Abstract
Highly safe intelligent vehicles can significantly reduce vehicle accidents by warning drivers of dangerous situations. Trajectory estimation of target vehicles is expected to be used in highly safe intelligent vehicles. Trajectory estimation requires that we estimate driver intent not detectable by sensors. The Bayesian Network (BN) building we propose for trajectory estimation related to driver intent defines driver intent hierarchically to simplify the BN as much as possible. Causal driver-intent relationships are discussed reflecting real-world motion. This raises the quality of driver-intent estimation and increasing inference performance. Experimental learning based on 2D image processing is presented to acquire probabilistic BN parameters.
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