Model-Based Analysis and Classification of Driver Distraction Under Secondary Tasks

It is well established in the literature that secondary tasks adversely affect driving behavior. Previous research has focused on discovering the general trends by analyzing the average effects of secondary tasks on a population of drivers. This paper conjectures that there may also be individual ef...

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Bibliographic Details
Published in:IEEE transactions on intelligent transportation systems 2010-09, Vol.11 (3), p.692-701
Main Authors: Ersal, T, Fuller, H J A, Tsimhoni, O, Stein, J L, Fathy, H K
Format: Article
Language:English
Subjects:
Applied sciences
Automotive engineering
Classification
Cognition & reasoning
Computer science
control theory
systems
Control systems
Data processing. List processing. Character string processing
Driver distraction
driver modeling
Drivers
Driving
Driving conditions
Exact sciences and technology
Ground, air and sea transportation, marine construction
Mathematical models
Memory organisation. Data processing
Neural networks
Predictive models
Radio control
Road transportation and traffic
Safety
secondary task
Software
support vector machine (SVM)
Support vector machine classification
Support vector machines
Tasks
Vehicle crash testing
Vehicle driving
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Summary:It is well established in the literature that secondary tasks adversely affect driving behavior. Previous research has focused on discovering the general trends by analyzing the average effects of secondary tasks on a population of drivers. This paper conjectures that there may also be individual effects, i.e., different effects of secondary tasks on individual drivers, which may be obscured within the average behavior of the population, and proposes a model-based approach to analyze them. Specifically, a radial-basis neural-network-based modeling framework is developed to characterize the normal driving behavior of a driver when driving without secondary tasks. The model is then used in a scenario of driving with a secondary task to predict the hypothetical actions of the driver, had there been no secondary tasks. The difference between the predicted normal behavior and the actual distracted behavior gives individual insight into how the secondary tasks affect the driver. It is shown that this framework can help uncover the different effects of secondary tasks on each driver, and when used together with support vector machines, it can help systematically classify normal and distracted driving conditions for each driver.
ISSN:1524-9050
1558-0016
DOI:10.1109/TITS.2010.2049741