Citation

Smeets J, Öztürk K, Liebich R. Transp. Res. C Emerg. Technol. 2024; 161: e104569.

Copyright

(Copyright © 2024, Elsevier Publishing)

DOI

10.1016/j.trc.2024.104569

PMID

unavailable

Abstract

The digital twin, with its descriptive and predictive services provides promising prospects in the automotive field. Increased insights in customer behavior and vehicle states through descriptive models enable the predictive services of the digital twin to project these vehicles in the future. Currently, most of the predictive services of the digital twin focus on a singular dimension, the asset itself, without considering the influences of the environment and the driver, which hampers the reliability and accuracy of these models. The present work aims to provide a solution in the form of a holistic digital twin comprising of three descriptive models representing the motorcycle itself, its operating environment, and the motorcycle riding behavior. Based on data gathered during a large-scale measurement campaign, novel insights in the motorcycle riding behavior have enabled its representation into two mathematical formulations. Highlighting the capability of the digital twin to integrate data from heterogeneous sources, the environmental model is generated using geospatial data from a map provider, followed by a novel formulation of a safety driving line. Using a kinematic motorcycle model, the speed and banking angle over the defined route are predicted with high correlation to real-world motorcycle riding behavior. The insights generated by the developed digital twin can be used to enable data-driven development or as an input to provide individualized predictive services during the usage phase.

Language: en

Keywords

Banking angle prediction; Digital twin; Motorcycle riding behavior characterization; Speed prediction; Two-wheeled vehicles