Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Wireless driver and vehicle surveillance system based on IEEE 802.11 networks
Nets4Cars/Nets4Trains'12 Proceedings of the 4th international conference on Communication Technologies for Vehicles
Architecture of car measurement system for driver monitoring
Nets4Cars/Nets4Trains'12 Proceedings of the 4th international conference on Communication Technologies for Vehicles
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Recent advances in practical adaptive driving assistance systems and sensing technology for automobiles have led to a detailed study of individual human driving behavior. In such a study, we need to deal with a large amount of stored data, which can be managed by splitting the analysis according to the driving states described by driver maneuvers and driving environment. As the first step of our long-term project, the driving behavior learning is formulated as a recognition problem of the driving states. Here, the classifier for recognizing the driving states is modeled via the boosting sequential labeling method (BSLM). We consider the recognition problems formed from driving data of three subject drivers who drove on two roads. In each problem, the classifier trained through BSLM is validated by analyzing the recognition accuracy of each driving state. The results indicate that even though the recognition accuracies of braking and decelerating states are mediocre, accuracies of the following, cruising an stopping states are exceptionally precise.