Learning and Generalizing Motion Primitives from Driving Data for Path-Tracking Applications

TL;DR

This paper introduces a two-level learning framework using Gaussian Mixture Models and Regression to extract and generalize driving motion primitives, enabling accurate prediction of lateral commands for path-tracking in intelligent vehicles.

Contribution

It proposes a novel two-level structure combining path segmentation, clustering, GMM, and GMR to learn and predict driving motion primitives from naturalistic data.

Findings

High accuracy in predicting lateral control commands.

Effective extraction of motion primitives from real driving data.

Improved prediction with different time scales and Gaussian components.

Abstract

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.