Lateral Motion Prediction of On-Road Preceding Vehicles: A Data-Driven Approach

Chen Wang, Jacques Delport, Yan Wang

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Drivers' behaviors and decision making on the road directly affect the safety of themselves, other drivers, and pedestrians. However, as distinct entities, people cannot predict the motions of surrounding vehicles and they have difficulty in performing safe reactionary driving maneuvers in a short time period. To overcome the limitations of making an immediate prediction, in this work, we propose a two-stage data-driven approach: classifying driving patterns of on-road surrounding vehicles using the Gaussian mixture models (GMM); and predicting vehicles' short-term lateral motions (i.e., left/right turn and left/right lane change) based on real-world vehicle mobility data, provided by the U.S. Department of Transportation, with different ensemble decision trees. We considered several important kinetic features and higher order kinematic variables. The research results of our proposed approach demonstrate the effectiveness of pattern classification and on-road lateral motion prediction. This methodology framework has the potential to be incorporated into current data-driven collision warning systems, to enable more practical on-road preprocessing in intelligent vehicles, and to be applied in autopilot-driving scenarios.

Original languageEnglish (US)
JournalSensors (Basel, Switzerland)
Volume19
Issue number9
DOIs
StatePublished - May 7 2019
Externally publishedYes

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Keywords

  • data mining
  • data-driven intelligent vehicles
  • driver behavior classification
  • lateral motion prediction
  • vehicle mobility data

ASJC Scopus subject areas

  • Analytical Chemistry
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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