Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving

Helldin, Tove; Falkman, Göran; Riveiro, Maria; Davidsson, Staffan

doi:10.1145/2516540.2516554

Cited by 222 publications

(147 citation statements)

References 17 publications

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“…the driver takes risks he would not take without an automation. Consequences of overreliance are longer reaction times (Beller, Heesen, & Vollrath, 2013;Helldin, Falkman, Riveiro, & Davidsson, 2013) or lower reaction quality (McGuirl & Sarter, 2006) in critical events. Therefore, take-over time and similar dependent variables could be confounded with the trust in automation and statistically controlling this attitude could be a promising way to clarify influence factors in the take-over process in future studies.…”

Section: Discussionmentioning

confidence: 99%

The influence of age on the take-over of vehicle control in highly automated driving

Körber

Gold

Lechner

et al. 2016

Transportation Research Part F: Traffic Psychology and Behaviou

198

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The growing proportion of older drivers in the population plays an increasingly relevant role in road traffic that is currently awaiting the introduction of automated vehicles. In this study, it was investigated how older drivers (≥ 60 years) compared to younger drivers (≤ 28 years) perform in a critical traffic event when driving highly automated. Conditions of the take-over situation were manipulated by adding a verbal non-driving task (20 questions task) and by variation of traffic density. Two age groups consisting of 36 younger and 36 older drivers drove either with or without a non-driving task on a six-lane highway. They encountered three situations with either no, medium or high traffic density where they had to regain vehicle control and evade an obstacle on the road. Older drivers reacted as fast as younger drivers, however, they differed in their modus operandi as they braked more often and more strongly and maintained a higher time-to-collision (TTC). Deterioration of take-over time and quality caused by increased traffic density and engagement in a non-driving task was on the same level for both age groups. Independent of the traffic density, there was a learning effect for both younger and older drivers in a way that the take-over time decreased, minimum TTC increased and maximum lateral acceleration decreased between the first and the last situation of the experiment. Results highlight that older drivers are able to solve critical traffic events as well as younger drivers, yet their modus operandi differs. Nevertheless, both age groups adapt to the experience of take-over situations in the same way.

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Section: Discussionmentioning

confidence: 99%

The influence of age on the take-over of vehicle control in highly automated driving

Körber

Gold

Lechner

et al. 2016

Transportation Research Part F: Traffic Psychology and Behaviou

198

View full text Add to dashboard Cite

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“…the driver takes risks he would not have taken without an automated system. Operators then tend to be vulnerable to monitoring failures (Bagheri & Jamieson, 2004;Bailey & Scerbo, 2007) and tend to exhibit longer reaction times (Beller, Heesen, & Vollrath, 2013;Helldin, Falkman, Riveiro, & Davidsson, 2013) or poorer reaction quality in critical events (McGuirl & Sarter, 2006;de Waard, van der Hulst, Hoedemaeker, & Brookhuis, 1999). Hence, not only a minimum level but an appropriate level of trust is crucial: The operator has to know the capabilities of an automated system and should monitor it adequately when it is close to the limits of its capability (Carlson, Drury, Desai, Kwak, & Yanco, 2014).…”

Section: The Role Of Trust In Automated Drivingmentioning

confidence: 99%

“…Beller et al (2013) showed that the presentation of information on an automated system's uncertainty improves situation awareness, improves a driver's mental model of the automated driving system, increases trust, and leads to an increased time to collision in the event of an automation failure. Drivers in the study of Helldin et al (2013), who were informed of the automated system's uncertainty, were better prepared in take-over situations while, on average, spending more time doing other activities. This study aims to investigate the relationship between trust, reliance behavior and take-over performance in conditional automated driving in greater detail.…”

Section: The Role Of Trust In Automated Drivingmentioning

confidence: 99%

“…Beyond this, a calibration of trust by visualizing the automated driving system's confidence could continue to ensure appropriate reliance in long-term use. This information may be provided by a color-coded visual scale, anthropomorphic symbols or emoticons in either the instrument cluster or a head-up display (Beller et al, 2013;Helldin et al, 2013;Seppelt & Lee, 2007). A human-machine interface (HMI) could also by request give a verbal or visual explanation of a system limit directly after its occurrence to promote the understanding of the automated driving system's intentions, limits, and actions.…”

Section: Practical Application and Future Workmentioning

confidence: 99%

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Introduction matters: Manipulating trust in automation and reliance in automated driving

2018

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Trust in automation is a key determinant for the adoption of automated systems and their appropriate use. Therefore, it constitutes an essential research area for the introduction of automated vehicles to road traffic. In this study, we investigated the influence of trust promoting (Trust promoted group) and trust lowering (Trust lowered group) introductory information on reported trust, reliance behavior and takeover performance. Forty participants encountered three situations in a 17-minute highway drive in a conditionally automated vehicle (SAE Level 3). Situation 1 and Situation 3 were non-critical situations where a take-over was optional. Situation 2 represented a critical situation where a take-over was necessary to avoid a collision. A non-driving-related task (NDRT) was presented between the situations to record the allocation of visual attention. Participants reporting a higher trust level spent less time looking at the road or instrument cluster and more time looking at the NDRT. The manipulation of introductory information resulted in medium differences in reported trust and influenced participants' reliance behavior. Participants of the Trust promoted group looked less at the road or instrument cluster and more at the NDRT. The odds of participants of the Trust promoted group to overrule the automated driving system in the non-critical situations were 3.65 times (Situation 1) to 5 times (Situation 3) higher. In Situation 2, the Trust promoted group's mean take-over time was extended by 1154 ms and the mean minimum time-to-collision was 933 ms shorter. Six participants from the Trust promoted group compared to no participant of the Trust lowered group collided with the obstacle. The results demonstrate that the individual trust level influences how much drivers monitor the environment while performing an NDRT. Introductory information influences this trust level, reliance on an automated driving system, and if a critical take-over situation can be successfully solved.

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“…However, to increase the safety and comfort of an automated ride, it may be necessary to present status information such as upcoming automated maneuvers (e.g., lane changes 2 Journal of Advanced Transportation or speed adaptations) or the confidence level of the automation [11][12][13][14]. As the driver's primary task will likely consist of engaging in NDRTs, drivers might prefer to be informed in a nondistracting way because interruptions of ongoing NDRTs may be perceived as a nuisance (e.g., when drivers are required to retrieve information from attention demanding displays).…”

Section: Motivation Partially Automated Vehicles (Sae Level 2;mentioning

confidence: 99%

Improving Usefulness of Automated Driving by Lowering Primary Task Interference through HMI Design

Naujoks

Forster

Wiedemann

et al. 2017

Journal of Advanced Transportation

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During conditionally automated driving (CAD), driving time can be used for non-driving-related tasks (NDRTs). To increase safety and comfort of an automated ride, upcoming automated manoeuvres such as lane changes or speed adaptations may be communicated to the driver. However, as the driver's primary task consists of performing NDRTs, they might prefer to be informed in a nondistracting way. In this paper, the potential of using speech output to improve human-automation interaction is explored. A sample of 17 participants completed different situations which involved communication between the automation and the driver in a motion-based driving simulator. The Human-Machine Interface (HMI) of the automated driving system consisted of a visualauditory HMI with either generic auditory feedback (i.e., standard information tones) or additional speech output. The drivers were asked to perform a common NDRT during the drive. Compared to generic auditory output, communicating upcoming automated manoeuvres additionally by speech led to a decrease in self-reported visual workload and decreased monitoring of the visual HMI. However, interruptions of the NDRT were not affected by additional speech output. Participants clearly favoured the HMI with additional speech-based output, demonstrating the potential of speech to enhance usefulness and acceptance of automated vehicles.

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Presenting system uncertainty in automotive UIs for supporting trust calibration in autonomous driving

Cited by 222 publications

References 17 publications

The influence of age on the take-over of vehicle control in highly automated driving

The influence of age on the take-over of vehicle control in highly automated driving

Introduction matters: Manipulating trust in automation and reliance in automated driving

Improving Usefulness of Automated Driving by Lowering Primary Task Interference through HMI Design

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