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Towards personalised automated driving: Prediction of preferred ACC behaviour based on manual driving

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Author: Gelder, E. de · Cara, I. · Uittenbogaard, J. · Kroon, L. · Iersel, S. van · Hogema, J.
Type:article
Date:2016
Publisher: Institute of Electrical and Electronics Engineers Inc.
Source:2016 IEEE Intelligent Vehicles Symposium, IV 2016, 19 June 2016 through 22 June 2016, 1211-1216
Identifier: 572372
doi: doi:10.1109/IVS.2016.7535544
ISBN: 9781509018215
Article number: 7535544
Keywords: Traffic · Traffic engineering computing · Behavioural sciences computing · Pattern matching · Road vehicles · Sensors · Advanced driver assistance systems · ADASs · Artificial intelligence · Automobile drivers · Intelligent vehicle highway systems · Learning systems · ACC systems · ACC · Automated driving · Machine learning techniques · Manual driving · Sensor inputs · Adaptive cruise control · Industrial Innovation · Fluid & Solid Mechanics · IVS - Integrated Vehicle Safety · TS - Technical Sciences

Abstract

More and more Advanced Driver Assistance Systems (ADASs) are entering the market for improving both safety and comfort. Adaptive Cruise Control (ACC) is an ADAS application that has high interaction with the driver. ACC systems use limited sensor input and have only few configuration possibilities. This may result in the behaviour of the ACC not matching user's preferences in all cases, resulting in lower acceptance of the system. In this work, we examine the possibilities for a Personalised ACC (PACC), which adapts the ACC settings such that it matches the driver preference in order to increase the acceptance. The driver preferred ACC behaviour is predicted using machine learning techniques and manual driving data. On-road experiments showed that the method is promising as it is able to discriminate between two preference clusters with an accuracy of 85%.