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 Repeated Usage of an L3 Motorway Chauffeur: Change of Evaluation and Usage

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 114 ◽  
Author(s):  
Barbara Metz ◽  
Johanna Wörle ◽  
Michael Hanig ◽  
Marcus Schmitt ◽  
Aaron Lutz
Keyword(s):  
Driving Simulator ◽  
Automated Driving ◽  
Driving Function ◽  
Behavioural Adaptation ◽  
The Road ◽  
Driving Time ◽  
Level 3 ◽  
Simulator Study ◽  
First Contact ◽  
Highly Automated Driving

Most studies on users’ perception of highly automated driving functions are focused on first contact/single usage. Nevertheless, it is expected that with repeated usage, acceptance and usage of automated driving functions might change this perception (behavioural adaptation). Changes can occur in drivers’ evaluation, in function usage and in drivers’ reactions to take-over situations. In a driving simulator study, N = 30 drivers used a level 3 (L3) automated driving function for motorways during six experimental sessions. They were free to activate/deactivate that system as they liked and to spend driving time on self-chosen side tasks. Results already show an increase of experienced trust and safety, together with an increase of time spent on side tasks between the first and fourth sessions. Furthermore, attention directed to the road decreases with growing experience with the system. The results are discussed with regard to the theory of behavioural adaptation. Results indicate that the adaptation of acceptance and usage of the highly automated driving function occurs rather quickly. At the same time, no behavioural adaptation for the reaction to take-over situations could be found.

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.

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.

Function-Specific Uncertainty Communication in Automated Driving

2019 ◽  
Vol 11 (2) ◽  
pp. 75-97
Author(s):  
Alexander Kunze ◽  
Stephen J. Summerskill ◽  
Russell Marshall ◽  
Ashleigh J. Filtness
Keyword(s):  
Augmented Reality ◽  
Situation Awareness ◽  
Driving Simulator ◽  
Automated Driving ◽  
Driving Experience ◽  
Visual Variables ◽  
Simulator Study ◽  
The Impact

Conveying the overall uncertainties of automated driving systems was shown to improve trust calibration and situation awareness, resulting in safer takeovers. However, the impact of presenting the uncertainties of multiple system functions has yet to be investigated. Further, existing research lacks recommendations for visualizing uncertainties in a driving context. The first study outlined in this publication investigated the implications of conveying function-specific uncertainties. The results of the driving simulator study indicate that the effects on takeover performance depends on driving experience, with less experienced drivers benefitting most. Interview responses revealed that workload increments are a major inhibitor of these benefits. Based on these findings, the second study explored the suitability of 11 visual variables for an augmented reality-based uncertainty display. The results show that particularly hue and animation-based variables are appropriate for conveying uncertainty changes. The findings inform the design of all displays that show content varying in urgency.

Function-Specific Uncertainty Communication in Automated Driving

2022 ◽  
pp. 1002-1026
Author(s):  
Alexander Kunze ◽  
Stephen J. Summerskill ◽  
Russell Marshall ◽  
Ashleigh J. Filtness
Keyword(s):  
Augmented Reality ◽  
Situation Awareness ◽  
Driving Simulator ◽  
Automated Driving ◽  
Driving Experience ◽  
Visual Variables ◽  
Simulator Study ◽  
The Impact

Conveying the overall uncertainties of automated driving systems was shown to improve trust calibration and situation awareness, resulting in safer takeovers. However, the impact of presenting the uncertainties of multiple system functions has yet to be investigated. Further, existing research lacks recommendations for visualizing uncertainties in a driving context. The first study outlined in this publication investigated the implications of conveying function-specific uncertainties. The results of the driving simulator study indicate that the effects on takeover performance depends on driving experience, with less experienced drivers benefitting most. Interview responses revealed that workload increments are a major inhibitor of these benefits. Based on these findings, the second study explored the suitability of 11 visual variables for an augmented reality-based uncertainty display. The results show that particularly hue and animation-based variables are appropriate for conveying uncertainty changes. The findings inform the design of all displays that show content varying in urgency.

scholarly journals The Impact of Situational Complexity and Familiarity on Takeover Quality in Uncritical Highly Automated Driving Scenarios

Information ◽  
2020 ◽  
Vol 11 (2) ◽  
pp. 115 ◽  
Author(s):  
Marlene Susanne Lisa Scharfe ◽  
Kathrin Zeeb ◽  
Nele Russwinkel
Keyword(s):  
Driving Simulator ◽  
Intelligent Vehicles ◽  
Automated Driving ◽  
Strong Focus ◽  
Cognitive Assistance ◽  
The Subject ◽  
Perceived Complexity ◽  
Study Participants ◽  
The Impact ◽  
Highly Automated Driving

In the development of highly automated driving systems (L3 and 4), much research has been done on the subject of driver takeover. Strong focus has been placed on the takeover quality. Previous research has shown that one of the main influencing factors is the complexity of a traffic situation that has not been sufficiently addressed so far, as different approaches towards complexity exist. This paper differentiates between the objective complexity and the subjectively perceived complexity. In addition, the familiarity with a takeover situation is examined. Gold et al. show that repetition of takeover scenarios strongly influences the take-over performance. Yet, both complexity and familiarity have not been considered at the same time. Therefore, the aim of the present study is to examine the impact of objective complexity and familiarity on the subjectively perceived complexity and the resulting takeover quality. In a driving simulator study, participants are requested to take over vehicle control in an uncritical situation. Familiarity and objective complexity are varied by the number of surrounding vehicles and scenario repetitions. Subjective complexity is measured using the NASA-TLX; the takeover quality is gathered using the take-over controllability rating (TOC-Rating). The statistical evaluation results show that the parameters significantly influence the takeover quality. This is an important finding for the design of cognitive assistance systems for future highly automated and intelligent vehicles.

scholarly journals Familiarity and Complexity during a Takeover in Highly Automated Driving

2021 ◽  
Author(s):  
Marlene Susanne Lisa Scharfe-Scherf ◽  
Nele Russwinkel
Keyword(s):  
Cognitive Modeling ◽  
Driving Simulator ◽  
Cognitive State ◽  
Automated Driving ◽  
Cognitive Assistance ◽  
Assistance Systems ◽  
The Impact ◽  
Subjective Complexity ◽  
Highly Automated Driving ◽  
Action Decision

AbstractThis paper shows, how objective complexity and familiarity impact the subjective complexity and the time to make an action decision during the takeover task in a highly automated driving scenario. In the next generation of highly automated driving the driver remains as fallback and has to take over the driving task whenever the system reaches a limit. It is thus highly important to develop an assistance system that supports the individual driver based on information about the drivers’ current cognitive state. The impact of familiarity and complexity (objective and subjective) on the time to make an action decision during a takeover is investigated. To produce replicable driving scenarios and manipulate the independent variables situation familiarity and objective complexity, a driving simulator is used. Results show that the familiarity with a traffic situation as well as the objective complexity of the environment significantly influence the subjective complexity and the time to make an action decision. Furthermore, it is shown that the subjective complexity is a mediator variable between objective complexity/familiarity and the time to make an action decision. Complexity and familiarity are thus important parameters that have to be considered in the development of highly automated driving systems. Based on the presented mediation effect, the opportunity of gathering the drivers’ subjective complexity and adapting cognitive assistance systems accordingly is opened up. The results of this study provide a solid basis that enables an individualization of the takeover by implementing useful cognitive modeling to individualize cognitive assistance systems for highly automated driving.

scholarly journals Context-Adaptive Availability Notifications for an SAE Level 3 Automation

2021 ◽  
Vol 5 (4) ◽  
pp. 16
Author(s):  
Simon Danner ◽  
Alexander Feierle ◽  
Carina Manger ◽  
Klaus Bengler
Keyword(s):  
Driving Simulator ◽  
Human Machine Interface ◽  
Gaze Behavior ◽  
Activation Time ◽  
Automated Driving ◽  
Driving Function ◽  
The Road ◽  
Level 3 ◽  
Machine Interface ◽  
Simulator Study

Context-adaptive functions are not new in the driving context, but even so, investigations into these functions concerning the automation human–machine interface (aHMI) have yet to be carried out. This study presents research into context-adaptive availability notifications for an SAE Level 3 automation in scenarios where participants were surprised by either availability or non-availability. For this purpose, participants (N = 30) took part in a driving simulator study, experiencing a baseline HMI concept as a comparison, and a context-adaptive HMI concept that provided context-adaptive availability notifications with the aim of improving acceptance and usability, while decreasing frustration (due to unexpected non-availability) and gaze deviation from the road when driving manually. Furthermore, it was hypothesized that participants, when experiencing the context-adaptive HMI, would activate the automated driving function more quickly when facing unexpected availability. None of the hypotheses could be statistically confirmed; indeed, where gaze behavior was concerned, the opposite effects were found, indicating increased distraction induced by the context-adaptive HMI. However, the trend in respect to the activation time was towards shorter times with the context-adaptive notifications. These results led to the conclusion that context-adaptive availability notifications might not always be beneficial for users, while more salient availability notifications in the case of an unexpected availability could be advantageous.

scholarly journals Dynamic scan paths investigations under manual and highly automated driving

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Jordan Navarro ◽  
Otto Lappi ◽  
François Osiurak ◽  
Emma Hernout ◽  
Catherine Gabaude ◽  
...  
Keyword(s):  
Relative Frequency ◽  
Human Locomotion ◽  
Visual Scanning ◽  
Automated Driving ◽  
The Gaze ◽  
Visual Inputs ◽  
Gaze Patterns ◽  
Gaze Behaviour ◽  
Scan Paths ◽  
Highly Automated Driving

AbstractActive visual scanning of the scene is a key task-element in all forms of human locomotion. In the field of driving, steering (lateral control) and speed adjustments (longitudinal control) models are largely based on drivers’ visual inputs. Despite knowledge gained on gaze behaviour behind the wheel, our understanding of the sequential aspects of the gaze strategies that actively sample that input remains restricted. Here, we apply scan path analysis to investigate sequences of visual scanning in manual and highly automated simulated driving. Five stereotypical visual sequences were identified under manual driving: forward polling (i.e. far road explorations), guidance, backwards polling (i.e. near road explorations), scenery and speed monitoring scan paths. Previously undocumented backwards polling scan paths were the most frequent. Under highly automated driving backwards polling scan paths relative frequency decreased, guidance scan paths relative frequency increased, and automation supervision specific scan paths appeared. The results shed new light on the gaze patterns engaged while driving. Methodological and empirical questions for future studies are discussed.

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