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Fitting cornering speed models with one-class support vector machines

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conference contribution
posted on 08.07.2020 by James Fleming, Xingda Yan, Roberto Lot
© 2019 IEEE. This paper investigates the modelling of cornering speed using road curvature as a predictive variable, which is of interest for advanced driver assistance system (ADAS) applications including eco-driving assistance and curve warning. Such models are common in the driver modelling and human factors literature, yet lack reliable parameter estimation methods, requiring an ad-hoc evaluation of the upper envelope of the data followed by linear regression to that envelope. Considering the space of possible combinations of lateral acceleration and cornering speed, we cast the modelling of cornering speed as an 'outlier detection' problem which may be solved using one-class Support Vector Machine (SVM) methods from machine learning. For an existing cornering model, we suggest a fitting method using a specific choice of kernel function in a one-class SVM. As the parameters of the cornering speed model may be recovered from the SVM solution, this provides a more robust and reproducible fitting method for this model of cornering speed than the existing envelope-based approaches. In addition, this gives comparable outlier detection performance to generic SVM methods based on Radial Basis Function (RBF) kernels while reducing training times by a factor of 10, indicating potential for use in adaptive eco-driving assistance systems that require retraining either online or between drives.

Funding

Green adaptive control for future interconnected vehicles

Engineering and Physical Sciences Research Council

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History

School

  • Mechanical, Electrical and Manufacturing Engineering

Published in

2019 IEEE Intelligent Vehicles Symposium (IV)

Pages

2457 - 2462

Source

2019 IEEE Intelligent Vehicles Symposium (IV)

Publisher

IEEE

Version

AM (Accepted Manuscript)

Rights holder

© IEEE

Publisher statement

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.

Publication date

2019-08-29

Copyright date

2019

ISBN

9781728105604

ISSN

2642-7214

Location

Paris

Event dates

9th June 2019 - 12th June 2019

Depositor

Dr James Fleming (email: J.Fleming@lboro.ac.uk | regno: 5034860). Deposit date: 8 July 2020

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