基于真实驾驶数据的运动基元提取与再生成

The human-like driving system is an essential technical way to improve the applicability and acceptance of an unmanned driving system by learning the knowledge and experience of human drivers. In order to solve the driving skills representation problem at trajectory and control level, by utilizing a large amount of collected real driving data, a hierarchical driver model based on trajectory primitives and operation primitives is proposed. The trajectory primitives are represented by the dynamic movement primitive, and the probabilistic extraction algorithm is applied to extract primitives from the unlabeled continuous trajectory data. The operation primitives use the Gaussian mixture model to complete the training process based on the extraction and classification results of the trajectory primitives. The Gaussian mixture regression(GMR) algorithm is applied to predict the steering angle. The results show that the probabilistic extraction algorithm not only utilizes the correlation between representation and extraction but also uses the reasonable setting of the initial segmentation point, which improves the efficiency of the algorithm and makes the extracted motion primitives conform to specific driving assumptions. The proposed motion primitives can not only represent the driver's driving data with high precision but also have good generalization ability to deal with the desired position and time duration change when the motion primitives are regenerated. Finally, the motion primitive library describing the driving behavior under all conditions is established, and the applicability of the motion primitives to different driving situations is significantly improved.