Evaluation of Imminent Take-Over Requests With Real Automation on a Test Track

Wintersberger, Philipp; Schartmüller, Clemens; Shadeghian-Borojeni, Shadan; Frison, Anna-Katharina; Riener, Andreas

doi:10.1177/00187208211051435

Cited by 18 publications

(9 citation statements)

References 61 publications

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“…The primary category of industrial vehicles considered in studies on SA included covered forestry harvesters, wheel loaders, excavators, dump trucks, wheeled excavators, mobile cranes [18], truck platooning [27], forklifts [28], and crawler and wheeled excavators [29,30]. The commercial vehicles were mainly passenger and goods vehicles [31][32][33][34], school vehicles [35], and automated vehicles [36][37][38].…”

Section: Demographic Analysis Of the Experimentsmentioning

confidence: 99%

“…Furthermore, ref. [37] studied the visual load and the associated loss of SA by examining the takeover and driving NDRT performance. A relevant conclusion of the study validates that in the real world, drivers' responses are primarily instinctive and swifter than those observed in simulators, whereas physical and cognitive loads may not have a significant impact on reaction times, as suggested in some simulated studies.…”

Section: Authormentioning

confidence: 99%

“…The funders had no role in the design of the study; in the collection, analysis, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. Author Year Experiment Eye Tracker [18] 2017 Naturalistic driving Pupil Labs [12] 2021 Video-based (driving simulation) EyeLink 1000 Plus [21] 2022 Review NP [32] 2022 Video-based (driving simulation) SMI iViewX RED120 [36] 2021 Video-based (driving simulation) Dikablis Glasses 3 [50] 2021 Video-based (driving simulation) NP [47] 2021 Video-based (driving simulation) FOVIO FX3 [39] 2021 Video-based (driving simulation) SMI glasses [11] 2021 Review NP [37] 2021 Naturalistic driving Tobii Pro Glasses 2 [51] 2021 Video-based (driving simulation) EyeTech VT3 Mini [31] 2019 Naturalistic driving SmartEye Pro [27] 2021 Naturalistic driving Tobii Glasses 2 [52] 2020 Video-based (driving simulation) Pupil Corewearable [53] 2020 Video-based (driving simulation) Tobii X2-60 [46] 2020 Video-based (driving simulation) EyeLink 1000 Plus [34] 2020 Video-based (driving simulation) Fovio FX3 [54] 2020 Video-based (driving simulation) NP [55] 2020 Video-based (driving simulation) NP [9] 2020 Review NP [61] 2020 Naturalistic driving SmartEye Pro [33] 2019 Naturalistic driving Logitech C920-1080p HD Pro standard webcam with 30 fps [56] 2019 Video-based (driving simulation) Tobii Pro Glasses 2 [48] 2019 Video-based (driving simulation) Tobii Pro Glasses 2 [41] 2019 Review NP [30] 2019 Video-based and virtual reality Pupil Labs [57] 2019 Video-based and augmented reality Eyeglasses, with SMI's BeGaze 3.5 [58] 2018 Video-based (driving simulation) Pupil Lab Pro [49] 2018 Video-based (driving simulation) NP [35] 2018 Naturalistic driving Tobii Pro Glasses 2 [42] 2017 Video-based (driving simulation) Tobii X2-60...…”

Section: Conflicts Of Interestmentioning

confidence: 99%

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Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review

Arias-Portela,

Mora-Vargas,

Caro

2024

Applied Sciences

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The conceptual framework for assessing the situational awareness (SA) of drivers consists of three hierarchical levels: perception of the elements of the environment, comprehension of the elements, and decision-making in the near future. A common challenge in evaluating SA is the determination of the available subjective and objective techniques and their selection and integration into methodologies. Among the objective techniques, eye tracking is commonly used, considering the influence of gaze behavior on driving. This review is presented as an innovative approach to the subject matter, introducing physiological metrics based on eye tracking and investigating their application in assessing the SA of drivers. In addition, experiments and methodologies that revealed patterns at the three levels of SA were identified. For this purpose, databases were searched, and 38 papers were considered. Articles were clustered according to prevalent themes such as eye-tracking metrics, eye-tracking devices, experiment design, and the relationship between SA and eye-tracking. This review summarizes the main metrics and key findings for each article and reveals a wide relationship between the eye-tracking metrics and SA. The influence of appropriately calibrated equipment, refined data collection protocols, and adequate selection of the eye-tracking metrics was examined. Further reviews are needed to systematically collect more evidence.

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Section: Demographic Analysis Of the Experimentsmentioning

confidence: 99%

Section: Authormentioning

confidence: 99%

Section: Conflicts Of Interestmentioning

confidence: 99%

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Situational Awareness Assessment of Drivers Boosted by Eye-Tracking Metrics: A Literature Review

Arias-Portela,

Mora-Vargas,

Caro

2024

Applied Sciences

View full text Add to dashboard Cite

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“…We required that the smartphone was placed in smartphone holder near the center console (see Figure 4.3). This allowed participants to engage in a natural NDRT (Naujoks et al, 2018) while the device would not interfere during the take-over (Wintersberger et al, 2021), which could have been a safety issue on the road. Participants were instructed that once the automated system took control, they were free to use their smartphone as they wished.…”

Section: Non-driving Related Taskmentioning

confidence: 99%

“…We argue that the approach taken in this study should be complemented by more fine scaled realistic driving research in more controlled environments (cf. Wintersberger et al, 2021). This approach would permit zooming in on sensitive variables, such as gaze or steering reactions, to self-paced and urgent take-over requests.…”

Section: Limitations and Future Directionsmentioning

confidence: 99%

Keeping the Driver in the Loop While Driving With Conditional Automation

Dillmann¹

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The automation of vehicles can be divided into several steps (SAE International, 2018). Level 0 contains no automation at all. Level 1 contains driving assistance that is either longitudinal or lateral. For instance, cruise control or adaptive cruise control would assist longitudinal vehicle control by holding or adapting velocity. At partial automation (Level 2) both lateral and longitudinal driving assistance systems are combined to support the driver. An example is the combination of adaptive cruise control and lane centering functions. In combination these two assistance functions support the driver in staying in the lane and maintaining a certain speed. However, at this level of automation, the driver remains the labels entail (Flach, 1995;Woods, 1993) and shifting scientific endeavors away from observable human performance (Dekker & Hollnagel, 2004). The Loop Defined From Perception-Action Theory PerspectiveGenerally speaking, perception-action theory originates from James Gibson's seminal work on understanding drivers' and pilots' capacity to control movement adequately (Gibson et al., 1955;Gibson & Crooks, 1938). Building on Gibson's later work (Gibson, 1986) the perception-action theory approach investigates how drivers locomote by interacting with the environment through the process of perception and action loops (Bootsma, 1998;Harrison et al., 2016;Mathieu et al., 2017a). For example, the optical expansion of a stop sign in the retina of drivers guides their braking actions towards it (DeLucia et al., 2016;Lee, 1976;Morando et al., 2016). In turn, the action of braking changes the optical expansion of the stop sign relative to the driver, thereby closing the perception-action loop. Iterations of these perception-action loops allow driving behavior to emerge (Harrison et al., 2016;Mathieu et al., 2017a).In order to understand how drivers can be kept in the loop, it is necessary to understand two key processes underlying the perception-action loop: perceptual attunement and perceptual-motor calibration. Perceptual attunement entails that a driver is exposed to -and able to detect and track -the specifying (i.e. task-relevant) visual information (Bootsma, 1998). In the vehicle braking example above, drivers are perceptually attuned to visual variables such as looming, specifying the relative distance to other vehicles (Fajen & Devaney, 2006). In lane keeping tasks, drivers have been shown to perceptually attune to visual variables such as optical flow, bearing-and slay angles (Li & Chen, 2010), or the inner point of approaching curves (Land & Lee, 1994).To close the perception-action loop, drivers must be proficient in scaling their motor responses adequately to this information, a process termed perceptualmotor calibration (Brand & de Oliveira, 2017;Van Andel et al., 2017). Perceptualmotor calibration is not static but dynamically dependent on the situation. Drivers have been shown to need a period of perceptual-motor calibration when tailwind is introduced in a vehicle braking task (Fajen, 2005b) or wh...

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