Validating Operator Event Sequence Diagrams: The case of an automated vehicle to human driver handovers

Neville A Stanton,Pat Langdon,Mike Bradley,Ioannis Politis,Alexandros Mouzakitis,James Brown,Lee Skrypchuk,Simon Thompson,Kirsten M A Revell

doi:10.1002/hfm.20887

Abstract

AbstractPredicting what drivers will do as vehicle control is handed over to them from automation is a relatively new challenge for the motor vehicle industry. Operator Event Sequence Diagrams (OESDs) offer a way of modeling the interactions between the driver and vehicle automation in the handover of control. In this paper, two studies are presented in which a range of handover strategies are tested. The anticipated driver strategies were modeled using OESDs to serve as predictions of driver behavior. Drivers were then observed in two separate studies: (1) using a Lower‐Fidelity (vehicle seat and controls) simulator and (2) using a Higher‐Fidelity (whole vehicle) simulator. Driver behavior during a takeover task was categorized according to the signal detection paradigm into hits, misses, false alarms, and correct rejections. The results showed that for all strategies in both sets of studies, the median criterion for validity was exceeded ( > 0.8), suggesting that OESDs made good predictions of driver behavior during the handover of the vehicle from automation to manual control.

Highlights

With the impending arrival of highly automated vehicles (Stanton et al, 2020), the handover of control from computing technology to human drivers becomes an important issue (Eriksson & Stanton, 2017)
Operator Event Sequence Diagrams (OESDs) are offered as a way of predicting how drivers are most likely to behave during the process of handover of control in driving simulators
It is interesting to note that the difference in the fidelity of the simulators did not affect the performance of the OESDs

Summary

Introduction

With the impending arrival of highly automated vehicles (Stanton et al, 2020), the handover of control from computing technology to human drivers becomes an important issue (Eriksson & Stanton, 2017). This issue may be exacerbated if the driver has not been monitoring the vehicle and the road environment, as defined in SAE level 3 automation (SAE, 2018: J3016). Bringing the driver back into control of the vehicle after some time of nonengagement is not a trivial undertaking This will require drivers to become aware of a range of situational and contextual features, including (but not limited to): the road environment, weather conditions, behavior

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