The Importance of Interruption Management for Usefulness and Acceptance of Automated Driving

Naujoks, Frederik; Wiedemann, Katharina; Schömig, Nadja

doi:10.1145/3122986.3123000

Cited by 30 publications

(22 citation statements)

References 92 publications

(115 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Information presentation closer to, or merged with the driving scene showed positive effects [18,20]; and using an IVIS for this is preferred as compared to handheld devices [41]. Closest to our study, Wulf et al [40] permanently displayed the driving scene in one corner of the IVIS and could improve situational awareness, but not driving performance; Naujoks et al [27] recommended, but did not study on interruption strategies to keep the driver in the loop.…”

Section: Improving Take-over Qualitysupporting

confidence: 49%

Improving take-over quality in automated driving by interrupting non-driving tasks

Köhn

Gottlieb

Schermann

et al. 2019

Proceedings of the 24th International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

With automated driving advancing, first production models started to incorporate the technology. However, until full autonomy is achieved, drivers always need to stay available to take over control from the car. This requirement has proven challenging: increased levels of automation reduce drivers' situational awareness and driving performance can suffer, especially in the critical moments after takeover. While manualdriving research introduced strategies to direct drivers' attention back to the road, notably interruptions of the non-driving task, the efficacy of these interventions on automated driving remain unclear. To investigate this, 53 participants drove in an automated simulator while performing tasks on an IVIS. With task interruptions, they reported increased situational awareness and showed improved reaction times during takeover , particularly for low-effort tasks (watching movies). Different to manual driving, halting tasks did not suffice; instead, we displayed the driving scene. Results question effects of situational awareness on takeover and offer solutions for manufacturers. CCS CONCEPTS • Human-centered computing~Empirical studies in ubiquitous and mobile computing • Human-centered computing~Empirical studies in interaction design • Humancentered computing~Information visualization KEYWORDS Semi-automated driving; take over request; situational awareness; out of loop; IVIS; interruptions.

show abstract

Section: Improving Take-over Qualitysupporting

confidence: 49%

Improving take-over quality in automated driving by interrupting non-driving tasks

Köhn

Gottlieb

Schermann

et al. 2019

Proceedings of the 24th International Conference on Intelligent User Interfaces

View full text Add to dashboard Cite

show abstract

“…The participants played "Angry Birds" as a nondriving task during the intervals of automated driving. Angry Birds was deemed suitable because it is an interruptible [61] task that does not penalize the player for switching to another task. The driver played the game on a Lenovo A7-50 7-inch tablet that was mounted in the centre console, in front of the radio.…”

Section: B Apparatusmentioning

confidence: 99%

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Eriksson

Petermeijer

Zimmermann

et al. 2019

IEEE Trans. Human-Mach. Syst.

View full text Add to dashboard Cite

This paper assessed four types of human-machine interfaces (HMIs), classified according to the stages of automation proposed by Parasuraman et al. ["A model for types and levels of human interaction with automation," IEEE Trans. Syst. Man, Cybern. A, Syst. Humans, vol. 30, no. 3, pp. 286-297, May 2000]. We hypothesized that drivers would implement decisions (lane changing or braking) faster and more correctly when receiving support at a higher automation stage during transitions from conditionally automated driving to manual driving. In total, 25 participants with a mean age of 25.7 years (range 19-36 years) drove four trials in a driving simulator, experiencing four HMIs having the following different stages of automation: baseline (information acquisitionlow), sphere (information acquisition-high), carpet (information analysis), and arrow (decision selection), presented as visual overlays on the surroundings. The HMIs provided information during two scenarios, namely a lane change and a braking scenario. Results showed that the HMIs did not significantly affect the drivers' initial reaction to the take-over request. Improvements were found, however, in the decision-making process: When drivers experienced the carpet or arrow interface, an improvement in correct decisions (i.e., to brake or change lane) occurred. It is concluded that visual HMIs can assist drivers in making a correct braking or lane change maneuver in a take-over scenario. Future research could be directed toward misuse, disuse, errors of omission, and errors of commission.

show abstract

“…[3] Therefore, trust as the observable and rational choice behavior can be a product of believing that these benefits will outweigh the costs [5]. For example, Naujoks et.al [43] discussed the desire to engage the non-driving-related tasks during autonomous driving, which requires the workload of the driving task to be lighter and makes the driver select the autonomous mode. Our framework could be integrated with such factors by adjusting the effect of the term P man with a coefficient γ, which represents a degree of human intention to do the main task manually.…”

Section: Limitations and Future Researchmentioning

confidence: 99%

Adaptive trust calibration for human-AI collaboration

2020

View full text Add to dashboard Cite

Safety and efficiency of human-AI collaboration often depend on how humans could appropriately calibrate their trust towards the AI agents. Over-trusting the autonomous system sometimes causes serious safety issues. Although many studies focused on the importance of system transparency in keeping proper trust calibration, the research in detecting and mitigating improper trust calibration remains very limited. To fill these research gaps, we propose a method of adaptive trust calibration that consists of a framework for detecting the inappropriate calibration status by monitoring the user's reliance behavior and cognitive cues called "trust calibration cues" to prompt the user to reinitiate trust calibration. We evaluated our framework and four types of trust calibration cues in an online experiment using a drone simulator. A total of 116 participants performed pothole inspection tasks by using the drone's automatic inspection, the reliability of which could fluctuate depending upon the weather conditions. The participants needed to decide whether to rely on automatic inspection or to do the inspection manually. The results showed that adaptively presenting simple cues could significantly promote trust calibration during over-trust.

show abstract

The Importance of Interruption Management for Usefulness and Acceptance of Automated Driving

Cited by 30 publications

References 92 publications

Improving take-over quality in automated driving by interrupting non-driving tasks

Improving take-over quality in automated driving by interrupting non-driving tasks

Rolling Out the Red (and Green) Carpet: Supporting Driver Decision Making in Automation-to-Manual Transitions

Adaptive trust calibration for human-AI collaboration

Contact Info

Product

Resources

About