  Considering the driving habits which are learned from the naturalistic
driving data in the path-tracking system can significantly improve the
acceptance of intelligent vehicles. Therefore, the goal of this paper is to
generate the prediction results of lateral commands with confidence regions
according to the reference based on the learned motion primitives. We present a
two-level structure for learning and generalizing motion primitives through
demonstrations. The lower-level motion primitives are generated under the path
segmentation and clustering layer in the upper-level. The Gaussian Mixture
Model(GMM) is utilized to represent the primitives and Gaussian Mixture
Regression (GMR) is selected to generalize the motion primitives. We show how
the upper-level can help to improve the prediction accuracy and evaluate the
influence of different time scales and the number of Gaussian components. The
model is trained and validated by using the driving data collected from the
Beijing Institute of Technology (BIT) intelligent vehicle platform. Experiment
results show that the proposed method can extract the motion primitives from
the driving data and predict the future lateral control commands with high
accuracy.
