Transactions of the Institute of Systems, Control and Information Engineers
Online ISSN : 2185-811X
Print ISSN : 1342-5668
Papers
An Adaptive Rear-End Collision Warning System for Drivers That Estimates Driving Phase and Selects Training Data
Kazushi IkedaHiroki MimaYuta InoueTomohiro ShibataNaoki FukayaKentaro HitomiTakashi Bando
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2011 Volume 24 Issue 8 Pages 193-199

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Abstract

The paper proposes a rear-end collision warning system for drivers, where the collision risk is adaptively set from driving signals. The system employs the inverse of the time-to-collision with a constant relative acceleration as the risk and the one-class support vector machine as the anomaly detector. The system also utilizes brake sequences for outliers detection. When a brake sequence has a low likelihood with respect to trained hidden Markov models, the driving data during the sequence are removed from the training dataset. This data selection is confirmed to increase the robustness of the system by computer simulations.

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© 2011 The Institute of Systems, Control and Information Engineers
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