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- W2318784603 abstract "Recent years have brought hope that driving support systems tailored to the characteristics of each driver can be developed. To accomplish this, a driver model must be constructed that considers the driver's psychological function when inferring driver behavior. This paper thus proposes a method to infer driver behavior by capturing time-series steering angle data at the time of lane change. The proposed method uses a static type conditional Gaussian model on Bayesian Networks. By using this method, if the driver behavior of the subject and learned data nearness of features (norms) are below a certain level, it is possible to infer driver behavior with nearly 100% probability. Moreover, compared to the HMM models, this method reduces the rate of incorrect inference inclusion." @default.
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- W2318784603 date "2007-01-01" @default.
- W2318784603 modified "2023-10-18" @default.
- W2318784603 title "A Study of Driver Behavior Inference Model at Time of Lane Change Using Bayesian Networks" @default.
- W2318784603 doi "https://doi.org/10.1299/kikaic.73.1794" @default.
- W2318784603 hasPublicationYear "2007" @default.
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