How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models
Journal article, 2019

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.

driver models

adaptive cruise control

visual looming

cruise control

autonomous driving

Author

Giulio Bianchi Piccinini

Chalmers, Mechanics and Maritime Sciences, Vehicle Safety

Esko Lehtonen

Chalmers, Mechanics and Maritime Sciences, Vehicle Safety

Fabio Forcolin

Volvo Group

Johan A Skifs Engström

Virginia Tech

Gustav M Markkula

University of Leeds

Johan Lodin

Volvo Group

Jesper Sandin

The Swedish National Road and Transport Research Institute (VTI)

Human Factors

0018-7208 (ISSN) 1547-8181 (eISSN)

Vol. In Press

Quantitative Driver Behaviour Modelling for Active Safety Assessment Expansion (QUADRAE)

VINNOVA, 2016-01-01 -- 2019-12-31.

Driving Forces

Sustainable development

Areas of Advance

Transport

Subject Categories

Transport Systems and Logistics

Psychology

Other Engineering and Technologies not elsewhere specified

Vehicle Engineering

Control Engineering

Environmental Health and Occupational Health

DOI

10.1177/0018720819875347

PubMed

31590570

More information

Latest update

11/13/2019