Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications
This work addresses the need for efficient motion planning in intelligent vehicles by learning basic driving skills from data, but it appears incremental as it builds on existing methods like Expectation-Maximization and dynamic movement primitives.
The paper tackles the problem of decomposing complex driving tasks into motion primitives from unlabeled trajectory data, using a probabilistic inference method based on Expectation-Maximization to segment trajectories and learn a library of motion primitives represented by dynamic movement primitives, with results showing it can find proper segmentation and establish the library simultaneously.
Developing an intelligent vehicle which can perform human-like actions requires the ability to learn basic driving skills from a large amount of naturalistic driving data. The algorithms will become efficient if we could decompose the complex driving tasks into motion primitives which represent the elementary compositions of driving skills. Therefore, the purpose of this paper is to segment unlabeled trajectory data into a library of motion primitives. By applying a probabilistic inference based on an iterative Expectation-Maximization algorithm, our method segments the collected trajectories while learning a set of motion primitives represented by the dynamic movement primitives. The proposed method utilizes the mutual dependencies between the segmentation and representation of motion primitives and the driving-specific based initial segmentation. By utilizing this mutual dependency and the initial condition, this paper presents how we can enhance the performance of both the segmentation and the motion primitive library establishment. We also evaluate the applicability of the primitive representation method to imitation learning and motion planning algorithms. The model is trained and validated by using the driving data collected from the Beijing Institute of Technology intelligent vehicle platform. The results show that the proposed approach can find the proper segmentation and establish the motion primitive library simultaneously.