Noncritical State Transitions During Conditionally Automated Driving on German Freeways: Effects of Non–Driving Related Tasks on Takeover Time and Takeover Quality

Frederik Naujoks · Christian Purucker · Katharina Wiedemann · Claus Marberger

104 citations0/0 files storedHuman Factors The Journal of the Human Factors and Ergonomics Society

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Human-Automation Interaction and Safety Vehicle Dynamics and Control Systems Autonomous Vehicle Technology and Safety

Abstract

OBJECTIVE: This study aimed at investigating the driver's takeover performance when switching from working on different non-driving related tasks (NDRTs) while driving with a conditionally automated driving function (SAE L3), which was simulated by a Wizard of Oz vehicle, to manual vehicle control under naturalistic driving conditions. BACKGROUND: Conditionally automated driving systems, which are currently close to market introduction, require the user to stay fallback ready. As users will be allowed to engage in more complex NDRTs during the automated drive than when driving manually, the time needed to regain full manual control could likely be increased. METHOD: Thirty-four users engaged in different everyday NDRTs while driving automatically with a Wizard of Oz vehicle. After approximately either 5 min or 15 min of automated driving, users were requested to take back vehicle control in noncritical situations. The test drive took place in everyday traffic on German freeways in the metropolitan area of Stuttgart. RESULTS: Particularly tasks that required users to turn away from the central road scene or hold an object in their hands led to increased takeover times. Accordingly, increased variance in the driver's lane position was found shortly after the switch to manual control. However, the drivers rated the takeover situations to be mostly "harmless." CONCLUSION: Drivers managed to regain control over the vehicle safely, but they needed more time to prepare for the manual takeover when the NDRTs caused motoric workload. APPLICATION: The timings found in the study can be used to design comfortable and safe takeover concepts for automated vehicles.

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References

1990Subjective and Objective Sleepiness in the Active Individual 1995The Out-of-the-Loop Performance Problem and Level of Control in Automation 2013“Take over!” How long does it take to get the driver back into the loop?2014Effects of adaptive cruise control and highly automated driving on workload and situation awareness: A review of the empirical evidence 2017Takeover Time in Highly Automated Vehicles: Noncritical Transitions to and From Manual Control 2014Transition to manual: Driver behaviour when resuming control from a highly automated vehicle 2003Preparing to resume an interrupted task: effects of prospective goal encoding and retrospective rehearsal 2014How Traffic Situations and Non-Driving Related Tasks Affect the Take-Over Quality in Highly Automated Driving

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2021DeepTake: Prediction of Driver Takeover Behavior using Multimodal Data 2021Modeling takeover behavior in level 3 automated driving via a structural equation model: Considering the mediating role of trust 2020Effects of Non-Driving-Related Task Attributes on Takeover Quality in Automated Vehicles 2021How drowsiness and distraction can interfere with take-over performance: A systematic and meta-analysis review

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