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

2021 ◽  
Author(s):  
J. B. Manchon ◽  
Mercedes Bueno ◽  
Jordan Navarro
Keyword(s):  
Initial Level ◽  
Automated Driving ◽  
Human Machine Interaction ◽  
Driving Style ◽  
Automated Vehicle ◽  
Visual Behaviors ◽  
Trust In Automation ◽  
Machine Interaction ◽  
Over Time ◽  
Highly Automated Driving

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.

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

2021 ◽  
pp. 001872082110528
Author(s):  
J. B. Manchon ◽  
Mercedes Bueno ◽  
Jordan Navarro
Keyword(s):  
Road Safety ◽  
Initial Level ◽  
Automated Driving ◽  
Human Machine Interaction ◽  
Driving Style ◽  
Automated Vehicle ◽  
Visual Behaviors ◽  
Machine Interaction ◽  
Over Time

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.

scholarly journals How the Initial Level of Trust in Automated Driving Impacts Drivers’ Behaviour and Early Trust Construction

2021 ◽  
Author(s):  
J. B. Manchon ◽  
Mercedes Bueno ◽  
Jordan Navarro
Keyword(s):  
Driving Simulator ◽  
Initial Level ◽  
Environment Monitoring ◽  
Automated Driving ◽  
The Road ◽  
Trust In Automation ◽  
Gaze Behaviour ◽  
Simulator Study ◽  
The Impact ◽  
Highly Automated Driving

Trust in Automation is known to influence human-automation interaction and user behaviour. In the Automated Driving (AD) context, studies showed the impact of drivers’ Trust in Automated Driving (TiAD), and linked it with, e.g., difference in environment monitoring or driver’s behaviour. This study investigated the influence of driver’s initial level of TiAD on driver’s behaviour and early trust construction during Highly Automated Driving (HAD). Forty drivers participated in a driving simulator study. Based on a trust questionnaire, participants were divided in two groups according to their initial level of TiAD: high (Trustful) vs. low (Distrustful). Declared level of trust, gaze behaviour and Non-Driving-Related Activities (NDRA) engagement were compared between the two groups over time. Results showed that Trustful drivers engaged more in NDRA and spent less time monitoring the road compared to Distrustful drivers. However, an increase in trust was observed in both groups. These results suggest that initial level of TiAD impact drivers’ behaviour and further trust evolution.

scholarly journals Concept of an Ontology for Automated Vehicle Behavior in the Context of Human-Centered Research on Automated Driving Styles

Information ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 21
Author(s):  
Johannes Ossig ◽  
Stephanie Cramer ◽  
Klaus Bengler
Keyword(s):  
Driving Behavior ◽  
Automation System ◽  
Automated Driving ◽  
Driving Style ◽  
Driving Experience ◽  
Automated Vehicle ◽  
Future Design ◽  
Vehicle System ◽  
The Future ◽  
Conceptual Distinction

In the human-centered research on automated driving, it is common practice to describe the vehicle behavior by means of terms and definitions related to non-automated driving. However, some of these definitions are not suitable for this purpose. This paper presents an ontology for automated vehicle behavior which takes into account a large number of existing definitions and previous studies. This ontology is characterized by an applicability for various levels of automated driving and a clear conceptual distinction between characteristics of vehicle occupants, the automation system, and the conventional characteristics of a vehicle. In this context, the terms ‘driveability’, ‘driving behavior’, ‘driving experience’, and especially ‘driving style’, which are commonly associated with non-automated driving, play an important role. In order to clarify the relationships between these terms, the ontology is integrated into a driver-vehicle system. Finally, the ontology developed here is used to derive recommendations for the future design of automated driving styles and in general for further human-centered research on automated driving.

The More You Know: Trust Dynamics and Calibration in Highly Automated Driving and the Effects of Take-Overs, System Malfunction, and System Transparency

2019 ◽  
Vol 62 (5) ◽  
pp. 718-736 ◽  
Author(s):  
Johannes Kraus ◽  
David Scholz ◽  
Dina Stiegemeier ◽  
Martin Baumann
Keyword(s):  
Design System ◽  
Full Potential ◽  
Positive Outcomes ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Human Machine Interaction ◽  
Temporary Decrease ◽  
Free Interaction ◽  
Machine Interaction ◽  
Transparency Condition

Objective This paper presents a theoretical model and two simulator studies on the psychological processes during early trust calibration in automated vehicles. Background The positive outcomes of automation can only reach their full potential if a calibrated level of trust is achieved. In this process, information on system capabilities and limitations plays a crucial role. Method In two simulator experiments, trust was repeatedly measured during an automated drive. In Study 1, all participants in a two-group experiment experienced a system-initiated take-over, and the occurrence of a system malfunction was manipulated. In Study 2 in a 2 × 2 between-subject design, system transparency was manipulated as an additional factor. Results Trust was found to increase during the first interactions progressively. In Study 1, take-overs led to a temporary decrease in trust, as did malfunctions in both studies. Interestingly, trust was reestablished in the course of interaction for take-overs and malfunctions. In Study 2, the high transparency condition did not show a temporary decline in trust after a malfunction. Conclusion Trust is calibrated along provided information prior to and during the initial drive with an automated vehicle. The experience of take-overs and malfunctions leads to a temporary decline in trust that was recovered in the course of error-free interaction. The temporary decrease can be prevented by providing transparent information prior to system interaction. Application Transparency, also about potential limitations of the system, plays an important role in this process and should be considered in the design of tutorials and human-machine interaction (HMI) concepts of automated vehicles.

Trust in Automation as a Matter of Media Influence and Experi-ence of Automated Vehicles

2016 ◽  
Vol 60 (1) ◽  
pp. 2024-2028 ◽  
Author(s):  
Anna Feldhütter ◽  
Christian Gold ◽  
Adrian Hüger ◽  
Klaus Bengler
Keyword(s):  
Road Safety ◽  
Driving Simulator ◽  
Intention To Use ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Trust In Automation ◽  
Different Types ◽  
The Media ◽  
Source Of Information ◽  
Highly Automated Driving

Highly automated vehicles (HAV), which could help to enhance road safety and efficiency, are very likely to enter the market within the next decades. To have an impact, these systems need to be purchased, which is a matter of trust and acceptance. These factors are dependent on the level of information that one has about such systems. One important source of information is various media, such as newspapers, magazines and videos, in which highly automated driving (HAD) is currently a frequent topic of discussion. To evaluate the influence of media on the perception of HAD, 31 participants were presented with three different types of media addressing HAD in a neutral manner. Afterwards, the participants experienced HAD in the driving simulator. In between these steps, the participants completed questionnaires assessing comfort, trust in automation, increase in safety, intention to use and other factors in order to analyze the effect of the media and the driving simulation experience. Results indicate that the perception of some aspects of HAD were affected by the media presented, while experiencing HAD in the driving simulator generally did not have an effect on the attitude of the participants. Other aspects, such as trust, were not affected by either media or experience. In addition, gender-related differences in the perception of HAD were found.

scholarly journals Automata for the People: Machine Noise and Attention

2012 ◽  
Vol 18 (3) ◽  
Author(s):  
David Ellison
Keyword(s):  
White Noise ◽  
William James ◽  
Human Machine Interaction ◽  
Musical Performances ◽  
The People ◽  
Eighteenth And Nineteenth Centuries ◽  
Attention Demands ◽  
Machine Interaction ◽  
Physical Mechanics ◽  
Over Time

This article examines the production and reception of incidental machine noise, specifically the variably registered sounds emanating from automata in the eighteenth and nineteenth centuries. The argument proposed here is that the audience for automata performances demonstrated a capacity to screen out mechanical noise that may have otherwise interfered with the narrative theatricality of their display. In this regard the audience may be said to resemble auditors at musical performances who learned to suppress the various noises associated with the physical mechanics of performance, and the faculty of attention itself. For William James among others, attention demands selection among competing stimuli. As the incidental noise associated with automata disappears from sensibility over time, its capacity to signify in other contexts emerges. In the examples traced here, such noise is a means of distinguishing a specifically etherealised human-machine interaction. This is in sharp distinction from other more degrading forms of relationship such as the sound of bodies labouring at machines. In this regard, the barely detected sound of the automata in operation may be seen as a precursor to the white noise associated with modern, corporate productivity.

scholarly journals Prediction of take-over time in highly automated driving by two psychometric tests

DYNA ◽  
2015 ◽  
Vol 82 (193) ◽  
pp. 195-201 ◽  
Author(s):  
Moritz Körber ◽  
Thomas Weißgerber ◽  
Christoph Blaschke ◽  
Mehdi Farid ◽  
Luis Kalb
Keyword(s):  
Psychometric Tests ◽  
Automated Driving ◽  
Over Time ◽  
Highly Automated Driving

scholarly journals Human-Machine Interaction in Automated Vehicle: The ABV Project

2014 ◽  
Vol 47 (3) ◽  
pp. 6344-6349 ◽  
Author(s):  
Chouki Sentouh ◽  
Jean-Christophe Popieul ◽  
Serge Debernard ◽  
Serge Boverie
Keyword(s):  
Human Machine Interaction ◽  
Automated Vehicle ◽  
Machine Interaction

Assessing Drivers’ Trust of Automated Vehicle Driving Styles With a Two-Part Mixed Model of Intervention Tendency and Magnitude

2019 ◽  
pp. 001872081988036 ◽  
Author(s):  
John D. Lee ◽  
Shu-Yuan Liu ◽  
Joshua Domeyer ◽  
Azadeh DinparastDjadid
Keyword(s):  
Mixed Model ◽  
Driving Simulator ◽  
Statistical Technique ◽  
Brake Pedal ◽  
Automated Vehicles ◽  
Automated Driving ◽  
Accelerator Pedal ◽  
Driving Style ◽  
Automated Vehicle ◽  
Stop Sign

Objective: This study examines how driving styles of fully automated vehicles affect drivers’ trust using a statistical technique—the two-part mixed model—that considers the frequency and magnitude of drivers’ interventions. Background: Adoption of fully automated vehicles depends on how people accept and trust them, and the vehicle’s driving style might have an important influence. Method: A driving simulator experiment exposed participants to a fully automated vehicle with three driving styles (aggressive, moderate, and conservative) across four intersection types (with and without a stop sign and with and without crossing path traffic). Drivers indicated their dissatisfaction with the automation by depressing the brake or accelerator pedals. A two-part mixed model examined how automation style, intersection type, and the distance between the automation’s driving style and the person’s driving style affected the frequency and magnitude of their pedal depression. Results: The conservative automated driving style increased the frequency and magnitude of accelerator pedal inputs; conversely, the aggressive style increased the frequency and magnitude of brake pedal inputs. The two-part mixed model showed a similar pattern for the factors influencing driver response, but the distance between driving styles affected how often the brake pedal was pressed, but it had little effect on how much it was pressed. Conclusion: Eliciting brake and accelerator pedal responses provides a temporally precise indicator of drivers’ trust of automated driving styles, and the two-part model considers both the discrete and continuous characteristics of this indicator. Application: We offer a measure and method for assessing driving styles.

scholarly journals Drivers’ Evaluation of Different Automated Driving Styles: Is It both Comfortable and Natural?

2021 ◽  
Author(s):  
Chen Peng ◽  
Natasha Merat ◽  
Richard Romano ◽  
Foroogh Hajiseyedjavadi ◽  
Evangelos Paschalidis ◽  
...  
Keyword(s):  
Risk Taking ◽  
Sensation Seeking ◽  
Driving Simulator ◽  
Subjective Evaluation ◽  
High Fidelity ◽  
Aggressive Driving ◽  
Automated Driving ◽  
Driving Style ◽  
Vehicle Controllers ◽  
Highly Automated Driving

Objective: This study investigated users’ subjective evaluation of three highly automated driving styles, in terms of comfort and naturalness, when negotiating a UK road in a high-fidelity, motion-based, driving simulator. Background: Comfort and naturalness are thought to play an important role in contributing to users’ acceptance and trust of automated vehicles (AVs), although not much is understood about the types of driving style which are considered comfortable or natural. Method: A driving simulator study, simulating roads with different road geometries and speed limits, was conducted. Twenty-four participants experienced three highly automated driving styles, two of which were recordings from human drivers, and the other was based on a machine learning (ML) algorithm, termed Defensive, Aggressive, and Turner respectively. Participants evaluated comfort or naturalness of each driving style, for each road segment, and completed a Sensation Seeking (SS) questionnaire, which assessed their risk-taking propensity. Results: Participants regarded human-like driving styles as more comfortable and natural, compared with the less human-like, ML-based, driving controller. However, between the two human-like controllers, only the Defensive style was considered comfortable, especially for the more challenging road environments. Differences in preference for controller by driver trait were also observed, with the Aggressive driving style evaluated as more natural by the high sensation seekers. Conclusion: Participants were able to distinguish between human- and machine-like AV controllers. A range of psychological concepts must be considered for the subjective evaluation of controllers. Application: Knowing how different driver groups evaluate automated vehicle controllers is important to design more acceptable systems in the future.

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