Devil in the details: Systematic review of TOR signals in automated driving with a generic classification framework

Jansen, R.J.; Tinga, A.M.; Zwart, R. de; Kint, S.T. van der

Meta studies on factors contributing to take-over performance did not include the design of take-over request (TOR) signals, other than the modality at which TORs are presented. A detailed understanding of the influence of TOR design on take-over performance is therefore lacking.

To gain an overview of the level of detail with which TOR designs are reported in academic literature, by using and evaluating a novel classification framework. In this framework TORs are classified in terms of modalities, classes, and underlying attributes. Furthermore, the framework involves classification of potentially competing background signals, as well as the setting in which a study is performed.

A systematic review was performed on articles written in English that were published between 2014 and 2021 using Web of Science, as well as articles retrieved from two previous TOR classification studies and three meta studies on take-over performance. Studies were considered for subsequent analysis if they involved a downward transition of the level of automation following a TOR, resulting in a sample of 391 TORs found in 189 studies.

No predominant TOR design was found, and a considerable part of the available design space has not yet been explored. Studies reported less information on TOR designs when examining TOR designs at an increased level of detail. On average, attribute information was reported for half of the TORs per class.

More attention towards a detailed description of TOR implementations is needed and how this can impact experimental findings. The classification framework and the corresponding coding sheet could support systematic reporting and subsequent meta-analysis in future work. This way, a better understanding about the impact of TOR design on take-over performance can be gained, which in turn can support implementation of safe and effective TORs in (automated) vehicles.

Published in
Transportation Research Part F: Traffic Psychology and Behaviour
91 (November 2022)

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