A two-stage-training support vector machine approach to predicting unintentional vehicle lane departure. Issue 1 (2nd January 2017)

• Record Type: Journal Article
• Authors: Albousefi, Alhadi Ali; Ying, Hao; Filev, Dimitar; Syed, Fazal; Prakah-Asante, Kwaku O.; Tseng, Finn; Yang, Hsin-Hsiang
• Is Part Of: Journal of intelligent transportation systems. Volume 21:Issue 1(2017)
• Page Start: 41
• Page End: 51
• Publication Date: 2017-01-02
• Subjects: prediction -- support vector machines -- unintentional lane departure; Intelligent transportation systems -- Periodicals; Transportation -- Technological innovations -- Periodicals; 388.312
• DOI: 10.1080/15472450.2016.1196141
• Languages: English
• ISSNs: 1547-2450

Abstract

ABSTRACT: Advanced driver assistance systems, such as unintentional lane departure warning systems, have recently drawn much attention and efforts. In this study, we explored utilizing the nonlinear binary support vector machine (SVM) technique to predict unintentional lane departure, which is innovative, as the SVM methodology has not previously been attempted for this purpose in the literature. Furthermore, we developed a two-stage training scheme to improve SVM's prediction performance in terms of minimization of the number of false positive prediction errors. Experiment data generated by VIRTTEX, a hydraulically powered, 6-degrees-of-freedom moving base driving simulator at Ford Motor Company, were used. All the vehicle variables were sampled at 50 Hz and there were 16 drowsy drivers (about 3 hours of driving per subject) and six control drivers (approximately 20 minutes f driving each). In total, 3, 508 unintentional lane departures occurred for the drowsy drivers and 23 for the control drivers. Our study involving these 22 drivers with a total of more than 7.5 million prediction decisions demonstrates that (a) excellent SVM prediction performance, measured by numbers of false positives (i.e., falsely predicted lane departures) and false negatives (i.e., lane departures failed to be predicted), was achieved when the prediction horizon was 0.6 seconds or less, (b) lateral position and lateral velocity worked the best as SVM input variables among the nine variable sets that we explored, and (c) the radial basis function performed the best as the SVM kernel function.