How Do Drivers Respond to Silent Automation Failures? Driving Simulator Study and Comparison of Computational Driver Braking Models
Journal article, 2020
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.
autonomous driving
visual looming
cruise control
driver models
adaptive cruise control
Author
Giulio Bianchi Piccinini
Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety
Esko Lehtonen
Chalmers, Mechanics and Maritime Sciences (M2), Vehicle Safety
Fabio Forcolin
Volvo Group
Johan A Skifs Engström
Virginia Polytechnic Institute and State University
Deike Albers
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. 62 7 1212-1229Quantitative Driver Behaviour Modelling for Active Safety Assessment Expansion (QUADRAE)
VINNOVA (2015-04863), 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