Learning to Segment and Represent Motion Primitives from Driving Data for Motion Planning Applications

TL;DR

This paper introduces a probabilistic method to segment driving trajectories into motion primitives using an Expectation-Maximization algorithm, enhancing motion planning and imitation learning for autonomous vehicles.

Contribution

It presents a novel joint segmentation and primitive learning approach leveraging mutual dependencies and initial segmentation, improving efficiency in modeling driving skills.

Findings

Successfully segments trajectories into primitives

Establishes a primitive library for driving data

Enhances motion planning and imitation learning

Abstract

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.