Emergency, Automation Off: Unstructured Transition Timing for Distracted Drivers of Automated Vehicles

Mok, Brian; Johns, Mishel; Lee, Key Jung; Miller, David; Sirkin, David; Ive, Page; Ju, Wendy

doi:10.1109/itsc.2015.396

Cited by 111 publications

(58 citation statements)

References 12 publications

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“…This model states that successful tactical decision can be invoked by giving operators more time or by enhancing the predictability of the situation. The authors used this to compare driver-paced transitions [53] (which allow for extra planning time) with transitions under time pressure (cf., [23] and [55]- [58]). In accordance with the predictions of the COCOM, we expected that improvements would occur in driver decision making by increasing the predictability of the situation through HMIs that involve different stages of automation.…”

Section: E Aim Of This Experimentsmentioning

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.

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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.

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Section: E Aim Of This Experimentsmentioning

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.

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show abstract

“…Sketch of Planned Maneuver SILAB Birds-Eye-View S1: Navigating intersection with traffic light and turning left S2: Navigating intersection with traffic light and driving straight on By way of an NDRA, half of the participants were offered the option of watching a movie on a 9.6 inch tablet mounted in front of the central information display, as this is a frequently requested and performed NDRA during automated driving [29,30]. This cognitive, visual NDRA has already been used in previous studies [43][44][45]. This NDRA has the advantages of being easy to interrupt and being able to be carried out successfully with little practice.…”

Section: Descriptionmentioning

confidence: 99%

Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors

et al. 2020

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During highly automated driving, the passenger is allowed to conduct non-driving related activities (NDRA) and no longer has to act as a fallback at the functional limits of the driving automation system. Previous research has shown that at lower levels of automation, passengers still wish to be informed about automated vehicle behavior to a certain extent. Due to the aim of the introduction of urban automated driving, which is characterized by high complexity, we investigated the information needs and visual attention of the passenger during urban, highly automated driving. Additionally, there was an investigation into the influence of the experience of automated driving and of NDRAs on these results. Forty participants took part in a driving simulator study. As well as the information presented on the human–machine interface (system status, navigation information, speed and speed limit), participants requested information about maneuvers, reasons for maneuvers, environmental settings and additional navigation data. Visual attention was significantly affected by the NDRA, while the experience of automated driving had no effect. Experience and NDRA showed no significant effect on the need for information. Differences in information needs seem to be due to the requirements of the individual passenger, rather than the investigated factors.

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“…A take-over request can be provided through pre-recorded voice (Gold et al, 2015;Mok et al, 2015;Politis et al, 2015), which may be an effective approach because humans are able to perceive sounds irrespective of head or eye orientation (Bazilinskyy and De Winter 2015). In aviation, a similar approach is used: traffic alert and collision avoidance systems (TCAS), which are mandatory in today's aircraft, apply voice commands (Kuchar and Yang, 2000).…”

Section: Take-over Requestsmentioning

confidence: 99%

“…Take-over situations may be of different urgency. Several studies have measured driver behavior in highly urgent situations, such as Mok et al (2015), who found that 50% of the drivers veered off the road when a critical lane-closure event followed only 2 s after a take-over request ("Emergency, Automation off"). Other studies have been concerned with larger lead times of 5 or 7 s (Gold et al, 2013; see Eriksson and Stanton, 2017; for an overview) or with discretionary transitions having a low urgency (Damb€ ock et al, 2013;Merat and Jamson, 2009;Nilsson et al, 2013).…”

Section: Take-over Requestsmentioning

confidence: 99%

Analyzing crowdsourced ratings of speech-based take-over requests for automated driving

Bazilinskyy

Winter

2017

Applied Ergonomics

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Take-over requests in automated driving should fit the urgency of the traffic situation. The robustness of various published research findings on the valuations of speech-based warning messages is unclear. This research aimed to establish how people value speech-based take-over requests as a function of speech rate, background noise, spoken phrase, and speaker's gender and emotional tone. By means of crowdsourcing, 2669 participants from 95 countries listened to a random 10 out of 140 take-over requests, and rated each take-over request on urgency, commandingness, pleasantness, and ease of understanding. Our results replicate several published findings, in particular that an increase in speech rate results in a monotonic increase of perceived urgency. The female voice was easier to understand than a male voice when there was a high level of background noise, a finding that contradicts the literature. Moreover, a take-over request spoken with Indian accent was found to be easier to understand by participants from India than by participants from other countries. Our results replicate effects in the literature regarding speech-based warnings, and shed new light on effects of background noise, gender, and nationality. The results may have implications for the selection of appropriate take-over requests in automated driving. Additionally, our study demonstrates the promise of crowdsourcing for testing human factors and ergonomics theories with large sample sizes.

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Emergency, Automation Off: Unstructured Transition Timing for Distracted Drivers of Automated Vehicles

Cited by 111 publications

References 12 publications

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

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

Information Needs and Visual Attention during Urban, Highly Automated Driving—An Investigation of Potential Influencing Factors

Analyzing crowdsourced ratings of speech-based take-over requests for automated driving

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