Visual-Manual Distraction Detection Using Driving Performance Indicators With Naturalistic Driving Data

Zhaojian Li · Shan Bao · Ilya V. Kolmanovsky · Xiang Yin

92 citations0/0 files storedIEEE Transactions on Intelligent Transportation Systems

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Human-Automation Interaction and Safety Traffic and Road Safety Sleep and Work-Related Fatigue

Abstract

This paper investigates the problem of driver distraction detection using driving performance indicators from onboard kinematic measurements. First, naturalistic driving data from the integrated vehicle-based safety system program are processed, and cabin camera data are manually inspected to determine the driver's state (i.e., distracted or attentive). Second, existing driving performance metrics, such as steering entropy, steering wheel reversal rate, and lane offset variance, are reviewed against the processed naturalistic driving data. Furthermore, a nonlinear autoregressive exogenous (NARX) driving model is developed to predict vehicle speed based on the range (distance headway), range rate, and speed history. For each driver, the NARX model is then trained on the attentive driving data. We show that the prediction error is correlated with driver distraction. Finally, two features, steering entropy and mean absolute speed prediction error from the NARX model are selected, and a support vector machine is trained to detect driving distraction. Prediction performances are reported.

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References

2003Financial time series forecasting using support vector machines 2001Application of support vector machines in financial time series forecasting 2005Effects of visual and cognitive load in real and simulated motorway driving 2001Financial time series prediction using least squares support vector machines within the evidence framework 1999Development of a Steering Entropy Method for Evaluating Driver Workload 2016Detection of Driver Cognitive Distraction: A Comparison Study of Stop-Controlled Intersection and Speed-Limited Highway

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