Skip to Main content Skip to Navigation
Conference papers

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

Boyang Wang 1, 2 Zirui Li 1 Jianwei Gong 1 Yidi Liu 1 Huiyan Chen 1 Chao Lu 1
2 IDH - Interactive Digital Humans
LIRMM - Laboratoire d'Informatique de Robotique et de Microélectronique de Montpellier
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.
Document type :
Conference papers
Complete list of metadatas
Contributor : Isabelle Gouat <>
Submitted on : Thursday, March 26, 2020 - 3:20:14 PM
Last modification on : Saturday, March 28, 2020 - 1:56:35 AM

Links full text




Boyang Wang, Zirui Li, Jianwei Gong, Yidi Liu, Huiyan Chen, et al.. Learning and Generalizing Motion Primitives From Driving Data for Path-Tracking Applications. IEEE Intelligent Vehicles Symposium (IV), Jun 2018, Changshu, China. pp.1191-1196, ⟨10.1109/IVS.2018.8500696⟩. ⟨lirmm-02520217⟩



Record views