Intention Recognition for Vehicle Driving by Sensing of User and Environment

I. E. Yairi, T. Yairi, and S. Igi (Japan)


Pattern Recognition, Machine Learning, Sup port Vector Machine, Microlevel Intentions, Multi dimensional Timeseries


Real-time intention recognition by sensing a user and its environment is an important function for man machine interaction. However, conventional methods are not applicable to the recognition of micro-level in tentions, because it is difficult to divide a user's micro level behavior into a clear sequence of action fractions and it varies from person to person. Our approach to this problem is to apply a pattern learning function which discovers and utilizes synchronous and tempo ral relations among the multi-dimensional time-series data of the user and environment sensors. In this pa per, we deal with a vehicle driving task as a typical ap plication of the proposed intention recognition method, and examine the realizability of the pattern learning function by SVM. Importance of sensing both a user and the environment for intention recognition in vehi cle driving is discussed as well as our future plan.

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