Title
Towards personalised automated driving: Prediction of preferred ACC behaviour based on manual driving
Author
de Gelder, E.
Cara, I.
Uittenbogaard, J.
Kroon, L.
van Iersel, S.
Hogema, J.
Publication year
2016
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%.
Subject
Fluid & Solid Mechanics
IVS - Integrated Vehicle Safety
TS - Technical Sciences
Traffic
Industrial Innovation
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
To reference this document use:
http://resolver.tudelft.nl/uuid:ad7a845f-9be2-4bc7-96b5-83e321188a69
DOI
https://doi.org/10.1109/ivs.2016.7535544
TNO identifier
572372
Publisher
Institute of Electrical and Electronics Engineers Inc.
ISBN
9781509018215
Source
2016 IEEE Intelligent Vehicles Symposium, IV 2016, 19 June 2016 through 22 June 2016, 1211-1216
Article number
7535544
Document type
conference paper