Trajectory Clustering Aided Personalized Driver Intention Prediction for Intelligent Vehicles

Dewei Yi, Jinya Su*, Cunjia Liu, Wen-Hua Chen

*Corresponding author for this work

Research output: Contribution to journalArticle

7 Citations (Scopus)

Abstract

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 behaviors. Then, various classifiers are evaluated to link low-level vehicle states to high-level driving behaviors. CART classifier with Bayesian optimization excels others in accuracy and computation. The proposed system is validated by a real-world driving dataset. Comparative experimental results indicate that PRM clustering can discover more in-depth driving behaviors than manually defined maneuver 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.
Original languageEnglish
Pages (from-to)3693-3702
Number of pages10
JournalIEEE Transactions on Industrial Informatics
Volume15
Issue number6
Early online date28 Dec 2018
DOIs
Publication statusPublished - Jun 2019

Keywords

  • Driver behavior prediction
  • intelligent vehicle
  • polynomial regression mixture (PRM)
  • trajectory clustering

Fingerprint Dive into the research topics of 'Trajectory Clustering Aided Personalized Driver Intention Prediction for Intelligent Vehicles'. Together they form a unique fingerprint.

  • Cite this