During automated driving at SAE level 2 or level 3, the driver’s role changes from an active driver to a fallback-ready driver, whose mental state must still be prepared for a takeover request (TOR) when prompted. This preliminary study investigates the sensitivity of eye-movement measures in assessing the mental state of fallback-ready drivers in automated driving. The mental state of a fallback-ready driver is considered to be defined by two major factors: the state of workload imposed by non-driving related task, and the state of drowsiness. A visual workload (using a tablet computer), a non-visual workload (performing a cognitive task), as well as a state of drowsiness (after a prolonged interval of automated driving) were induced, and their effects on driving performance were studied in a high-fidelity driving simulator. The number and size of saccadic eye-movements, as well as the durations of eyeblinks, were measured by using electrooculography. The results indicate that the percentage of small saccades was sensitive to both visually and non-visually loaded states and can therefore be used to distinguish the two types of workload, and eyeblink duration was sensitive to the state of drowsiness. These results should be helpful in understanding the fallback-ready driver’s mental state in automated driving, and can provide a new perspective on the selection of input features in developing camera-based driver state monitoring systems.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Assessing the Mental States of Fallback-Ready Drivers in Automated Driving by Electrooculography


    Contributors:


    Publication date :

    2019-10-01


    Size :

    302231 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English





    Partially automated driving as a fallback level of high automation

    Gold,C. / Damboeck,D. / Bengler,K. et al. | Automotive engineering | 2013


    Assessing Truck Drivers’ and Fleet Managers’ Opinions Towards Highly Automated Driving

    Richardson, Natalie / Doubek, Fabian / Kuhn, Kevin et al. | Springer Verlag | 2016