Take-over requests during highly automated driving: How should they be presented and under what conditions?

Brandenburg, Stefan; Chuang, Lewis L.

doi:10.1016/j.trf.2019.08.023

Cited by 29 publications

(12 citation statements)

References 30 publications

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“…According to Gold, Naujoks [42] and the suggestion of existing evidence, we considered covering three types of tasks in the training protocol: 1) Critical takeover tasks (critical): These takeover tasks constitute the most crucial part of the challenges associated with highly automated driving, and it is necessary to prepare the driver for such circumstances before the initial exposure to real driving. 2) Non-critical takeover tasks (non-critical): Non-critical takeover tasks have also been discussed as important activities in automated driving [43,44]. Compared to critical takeover events, these scenarios have less criticality and urgency or are predictable, though familiarizing the driver with these kinds of non-critical system-initiated transitions could be essential to a pre-trip training program.…”

Section: What Do First-time Drivers Need To Learn About Automation?mentioning

confidence: 99%

Virtual reality tour for first-time users of highly automated cars: Comparing the effects of virtual environments with different levels of interaction fidelity

et al. 2021

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Section: What Do First-time Drivers Need To Learn About Automation?mentioning

confidence: 99%

Virtual reality tour for first-time users of highly automated cars: Comparing the effects of virtual environments with different levels of interaction fidelity

et al. 2021

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“…We suggested that the pupil size and fixation duration can be added to the factors, which can be obtained by the in-car camera. These factors can be used to judge the driver's state, the priately (Brandenburg & Chuang, 2019). An automated driving take-over system should clearly communicate the functions that are being handled by the automation and those expected by the driver.…”

Section: Discussionmentioning

confidence: 99%

“…In the meantime, head‐up displays of cars are located on the bottom and left sides of the windshield. The design of the human‐machine interface influences how a user understands the situation to respond appropriately (Brandenburg & Chuang, 2019). An automated driving take‐over system should clearly communicate the functions that are being handled by the automation and those expected by the driver.…”

Section: Discussionmentioning

confidence: 99%

Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving

Niu

Sun

et al. 2020

Hum Ftrs & Erg Mfg Svc

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A high degree of automated driving distracts drivers more easily, resulting in slow recognition of critical events during driving and slow responses to emergencies. Automated driving and manual switching processes are also prone to erroneous decisions. We conducted a simulated automated driving experiment to study participants' visual perception characteristics during the takeover of vehicle control. The present study used dynamic videos to imitate the driving situations when drivers returned their gaze from the distractive source to the road. We collected the drivers' eye movement data to analyze the search strategy and physiological characteristics of the drivers after the takeover reminder. The results showed that the instant information search method of the drivers was scanning of the driving scene. When the degree of distraction deepened and the hazard level of scenes increased, the pupil diameter of the drivers increased and the fixation duration became longer. These findings can help to design takeover warnings and support more intelligent automated driving systems to judge whether measures should be taken to interfere with the driver's operation to avoid collisions. Furthermore, the drivers' fixation point distribution focused on the left side and the lower side of the scene. We suggest that the takeover warning is displayed in the head-up display. This study provides a better understanding of drivers' visual perception characteristics when drivers' eyesight returns from other distractors to the driving scene and a good theoretical basis for the design of hazard warning information for automated driving.

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“…Additional research indicated that the existence of curves increased lateral acceleration and reaction time to respond to a TOR [16]. Furthermore, the authors in [17] argued that drivers tend to make abrupt deceleration maneuvers after regaining the control of the vehicle when entering a curve.…”

Section: Related Workmentioning

confidence: 99%

Real-World Evaluation of the Impact of Automated Driving System Technology on Driver Gaze Behavior, Reaction Time and Trust

Morales-Alvarez

Marouf

Tadjine

et al. 2021

2021 IEEE Intelligent Vehicles Symposium Workshops (IV Workshops)

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Recent developments in advanced driving assistance systems (ADAS) that rely on some level of autonomy have led the automobile industry and research community to investigate the impact they might have on driving performance. However, most of the research performed so far is based on simulated environments. In this study we investigated the behavior of drivers in a vehicle with automated driving system (ADS) capabilities in a real life driving scenario. We analyzed their response to a take over request (TOR) at two different driving speeds while being engaged in non-driving-related tasks (NDRT). Results from the performed experiments showed that driver reaction time to a TOR, gaze behavior and self-reported trust in automation were affected by the type of NDRT being concurrently performed and driver reaction time and gaze behavior additionally depended on the driving or vehicle speed at the time of TOR.

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Take-over requests during highly automated driving: How should they be presented and under what conditions?

Cited by 29 publications

References 30 publications

Virtual reality tour for first-time users of highly automated cars: Comparing the effects of virtual environments with different levels of interaction fidelity

Virtual reality tour for first-time users of highly automated cars: Comparing the effects of virtual environments with different levels of interaction fidelity

Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving

Real-World Evaluation of the Impact of Automated Driving System Technology on Driver Gaze Behavior, Reaction Time and Trust

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