Conference Proceeding
Authors: Callum Mole (School of Psychology) , Gustav Markkula (Institute of Transport Studies) , Oscar Giles (School of Psychology, Institute of Transport Studies) , Yuki Okafuji (Department of Mechanical Engineering) , Richard Romano (Institute of Transport Studies) , Natasha Merat (Institute of Transport Studies) , Richard Wilkie (School of Psychology)
The human perceptual-motor system remains well-calibrated during manual driving supporting successful steering despite changing conditions, such as alterations in vehicle speed. Automated vehicles may interrupt perceptual-motor calibration so that when a driver takes-over control they will not be prepared for the driving conditions. Optic flow is a powerful source of visual information for calibrating to speed changes during manual steering, but it is currently unclear whether humans are sensitive to changes in optic flow speed when they are not in active control of the vehicle (i.e. by relying upon vision alone). Here we used a driving simulator to examine sensitivity to changes in optic flow speed across active (manual steering) and passive (automated steering) modes of control. Optic flow speed was altered independent of vehicle speed. The mismatch between perceived speed and actual speed causes a well-calibrated motor system to be reliably biased. Drivers were asked to take-over manual steering control after a short (~10 s) period of automation. Results showed that manual steering was not biased when flow speed was manipulated only in the automated period. One interpretation is that drivers had trouble recalibrating to optic flow changes that occurred during automated driving. If so, this suggests that there will exist a period where the perceptual-motor system is miscalibrated in the early stages of take-over after automated vehicle control.
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How to Cite: Mole, C. , Markkula, G. , Giles, O. , Okafuji, Y. , Romano, R. , Merat, N. & Wilkie, R. (2019) “Drivers Fail to Calibrate to Optic Flow Speed Changes During Automated Driving”, Driving Assessment Conference. 10(2019). doi: https://doi.org/10.17077/drivingassessment.1683