Corpus ID: 235422046

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

@article{MoralesAlvarez2021RealWorldEO,
  title={Real-World Evaluation of the Impact of Automated Driving System Technology on Driver Gaze Behavior, Reaction Time and Trust},
  author={Walter Morales-Alvarez and M. Marouf and H. Tadjine and C. Olaverri-Monreal},
  journal={ArXiv},
  year={2021},
  volume={abs/2106.07234}
}
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… Expand

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References

SHOWING 1-10 OF 32 REFERENCES
Automatic and manual driving paradigms: Cost-efficient mobile application for the assessment of driver inattentiveness and detection of road conditions
TLDR
A cost effective mobile application to measure gaze behavior and analyze road conditions for a request to take vehicle's control in case of an automatic driving or to avoid inattentive driving in a manual driving paradigm is presented. Expand
How Traffic Situations and Non-Driving Related Tasks Affect the Take-Over Quality in Highly Automated Driving
Highly automated driving constitutes a temporary transfer of the primary driving task from the driver to the automated vehicle. In case of system limits, drivers take back control of the vehicle.Expand
The effect of urgency of take-over requests during highly automated driving under distraction conditions
Highly automated driving may improve driving comfort and safety in the near future. Due to possible system limits of highly automated driver support, the driver is expected to take over the vehicleExpand
Automated Driving: A Literature Review of the Take over Request in Conditional Automation
TLDR
This paper summarizes and analyzes previously published works in the field of conditional automation and the TOR process, and compiles guidelines and standards related to automation in driving and highlights the research gaps that need to be addressed in future research. Expand
Taking Over Control From Highly Automated Vehicles in Complex Traffic Situations
TLDR
The present results can be used by developers of highly automated systems to appropriately design human–machine interfaces and to assess the driver’s time budget for regaining control under various driving situations and different driver states. Expand
Effects of Non-Driving Related Task Modalities on Takeover Performance in Highly Automated Driving
TLDR
Evaluating the impact of different non-driving related tasks (NDR tasks) on takeover performance in highly automated driving showed that NDR task modalities are relevant factors for takeover performance. Expand
Feel the Movement: Real Motion Influences Responses to Take-over Requests in Highly Automated Vehicles
TLDR
It is found that with motion, user responses to TORs vary depending on the road context where TORs are issued, which indicates that TORs should be designed to be aware of road context to accommodate natural user responses. Expand
“Take over!” How long does it take to get the driver back into the loop?
Raising the automation level in cars is an imaginable scenario for the future in order to improve traffic safety. However, as long as there are situations that cannot be handled by the automation,Expand
Is take-over time all that matters? The impact of visual-cognitive load on driver take-over quality after conditionally automated driving.
TLDR
The findings seem to indicate that establishing motor readiness may be carried out almost reflexively, but cognitive processing of the situation is impaired by driver distraction, which, in turn, appears to determine take-over quality. Expand
Modeling take-over performance in level 3 conditionally automated vehicles.
TLDR
Regression models were developed using 753 take-over situations recorded in a series of driving simulator experiments and accurately captured take- over time, time-to-collision and crash probability, and moderately predicted the brake application. Expand
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