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The Influence of Non-driving Related Tasks on Driver Availability in the Context of Conditionally Automated Driving

  • Jonas Radlmayr
  • Fabian Marco Fischer
  • Klaus Bengler
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 823)

Abstract

This study looked at the effect of three different non-driving related tasks (NDRT) (Surrogate Reference Task (SuRT), n-back task and a motoric task), instructed or free engagement into these tasks and the resulting take-over performance in two different take-over situations after conditionally automated driving.

We conducted a study with 53 participants in a static driving simulator. Participants were split into three groups and each group was assigned one of the three NDRT’s. Each participant per group experienced two different take-over situations twice, totaling in four take-overs. In addition, prior to a take-over, participants were either instructed to engage in the task assigned to their group or could choose their NDRT’s freely.

Dependent variables to assess driver availability were percent eyes on road (PEOR), standard deviation of the horizontal gaze dispersion (HGD) and blink frequency and changes in the center of pressure and contact area (COP) in the seat. To analyze take-over performance, we looked at gaze reaction time, take-over time, time to collision (TTC), standard deviation of lateral position (SDLP), longitudinal and lateral accelerations and subjective ratings.

Results showed significant changes for the different NDRT’s for the driver availability variables. The type of instruction did not show differences in the take-over performance, while we saw significant differences between the two different take-over situations.

We concluded that the influence from different take-over situations is high, while differences in driver availability can be measured well with the right sensors, but do not lead to different take-over performances.

Keywords

Automated driving Non-driving related tasks NDRT Take-over performance 

Notes

Acknowledgment

This work results from the joint project Ko-HAF - Cooperative Highly Automated Driving and has been funded by the Federal Ministry for Economic Affairs and Energy based on a resolution of the German Bundestag.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jonas Radlmayr
    • 1
  • Fabian Marco Fischer
    • 1
  • Klaus Bengler
    • 1
  1. 1.Chair of ErgonomicsTechnical University of MunichGarchingGermany

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