Mode Errors and Intentional Violations in Visual Monitoring of Level 2 Driving Automation

Boos, Annika; Feldhütter, Anna; Schwiebacher, Johannes; Bengler, Klaus

doi:10.1109/itsc45102.2020.9294690

Cited by 12 publications

(17 citation statements)

References 19 publications

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“…Drivers were found to disengage from their monitoring task easily and rather engage in NDRAs [3][4][5][6][7]. These findings are consistent with recent research on mode awareness (general knowledge of levels and currently engaged level [8]) in vehicles with both L2 and L3 automation: In studies by [9,10], participants increased their NDRA engagement with exposure duration. In turn, visual attention deteriorated during L2 driving with exposure duration and frequency.…”

Section: Introductionsupporting

confidence: 84%

How to Keep Drivers Attentive during Level 2 Automation? Development and Evaluation of an HMI Concept Using Affective Elements and Message Framing

Hecht

Zhou

Bengler

2022

Safety

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With Level 3 and 4 automated driving activated, users will be allowed to engage in a wide range of non-driving related activities (NDRAs). Although Level 2 automation can appear very similar to L3 and L4, drivers are required to always monitor the system. However, past research has found drivers neglect this obligation at least partly and instead engage in NDRAs. Since this behavior can have negative impacts on traffic safety, the goal of this work was to develop a human–machine interface (HMI) concept to motivate users to continue their supervision task. This work’s concept used message framing in connection with affective elements. Every three minutes, messages were displayed on the head-up display. To evaluate the affective message concept’s (AMC) effectiveness, we conducted a between-subject driving simulator study (baseline vs. advanced HMI) with 32 participants and 45 min of driving time with both L2 and L4 phases and a silent system malfunction. Results show the road attention ratio decreases and the NDRA engagement ratio increases over time only for baseline participants. Participants supported by the AMC did not show a change over time in monitoring behavior and NDRA engagement. However, no effect on the drivers’ reaction to the system failure became apparent. No effects on subjective workload and user experience were found. Additional research is needed to further investigate the safety implications and long-term effectiveness of the concept, as well as a driver-state-dependent design.

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Section: Introductionsupporting

confidence: 84%

How to Keep Drivers Attentive during Level 2 Automation? Development and Evaluation of an HMI Concept Using Affective Elements and Message Framing

Hecht

Zhou

Bengler

2022

Safety

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“…Without TOR, drivers take longer to react and have more accidents, which could indeed confirm that drivers neglect their supervisory task (Boos et al, 2020) and perform worse in reacting to critical situations. It appears here that the TOR positively affects takeover performance.…”

Section: Discussionmentioning

confidence: 90%

“…On the one hand, research suggests that drivers may neglect their monitoring task (Banks et al, 2018) and do not always respect safety requirements. Indeed, Boos et al (2020) showed that drivers who were well aware of their responsibility to supervise the road tended to neglect their supervision task, in part because they were too confident in their system. On the other hand, when drivers performed NRDT while the system was in AD, they were considered as not being in the driving control loop.…”

Section: Out-of-the-loopmentioning

confidence: 99%

Takeover performance according to the level of disengagement during automated driving

Gallouin

Wang

Beillas

et al. 2022

Proceedings of the 7th International Digital Human Modeling Symposium -

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Taking over the manual control of a car after Automated Driving (AD) is a key issue for future road safety. However, performance to resume this manual control may be dependant of the driver's level of engagement in driving during AD. Indeed, according to the level of automation (from L2 to L3 of the SAE), drivers will be in charge of monitoring the driving situation, or will be allowed to perform nondriving related tasks (NDRT) and thus, to be fully disengaged of the driving task. In this context, the present study aims to investigate the influence of the driver's level of engagement/disengagement during AD on takeover performance using a driving simulator. Four levels of engagement/disengagement were studied: (C1) being engaged in driving situation monitoring without TakeOver Request (TOR) to resume the manual control, (C2) being engaged in driving situation monitoring with a TOR to resume the manual control, (C3) being disengaged of the driving monitoring by performing a cognitively demanding secondary task with a TOR to resume the manual control, and (C4) being disengaged of the driving monitoring in a relaxed position situation with eyes closed and with a TOR to resume the manual control.Forty participants were performed sixteen critical takeover scenarios involving different critical takeover situations. Drivers reaction times and collision risks were measured to assess their takeover performances and to investigate the safety of automation levels 2 and 3. Driving situation monitoring with a TOR (C2) induce shortest reaction times and a lower number of collisions. For the relaxed posture (C4), drivers took longer time to react than the other three conditions. Driving situation monitoring without TOR (C1), had the highest number of collisions. This suggests that the engagement in driving is not always effective and efficient without TOR. Moreover, being in a relaxed position during automated driving decreases takeover performance.

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“…This refers to confusion about the activated automation mode and its associated capabilities. Such confusion can be dangerous because it can lead to incorrect actions from the driver or 'mode errors' (Boos et al, 2020;Sarter & Woods, 1995). For example, in Boos et al (2020) participants missed a silent failure of the automation and ended up in the hard shoulder of the road.…”

Section: Introductionmentioning

confidence: 99%

“…Such confusion can be dangerous because it can lead to incorrect actions from the driver or 'mode errors' (Boos et al, 2020;Sarter & Woods, 1995). For example, in Boos et al (2020) participants missed a silent failure of the automation and ended up in the hard shoulder of the road. Mode awareness can be supported by a well-designed Human Machine Interface (HMI, Carsten & Martens, 2019).…”

Section: Introductionmentioning

confidence: 99%

Development and evaluation of a human machine interface to support mode awareness in different automated driving modes

Tinga¹,

Zeumeren²,

Christoph³

et al. 2023

Transportation Research Part F: Traffic Psychology and Behaviou

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Mode Errors and Intentional Violations in Visual Monitoring of Level 2 Driving Automation

Cited by 12 publications

References 19 publications

How to Keep Drivers Attentive during Level 2 Automation? Development and Evaluation of an HMI Concept Using Affective Elements and Message Framing

How to Keep Drivers Attentive during Level 2 Automation? Development and Evaluation of an HMI Concept Using Affective Elements and Message Framing

Takeover performance according to the level of disengagement during automated driving

Development and evaluation of a human machine interface to support mode awareness in different automated driving modes

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