How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models

Giulio Bianchi Piccinini*, Esko Lehtonen, Fabio Forcolin, Johan Engström, Deike Albers, Gustav Markkula, Johan Lodin, Jesper Sandin

*Corresponding author for this work

Research output: Contribution to journalJournal articleResearchpeer-review

20 Citations (Scopus)

Abstract

Objective: This paper aims to describe and test novel computational driver models, predicting drivers’ brake reaction times (BRTs) to different levels of lead vehicle braking, during driving with cruise control (CC) and during silent failures of adaptive cruise control (ACC). Background: Validated computational models predicting BRTs to silent failures of automation are lacking but are important for assessing the safety benefits of automated driving. Method: Two alternative models of driver response to silent ACC failures are proposed: a looming prediction model, assuming that drivers embody a generative model of ACC, and a lower gain model, assuming that drivers’ arousal decreases due to monitoring of the automated system. Predictions of BRTs issued by the models were tested using a driving simulator study. Results: The driving simulator study confirmed the predictions of the models: (a) BRTs were significantly shorter with an increase in kinematic criticality, both during driving with CC and during driving with ACC; (b) BRTs were significantly delayed when driving with ACC compared with driving with CC. However, the predicted BRTs were longer than the ones observed, entailing a fitting of the models to the data from the study. Conclusion: Both the looming prediction model and the lower gain model predict well the BRTs for the ACC driving condition. However, the looming prediction model has the advantage of being able to predict average BRTs using the exact same parameters as the model fitted to the CC driving data. Application: Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving.

Original languageEnglish
JournalHuman Factors
Volume62
Issue number7
Pages (from-to)1212-1229
Number of pages18
ISSN0018-7208
DOIs
Publication statusPublished - 1 Nov 2020

Bibliographical note

Funding Information:
The authors are grateful to Vinnova?Swedish governmental agency for innovation?for providing funding to this research as part of the project Quantitative Driver Behaviour Modelling for Active Safety Assessment Expansion (QUADRAE). The authors also thank Bruno Augusto, Frida Reichenberg, and Laura W?rns for the assistance during the data collection, and all the colleagues who joined the interesting discussions within the project. This work has been carried out at SAFER?Vehicle and Traffic Safety Centre at Chalmers, Sweden. Deike Albers is also affiliated with Technical University of Munich, Germany.

Funding Information:
Knowledge resulting from this research can be helpful for assessing the safety benefits of automated driving. adaptive cruise control autonomous driving cruise control driver models visual looming vinnova https://doi.org/10.13039/501100001858 2015-04863 edited-state corrected-proof The authors are grateful to Vinnova—Swedish governmental agency for innovation—for providing funding to this research as part of the project Quantitative Driver Behaviour Modelling for Active Safety Assessment Expansion (QUADRAE). The authors also thank Bruno Augusto, Frida Reichenberg, and Laura Wörns for the assistance during the data collection, and all the colleagues who joined the interesting discussions within the project. This work has been carried out at SAFER—Vehicle and Traffic Safety Centre at Chalmers, Sweden. Deike Albers is also affiliated with Technical University of Munich, Germany. ORCID iDs Giulio Bianchi Piccinini https://orcid.org/0000-0003-1885-6360 Esko Lehtonen https://orcid.org/0000-0003-0926-1517 Deike Albers https://orcid.org/0000-0002-9923-0724

Publisher Copyright:
© 2019, Human Factors and Ergonomics Society.

Keywords

  • adaptive cruise control
  • autonomous driving
  • cruise control
  • driver models
  • visual looming
  • Human Factors
  • Road safety

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