cff-version: 1.2.0
abstract: "<p>As automated vehicles require human drivers to resume control in critical situations, predicting driver takeover behaviour could be beneficial for safe transitions of control. While previous research has explored predicting takeover behaviour in relation to driver state and traits, little work has examined the predictive value of manual driving style. We hypothesised that drivers’ behaviour during manual driving is predictive of their takeover behaviour when resuming control from an automated vehicle. We assessed 38 drivers with varying experience in a high-fidelity driving simulator. After completing manual driving sessions to assess their driving style, participants performed an automated driving task, typically on a subsequent date. Measures of driving style from manual driving sessions, including headway and lane change speed, were found to be predictive of takeover behaviour. The level of driving experience was associated with the behavioural measures, but correlations between measures of manual driving style and takeover behaviour remained after controlling for driver experience. Our findings demonstrate that how drivers reclaim control from their automated vehicle is not an isolated phenomenon but is associated with manual driving behaviour and driving experience. Strategies to improve takeover safety and comfort could be based on driving style measures, for example by the automated vehicle adapting its behaviour to match a driver’s driving style.</p>"
authors:
  - family-names: de Winter
    given-names: Joost
    orcid: "https://orcid.org/0000-0002-1281-8200"
  - family-names: Verschoor
    given-names: Koen
  - family-names: Doubek
    given-names: Fabian
  - family-names: Happee
    given-names: R. (Riender)
    orcid: "https://orcid.org/0000-0001-5878-3472"
title: "Supplementary data for the paper &#39;Once a driver, always a driver—Manual driving style persists in automated driving takeover&#39;"
keywords:
version: 1
identifiers:
  - type: doi
    value: 10.4121/57e36cf2-4f74-4d2f-86e2-7fae7250438a.v1
license: CC BY 4.0
date-released: 2024-09-30