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Driver behaviour prediction for motion simulators using changepoint segmentation
conference contribution
posted on 2015-01-01, 00:00 authored by Mohammed Hossny, Shady MohamedShady Mohamed, Saeid NahavandiDriving phenomenon is a repetitive process, that permits sequential learning under identifying the proper change periods. Sequential filtering is widely used for tracking and prediction of state dynamics. However, it suffers at abrupt changes, which cause sudden incremental prediction error. We provide a sequential filtering approach using online Bayesian detection of change points to decrease prediction error generally, and specifically at abrupt changes. The approach learns from optimally detected segments for identifying driving behaviour. Change points detection is done by the Pruned Exact Linear Time algorithm. Computational cost of our approach is bounded by the cost of the implemented sequential filter. This computational performance is suitable to the online nature of motion simulator's delay reduction. The approach was tested on a simulated driving scenario using Vortex by CM Labs. The state dimensions are simulated 2D space coordinates, and velocity. Particle filter was used for online sequential filtering. Prediction results show that change-point detection improves the quality of state estimation compared to traditional sequential filters, and is more suitable for predicting behavioural activities.
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IEEE International Conference on Systems, Man, and Cybernetics (2015 : Hong Kong, China)Pagination
457 - 462Publisher
IEEELocation
Hong Kong, ChinaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2015-10-09End date
2015-10-12ISSN
1062-922XISBN-13
9781479986965Language
engPublication classification
E Conference publication; E1 Full written paper - refereedCopyright notice
2015, IEEETitle of proceedings
SMC 2015 : Big Data Analytics for Human-Centric Systems. Proceedings of the 2015 IEEE International Conference on Systems, Man, and CyberneticsUsage metrics
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