How the initial level of trust in automated driving impacts drivers’ behaviour and early trust construction

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

doi:10.1016/j.trf.2022.02.006

Cited by 17 publications

(13 citation statements)

References 55 publications

(47 reference statements)

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“…The second group, Distrustful drivers, had low initial levels of trust, defined as a trust score under 35%. These thresholds were selected based on a prior work (Manchon et al, 2022), where two homogenous groups of Trustful and Distrustful people were obtained. The two groups had mean trust scores such as M trustful = 67.4 and M distrustful = 33.3.…”

Section: Methodsmentioning

confidence: 99%

“…This initial level of trust is influenced by the driver’s mental model (Beggiato et al, 2015), expectations (Hergeth et al, 2017; Körber, Baseler, et al, 2018), prior experiences with other automated systems (Schwarz et al, 2019; Walker et al, 2019), and perceived understanding of automation (Khastgir et al, 2018). Based on these elements, a previous study carried out as part of a thesis work (Manchon et al, 2022) has classified drivers as “Trustful” or “Distrustful” according to their initial level of trust. Such methodology was used in the present paper.…”

Section: Introductionmentioning

confidence: 99%

“…A previous thesis experiment showed that drivers’ initial level of trust influenced their early trust calibration, behavior, and road monitoring strategies (Manchon et al, 2022). Moreover, another study showed that people with a higher level of trust in automation seemed more likely to recalibrate their trust after they experienced a real automated driving system (Pop et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

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

2021

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Objective Automated driving is becoming a reality, and such technology raises new concerns about human–machine interaction on road. This paper aims to investigate factors influencing trust calibration and evolution over time. Background Numerous studies showed trust was a determinant in automation use and misuse, particularly in the automated driving context. Method 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 two sessions of simulated automated driving. 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 Results showed an increase of trust 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. Conclusion Trust in automated driving increases rapidly when drivers’ experience such a system. Initial level of trust seems to be crucial in further trust calibration and modulate the effect of automation performance. Long-term trust evolutions suggest that experience modify drivers’ mental model about automated driving systems. Application In the automated driving context, trust calibration is a decisive question to guide such systems’ proper utilization, and road safety.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2021

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“…Another method of increasing trust lies in the time spent using the system. With both initially trusting and distrusting drivers, 10 min in the simulator experiencing the sounds, environment, and system of highly automated driving (HAD) before the study significantly increased the level of trust in HAD ( Manchon et al, 2022 ). After this time, drivers were shown to monitor the road with fewer glances and with more time spent engaging in Non-Driving Related Activities (NDRA).…”

Section: Related Workmentioning

confidence: 99%

Happiness and high reliability develop affective trust in in-vehicle agents

Zieger

Dong²,

Taylor³

et al. 2023

Front. Psychol.

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The advancement of Conditionally Automated Vehicles (CAVs) requires research into critical factors to achieve an optimal interaction between drivers and vehicles. The present study investigated the impact of driver emotions and in-vehicle agent (IVA) reliability on drivers’ perceptions, trust, perceived workload, situation awareness (SA), and driving performance toward a Level 3 automated vehicle system. Two humanoid robots acted as the in-vehicle intelligent agents to guide and communicate with the drivers during the experiment. Forty-eight college students participated in the driving simulator study. The participants each experienced a 12-min writing task to induce their designated emotion (happy, angry, or neutral) prior to the driving task. Their affective states were measured before the induction, after the induction, and after the experiment by completing an emotion assessment questionnaire. During the driving scenarios, IVAs informed the participants about five upcoming driving events and three of them asked for the participants to take over control. Participants’ SA and takeover driving performance were measured during driving; in addition, participants reported their subjective judgment ratings, trust, and perceived workload (NASA-TLX) toward the Level 3 automated vehicle system after each driving scenario. The results suggested that there was an interaction between emotions and agent reliability contributing to the part of affective trust and the jerk rate in takeover performance. Participants in the happy and high reliability conditions were shown to have a higher affective trust and a lower jerk rate than other emotions in the low reliability condition; however, no significant difference was found in the cognitive trust and other driving performance measures. We suggested that affective trust can be achieved only when both conditions met, including drivers’ happy emotion and high reliability. Happy participants also perceived more physical demand than angry and neutral participants. Our results indicated that trust depends on driver emotional states interacting with reliability of the system, which suggested future research and design should consider the impact of driver emotions and system reliability on automated vehicles.

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“…Trust in automation can affect the choice of automated or manual control by operators and influences system use, perhaps inducing misuse or abuse ( 15 , 16 ). Specifically, trust in AD can influence its adoption ( 17 ), and also the way drivers interact with the system—for example, a reduced number of glances toward the road ( 18 ). Acceptability is a concept related to trust, because it also influences adoption or the intention to use the system ( 19 , 20 ).…”

mentioning

confidence: 99%

Allocation of Drivers’ Visual Attention During Preliminary Uses of Automated Driving: A Wizard-of-Oz Study

Barré

Sahaï

Bueno

2022

Transportation Research Record: Journal of the Transportation R

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In this experiment, we analyzed drivers’ visual attention during their first experiences with automated driving (AD). At the beginning of the study, participants ( n = 45) received one of three AD training modules: paper, video, or practice. They then drove two sessions on a public road with a Wizard-Of-Oz vehicle, each ending with a request to intervene (RTI). The first session (forward path) included 10 min of AD with a mandatory non-driving-related task (NDRT). The second session (return path) included 10 min of AD with a nonmandatory NDRT. Control checks (number and duration of gazes) toward the road and rear-view mirrors and three self-assessment questionnaires (trust, acceptability, and technophilia) were measured during both sessions. The results indicated a decrease in road and rear-view mirror gazes over time, mainly for the practice group. Drivers with a low level of trust glanced at the road and mirrors more often than participants with a high level of trust. In addition, participants with a high level of technophilia spent less time controlling the road traffic but only during the forward path. Visual attention on the road decreased rapidly during the first minutes of AD, mainly among drivers who followed the practice training and those who had a high level of trust and were tech-savvy. We observed that participants looked at the rear-view mirrors after 10 and 14 s on average after the RTI. These results raise questions about driver situation awareness in critical situations, including the capacity to intervene in an efficient and safe way during takeover requests.

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How the initial level of trust in automated driving impacts drivers’ behaviour and early trust construction

Cited by 17 publications

References 55 publications

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

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

Happiness and high reliability develop affective trust in in-vehicle agents

Allocation of Drivers’ Visual Attention During Preliminary Uses of Automated Driving: A Wizard-of-Oz Study

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