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

Boyang Wang, Zirui Li, Jianwei Gong, Yidi Liu, Huiyan Chen, Chao Lu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

10 Citations (Scopus)

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.

Original languageEnglish
Title of host publication2018 IEEE Intelligent Vehicles Symposium, IV 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1191-1196
Number of pages6
ISBN (Electronic)9781538644522
DOIs
Publication statusPublished - 18 Oct 2018
Event2018 IEEE Intelligent Vehicles Symposium, IV 2018 - Changshu, Suzhou, China
Duration: 26 Sept 201830 Sept 2018

Publication series

NameIEEE Intelligent Vehicles Symposium, Proceedings
Volume2018-June

Conference

Conference2018 IEEE Intelligent Vehicles Symposium, IV 2018
Country/TerritoryChina
CityChangshu, Suzhou
Period26/09/1830/09/18

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