基于多重示范的智能车辆运动基元表征与序列生成

为解决不同驾驶风格的类人驾驶轨迹生成问题，以包含同类型多重示范的真实行车轨迹数据集为依托，提出一种基于驾驶风格参量提取的运动基元表征及基元序列生成方法。单一运动基元以改进的动态运动基元方法进行表征，并引入奇异值分解实现同类型轨迹集合中主要形状表征参量与驾驶风格参量的分离；基元序列则是在关联各独立表征参量的基础上，利用准均匀B样条 曲线进行序列拟合。结果表明：单一运动基元表征方法在保证轨迹表征精度的同时，扩展了基元依据驾驶风格的泛化调整能力；基元序列生成方法既实现了对目标点航向和位置的钳位，又保证了各独立基元之间的平滑过渡。

Representation of Motion Primitives of Intelligent Vehicle Based on Multiple Demonstrations and Generation of Their Sequences

In order to solve the problem of human-like trajectory generation in different driving styles, a driving style parameter extraction-based motion primitive representation method and a corresponding motion primitive sequence generation method are proposed based on the real driving trajectory data set containing the same type of multiple demonstrations. The single motion primitive is represented by a modified dynamic motion primitive method, and the singular value decomposition is introduced to separate the main shape representation parameters and driving style parameters in the same type of trajectory set. The sequence of motion primitives is fitted with a clamped B-spline curve on the premise of correlating the parameters of the independent motion primitives. The results show that the proposed single motion primitive representation method not only guarantees the accuracy of trajectory representation, but also expands the generalized adjustment ability of primitives according to driving style. On this basis, the associated primitive sequence not only achieves the clamping of the course and position of target point, but also ensures a smooth transition between the individual primitives.