Noncritical State Transitions During Conditionally Automated Driving on German Freeways: Effects of Non–Driving Related Tasks on Takeover Time and Takeover Quality
Restricted accessResearch articleFirst published online June, 2019
Noncritical State Transitions During Conditionally Automated Driving on German Freeways: Effects of Non–Driving Related Tasks on Takeover Time and Takeover Quality
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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