. Trust was also found to fluctuate over time depending on the specific events handled by the automated vehicle. Take-over performances were not influenced by the initial level of trust nor automated driving style.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Calibration of Trust in Automated Driving: A Matter of Initial Level of Trust and Automation Driving Style?

Manchon¹,

Bueno²,

Navarro³

2021

Preprint

Self Cite

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“…In addition, participants could have used peripheral vision to ensure that the automatic controller was adjusting the lateral position of the vehicle efficiently, a task drivers are known to perform using peripheral vision cues [44]. A poor level of trust in automation, a key concept when considering human-machine interactions [45][46][47], including those related to highly automated driving (see [48] for a review) might explain the preservation of eye-hand coordination under AS. Indeed, it could be hypothesized that participants keep on scanning the visual environment under AS in a similar way to what they do under MD, because they did not completely trust automatic steering.…”

Section: Plos Onementioning

confidence: 99%

On the nature of eye-hand coordination in natural steering behavior

2020

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Eyes and hand movements are known to be coordinated during a variety of tasks. While steering a vehicle, gaze was observed to be tightly linked with steering wheel angle changes over time, with the eyes leading the hands. In this experiment, participants were asked to drive a winding road composed of bends with systematically manipulated radii of curvature, under regular and automatic steering conditions. With automatic steering, the vehicle followed the road, but the steering wheel and participants hands did not move. Despite the absence of physical eye-hand coordination in that condition, the eye and [what the hands should have done] to produce the action on the steering wheel were found to be coordinated, as under regular steering. This result brings a convincing piece of evidence that eye movements do more than just guiding the hands. In addition, eye-hand coordination was also found to be intermittent, context and person-dependant.

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“…Because it brings a valuable synthesis of its topic, this model has been widely used during the past years and has proved itself to be a strong theoretical frame in the automated driving context (e.g., Ekman et al, 2018;Hergeth et al, 2016;Kraus, Scholz, Messner, et al, 2020;Manchon et al, 2022;Molnar et al, 2018;Walker et al, 2019). Hoff and Bashir's model describes and organizes the factors linked with trust and has been shown to fit for studying TiAD at several steps of the interaction between the driver and the HAD system (see Manchon et al, 2020 for a review). Nevertheless, this relation of trust has common points with the general TiA theory, but also some major particularities that justify the development of another, more specific concept: Trust in Automated Driving (TiAD).…”

Section: Introductionmentioning

confidence: 99%

Why do they trust it? Determinants of the initial Trust in Automated Driving

Manchon¹,

Beaufort²,

Bueno³

et al. 2022

Preprint

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Because driving automation is quickly increasing, humans’ relation to automated driving evolves accordingly. Trust in automated driving has been shown to strongly correlate with drivers’ acceptance of such systems. Nonetheless, there is currently a lack of specific questionnaires to assess drivers’ level of trust in Highly Automated Driving (HAD). Moreover, little is known about which factors determine trust before any interaction with HAD systems. Theoretical trust in automation models propose several factors to explain how trust builds before and during interactions with automation. Using 844 answers collected through an online survey, this paper aims to propose a new scale designed to evaluate initial trust in automated driving. Moreover, we also measure other factors linked to drivers’ initial level of trust in HAD by operationalizing the trust components proposed by Hoff and Bashir (2015)’s theoretical trust model. In order to better describe what determines trust in HAD, a linear model based on the collected data is proposed. The model not only weights the factors determining trust but is also able to predict the level of trust considering these factors.

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From manual to automated driving: how does trust evolve?

Cited by 34 publications

References 75 publications

Calibration of Trust in Automated Driving: A Matter of Initial Level of Trust and Automation Driving Style?

Calibration of Trust in Automated Driving: A Matter of Initial Level of Trust and Automation Driving Style?

On the nature of eye-hand coordination in natural steering behavior

Why do they trust it? Determinants of the initial Trust in Automated Driving

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