ALL_TII-18-1273.pdf (1.41 MB)

Trajectory clustering aided personalized driver intention prediction for intelligent vehicles

Download (1.41 MB)
journal contribution
posted on 15.01.2019, 09:50 by Dewei Yi, Jinya Su, Cunjia Liu, Wen-Hua Chen
Early driver intention prediction plays a significant role in intelligent vehicles. Drivers exhibit various driving characteristics impairing the performance of conventional algorithms using all drivers’ data indiscriminatingly. This paper develops a personalized driver intention prediction system at unsignalized T-intersections by seamlessly integrating clustering and classification. Polynomial regression mixture (PRM) clustering and Akaike’s Information Criterion are applied to individual drivers trajectories for learning in-depth driving behaviours. Then various classifiers are evaluated to link low-level vehicle states to high-level driving behaviours. CART classifier with Bayesian optimization excels others in accuracy and computation. The proposed system is validated by a realworld driving dataset. Comparative experimental results indicate that PRM clustering can discover more in-depth driving behaviours than manually defined manoeuvres due to its fine ability in accounting for both spatial and temporal information; the proposed framework integrating PRM clustering and CART classification provides promising intention prediction performance and is adaptive to different drivers.

Funding

This work is jointly supported by the U.K. Engineering and Physical Sciences Research Council (EPSRC) Autonomous and Intelligent Systems programme under the grant number EP/J011525/1 with BAE Systems as the leading industrial partner.

History

School

  • Aeronautical, Automotive, Chemical and Materials Engineering

Department

  • Aeronautical and Automotive Engineering

Published in

IEEE Transactions on Industrial Informatics

Pages

1 - 1

Citation

YI, D. ... et al., 2018. Trajectory clustering aided personalized driver intention prediction for intelligent vehicles. IEEE Transactions on Industrial Informatics, 15 (6), pp.3693-3702.

Publisher

© Institute of Electrical and Electronics Engineers (IEEE)

Version

AM (Accepted Manuscript)

Acceptance date

20/12/2018

Publication date

2018-12-28

Notes

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works

ISSN

1551-3203

eISSN

1941-0050

Language

en

Exports