Towards personalised automated driving: Prediction of preferred ACC behaviour based on manual driving
conference paper
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%.
Topics
Traffic engineering computingBehavioural sciences computingPattern matchingRoad vehiclesSensorsAdvanced driver assistance systemsADASsArtificial intelligenceAutomobile driversIntelligent vehicle highway systemsLearning systemsACC systemsACCAutomated drivingMachine learning techniquesManual drivingSensor inputsAdaptive cruise control
TNO Identifier
572372
ISBN
9781509018215
Publisher
Institute of Electrical and Electronics Engineers Inc.
Article nr.
7535544
Source title
2016 IEEE Intelligent Vehicles Symposium, IV 2016, 19 June 2016 through 22 June 2016
Pages
1211-1216
Files
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