Vehicle road departure detection using anomalies in dynamics

Hang Yang, Derek McBlane, Christina Boyd, Craig Beal, Sean Brennan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This research investigates the viability of detecting vehicle road departure via the measurements of anomalies in vehicle dynamics, especially under conditions when left and right tires experience imbalance of forces (split-μ condition). This approach is based on established low-order vehicle models to facilitate real-time implementation. Vehicle states are obtained from an INS system to obtain real-time estimates of model agreement with measured data obtained from a steer-by-wire experimental test vehicle (P1). Experimental maneuvers were conducted on various surface conditions, including four tires on dry asphalt, the two passenger tires on a low-friction patch, and all four tires on a low-friction surface. These vehicle states and steering moments were recorded from INS systems and torque transducers, in an effort to identify normal and abnormal driving modes. Results at high speeds show a yawrate mismatch between model and experimental measurements that gives a large enough signal-to-noise ratio to allow detection using anomalies in dynamics, whereas at lower speeds measurements of steering torque may be additionally needed to improve the signal-to-noise ratio. A combination of steering torque and yawrate measurements is needed to obtain comprehensive information to allow detection and determination of road departure events.

Original languageEnglish (US)
Title of host publication2016 American Control Conference, ACC 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages6314-6319
Number of pages6
ISBN (Electronic)9781467386821
DOIs
StatePublished - Jul 28 2016
Event2016 American Control Conference, ACC 2016 - Boston, United States
Duration: Jul 6 2016Jul 8 2016

Publication series

NameProceedings of the American Control Conference
Volume2016-July
ISSN (Print)0743-1619

Other

Other2016 American Control Conference, ACC 2016
CountryUnited States
CityBoston
Period7/6/167/8/16

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All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering

Cite this

Yang, H., McBlane, D., Boyd, C., Beal, C., & Brennan, S. (2016). Vehicle road departure detection using anomalies in dynamics. In 2016 American Control Conference, ACC 2016 (pp. 6314-6319). [7526662] (Proceedings of the American Control Conference; Vol. 2016-July). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ACC.2016.7526662