Why did my car just do that? Explaining semi-autonomous driving actions to improve driver understanding, trust, and performance

Koo, Jeamin; Kwac, Jungsuk; Ju, Wendy; Steinert, Martin; Leifer, Larry; Nass, Clifford

doi:10.1007/s12008-014-0227-2

Cited by 374 publications

(218 citation statements)

References 10 publications

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“…Most work has focused on the design of the alert, in terms of the timing and modalities so that it conveys the required urgency [20,32,40,44,54]. For example, Gold et al [20] showed that alerts happening 7 seconds before the incident resulted in more successful take overs compared to alerts that were presented 5 seconds before.…”

Section: (In-car) Alertsmentioning

confidence: 99%

Priming Drivers before Handover in Semi-Autonomous Cars

Heiden

Iqbal

Janssen

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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Semi-autonomous vehicles occasionally require control to be handed over to the driver in situations where the vehicle is unable to operate safely. Currently, such handover requests require the driver to take control almost instantaneously. We investigate how auditory pre-alerts that occur well before the handover request impact the success of the handover in a dual task scenario. In a study with a driving simulator, drivers perform tasks on their phone while the car is in an autonomous mode. They receive a repeated burst audio prealert or an increasing pulse audio pre-alert preceding the standard warning for immediate handover. Results show that pre-alerts caused people to look more at the road before the handover occurred, and to disengage from the secondary task earlier, compared to when there was no pre-alert. This resulted in safer handover situations. Increasing pulse prealerts show particular promise due to their communication of urgency. Our detailed analysis informs the design and evaluation of alerts in safety-critical systems with automation.

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Section: (In-car) Alertsmentioning

confidence: 99%

Priming Drivers before Handover in Semi-Autonomous Cars

Heiden

Iqbal

Janssen

2017

Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems

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“…Koo et al [8] used speech warnings when the car took over from the driver due to an unexpected event. They found that messages describing why an event happened (e.g.…”

Section: Control Handovers In Autonomous Carsmentioning

confidence: 99%

“…This was reflected in the text of the messages, concluding with whether the messages were an enforced handover or handover request. In line with [8], the messages explained why a handover was necessary, rather than how the handover would happen. The messages were recorded by a female voice actor using a Rode NT2-A 1 condenser microphone.…”

Section: Warning Designmentioning

confidence: 99%

“…It is essential to warn the driver effectively in such a handover situation and design warnings that will enable safe transitions. There have been some studies on handover warnings using a limited number of modalities [13], looking into message content [8] and investigating transition times [5]. However, there has been no study on designing effective multimodal warnings for diverse handover situations when the driver of an autonomous car is busy with a demanding side task.…”

Section: Introductionmentioning

confidence: 99%

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Language-based multimodal displays for the handover of control in autonomous cars

Politis

Brewster

Pollick

2015

Proceedings of the 7th International Conference on Automotive User Interfaces and Interactive Vehicular Applications

143

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This paper presents a first evaluation of multimodal language-based warnings for handovers of control in autonomous cars. A set of possible handover situations varying in urgency is described. A set of multimodal, language-based warnings for these situations is then introduced. All combinations of audio, tactile and visual warnings for handovers were evaluated in terms of perceived urgency, annoyance and alerting effectiveness. Results showed clear recognition of the warning urgency in this new context, as well as low perceived annoyance overall, and higher perceived effectiveness for critical warnings. The time of transition from self-driving to manual mode in the presence of the warnings was then evaluated. Results showed quicker transitions for highly urgent warnings and poor driving performance for unimodal visual warnings. These results provide a novel set of guidelines for an effective transition of control between car and driver in an autonomous vehicle.

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“…These applications are at the doorstep to industrial and real-world application, in production (autonomously moving robots with human interaction), traffic (autonomous cars and trucks) as well as health care (surgery as well as care robotics). These levels or hurdles can be seen throughout a sequential level of personal intrusion (x-axis), arriving at a completely new situation after the three hurdle areas: The situation of trust towards an AI application, where humans are inclined to actively and trustfully cooperate with automated applications (compare e.g., [76][77][78][79][80]). …”

Section: Description Status Quomentioning

confidence: 99%

Innovation Potentials and Pathways Merging AI, CPS, and IoT

Klumpp

2018

ASI

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Recent advances in the areas of Artificial Intelligence (AI) in the informatics field, Cyber-Physical Systems (CPS) in the production field, and Internet of Things (IoT) in the logistics and transportation field have induced a tremendous growth and innovation potential for global value chain setups. The question is not if further innovation and automation will happen but when-sooner than later-and how. Independent of physical production innovations (additive manufacturing) the information integration and decision autonomy tendencies themselves will drive new supply chain and customer interaction designs and business models. This article presents a technology forecast model based on extensive descriptions of developments by field as well as interaction traits. Results suggest that the crucial element in AI and technology application in logistics will be the human factor and human-artificial cooperation capacities and attitudes.

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Why did my car just do that? Explaining semi-autonomous driving actions to improve driver understanding, trust, and performance

Cited by 374 publications

References 10 publications

Priming Drivers before Handover in Semi-Autonomous Cars

Priming Drivers before Handover in Semi-Autonomous Cars

Language-based multimodal displays for the handover of control in autonomous cars

Innovation Potentials and Pathways Merging AI, CPS, and IoT

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