Priming Drivers before Handover in Semi-Autonomous Cars

Heiden, Remo M.A. van der; Iqbal, Shamsi T.; Janssen, Christian P.

doi:10.1145/3025453.3025507

Cited by 75 publications

(29 citation statements)

References 49 publications

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“…This suggests that the information allowed for trust calibration. Likewise, take-over requests and alerts have been found to lead to safer usage and greater trust [16,42]. While our information increased users' perceptions of system trustworthiness, a more appropriate goal for system designers and institutions is to give information that improves trust.…”

Section: Effect Of System Information On Ability and Integrity Beliefsmentioning

confidence: 86%

Initial Trustworthiness Perceptions of a Drone System based on Performance and Process Information

Jensen

Albayram

Khan

et al. 2018

Proceedings of the 6th International Conference on Human-Agent Interaction

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Prior work notes dispositional, learned, and situational aspects of trust in automation. However, no work has investigated the relative role of these factors in initial trust of an automated system. Moreover, trust in automation researchers often consider trust unidimensionally, whereas ability, integrity, and benevolence perceptions (i.e., trusting beliefs) may provide a more thorough understanding of trust dynamics. To investigate this, we recruited 163 participants on Amazon's Mechanical Turk (MTurk) and randomly assigned each to one of 4 videos describing a hypothetical drone system: one control, the others with additional system performance or process, or both types of information. Participants reported on trusting beliefs in the system, propensity to trust other people, risk-taking tendencies, and trust in the government law enforcement agency behind the system. We found that financial risk-taking tendencies influenced trusting beliefs. Also, those who received process information were likely to have higher integrity and ability beliefs than those not receiving process information, while those who received performance information were likely to have higher ability beliefs. Lastly, perceptions of structural assurance positively influenced all three trusting beliefs. Our findings suggest that a) users' risktaking tendencies influence trustworthiness perceptions of systems, b) different types of information about a system have varied effects on the trustworthiness dimensions, and c) institutions play an important role in users' calibration of trust. Insights gained from this study can help design training materials and interfaces that improve user trust calibration in automated systems.

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Section: Effect Of System Information On Ability and Integrity Beliefsmentioning

confidence: 86%

Initial Trustworthiness Perceptions of a Drone System based on Performance and Process Information

Jensen

Albayram

Khan

et al. 2018

Proceedings of the 6th International Conference on Human-Agent Interaction

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“…Such a measurement should quantify the distribution of errors P(ɛ TTM ), as reported in Refs. [25,27], enabling the impact evaluation in simulation to obtain relative frequency and confidence for various outcomes. This is described in detail next.…”

Section: Quantitative Hazard Modelingmentioning

confidence: 99%

Analysis of Hazards for Autonomous Driving

Schwalb

2021

Journal of Autonomous Vehicles and Systems

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Hazard analysis is the core of numerous approaches to safety engineering, including the functional safety standard ISO-26262 (FuSa) and Safety of the Intended Function (SOTIF) ISO/PAS 21448. We focus on addressing the immense challenge associated with the scope of training and testing for rare hazard for autonomous drivers, leading to the need to train and test on the equivalent of >108 naturalistic miles. We show how risk can be estimated and bounded using the probabilistic hazard analysis. We illustrate the definition of hazards using well-established tests for hazard identification. We introduce a dynamic hazard approach, whereby autonomous drivers continuously monitor for potential and developing hazard, and estimate their time to materialization (TTM). We describe systematic TTM modeling of the various hazard types, including environment-specific perception limitations. Finally, we show how to enable accelerated development and testing by training a neural network sampler to generate scenarios in which the frequency of rare hazards is increased by orders of magnitude.

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“…Même si cette différence n'est pas statistiquement significative, cela demanderait à être testé avec un plus grand nombre de participants pour vérifier les effets de cette pré-alerte visuelle sur le temps de réaction. Dans le cadre de la conduite semi-autonome, d'autres études ont déjà montré les bénéfices des pré-alertes auditives quant au temps de réaction suite à une reprise de contrôle [30].…”

Section: Retours Des Participantsunclassified

Secondary task and situation awareness, a mobile application for semi-autonomous vehicle

Capallera

Meteier

Salis

et al. 2019

Proceedings of the 31st Conference on l'Interaction Homme-Machine

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Autonomous vehicles are developing rapidly and will lead to a significant change in the driver's role: he/she will have to move from the role of actor to the role of supervisor. Indeed, the driver will soon be able to perform a secondary task but he/she must be able to take over control in the event of a critical situation that is not managed by the autonomous system. This implies that the role of new interfaces and interactions within the vehicle is important to take into account. This article describes the design of an application that provides the driver with information about the environment perceived by his/her vehicle in the form

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Priming Drivers before Handover in Semi-Autonomous Cars

Cited by 75 publications

References 49 publications

Initial Trustworthiness Perceptions of a Drone System based on Performance and Process Information

Initial Trustworthiness Perceptions of a Drone System based on Performance and Process Information

Analysis of Hazards for Autonomous Driving

Secondary task and situation awareness, a mobile application for semi-autonomous vehicle

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