ICDM '09 Proceedings of the 9th Industrial Conference on Advances in Data Mining. Applications and Theoretical Aspects
On the roles of eye gaze and head dynamics in predicting driver's intent to change lanes
IEEE Transactions on Intelligent Transportation Systems
IEEE Transactions on Intelligent Transportation Systems
Consensus-based distributed linear support vector machines
Proceedings of the 9th ACM/IEEE International Conference on Information Processing in Sensor Networks
IEEE Transactions on Intelligent Transportation Systems
Consensus-Based Distributed Support Vector Machines
The Journal of Machine Learning Research
Non-intrusive Detection of Driver Distraction using Machine Learning Algorithms
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Model-based analysis and classification of driver distraction under secondary tasks
IEEE Transactions on Intelligent Transportation Systems
Driving distraction analysis by ECG signals: an entropy analysis
IDGD'11 Proceedings of the 4th international conference on Internationalization, design and global development
Automation effects on driver's behaviour when integrating a PADAS and a distraction classifier
ICDHM'11 Proceedings of the Third international conference on Digital human modeling
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As use of in-vehicle information systems (IVISs) such as cell phones, navigation systems, and satellite radios has increased, driver distraction has become an important and growing safety concern. A promising way to overcome this problem is to detect driver distraction and adapt in-vehicle systems accordingly to mitigate such distractions. To realize this strategy, this paper applied support vector machines (SVMs), which is a data mining method, to develop a real-time approach for detecting cognitive distraction using drivers' eye movements and driving performance data. Data were collected in a simulator experiment in which ten participants interacted with an IVIS while driving. The data were used to train and test both SVM and logistic regression models, and three different model characteristics were investigated: how distraction was defined, which data were input to the model, and how the input data were summarized. The results show that the SVM models were able to detect driver distraction with an average accuracy of 81.1%, outperforming more traditional logistic regression models. The best performing model (96.1% accuracy) resulted when distraction was defined using experimental conditions (i.e., IVIS drive or baseline drive), the input data were comprised of eye movement and driving measures, and these data were summarized over a 40-s window with 95% overlap of windows. These results demonstrate that eye movements and simple measures of driving performance can be used to detect driver distraction in real time. Potential applications of this paper include the design of adaptive in-vehicle systems and the evaluation of driver distraction