C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
Machine Learning
Understanding and Using Context
Personal and Ubiquitous Computing
The pothole patrol: using a mobile sensor network for road surface monitoring
Proceedings of the 6th international conference on Mobile systems, applications, and services
Computational Intelligence: An Introduction
Computational Intelligence: An Introduction
The WEKA data mining software: an update
ACM SIGKDD Explorations Newsletter
Towards the automotive HMI of the future: overview of the AIDE-integrated project results
IEEE Transactions on Intelligent Transportation Systems
Hang on a sec!: effects of proactive mediation of phone conversations while driving
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Undistracted driving: a mobile phone that doesn't distract
Proceedings of the 12th Workshop on Mobile Computing Systems and Applications
Sensing vehicle dynamics for determining driver phone use
Proceeding of the 11th annual international conference on Mobile systems, applications, and services
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This paper discusses how the driving context (driving events and distraction level) can be determined using a mobile phone equipped with several sensors. The majority of existing in-car communication systems (ICCS) available today are built-in and do not use the driving context. This creates two issues: firstly, the use of an ICCS is limited to specific cars and secondly, the driver's safety remains an issue, as the driving context is not taken into account. This paper discusses two experiments in which data are collected, trained and tested in order to create a model that predicts driving events and distraction level. A mobile, context-aware application was built using the MIMIC (Multimodal Interface for Mobile Info-communication with Context) Framework. The Inference Engine uses information from several sources, namely mobile sensors, GPS and weather information, to infer both the driving event and the distraction level. The results obtained showed that the driving events and the distraction level can be accurately predicted. The driving events were predicted using the IB1 technique with an accuracy of 92.25%. In the second experiment, the distraction level was predicted with 95.16% accuracy, using the KStar (decision tree) technique. An analysis of the decision tree showed that some variables were more important than others in predicting the driving context. These variables included the speed and direction, as well as acceleration, magnetic field and orientation.