Print Email Facebook Twitter Classification for Safety-Critical Car-Cyclist Scenarios Using Machine Learning Title Classification for Safety-Critical Car-Cyclist Scenarios Using Machine Learning Author Cara, I. Gelder, E.D. Publication year 2015 Abstract The number of fatal car-cyclist accidents is increasing. Advanced Driver Assistance Systems (ADAS) can improve the safety of cyclists, but they need to be tested with realistic safety-critical car-cyclist scenarios. In order to store only relevant scenarios, an online classification algorithm is needed. We demonstrate that machine learning techniques can be used to detect and classify those scenarios based on their trajectory data. A dataset consisting of 99 realistic car-cyclist scenarios is gathered using an instrumented vehicle. We achieved a classification accuracy of the gathered data of 87.9%. The execution time of only 45.8 us shows that the algorithm is suitable for online purposes. cop. 2015 IEEE. Subject Fluid & Solid MechanicsIVS - Integrated Vehicle SafetyTS - Technical SciencesTrafficMobilityAccidentsArtificial intelligenceSafety engineeringMachine learning techniquesAdvanced driver assistance systems To reference this document use: http://resolver.tudelft.nl/uuid:63e71cd8-c8bf-429d-bf3b-6ff0b4047575 DOI https://doi.org/10.1109/itsc.2015.323 TNO identifier 530897 Publisher Institute of Electrical and Electronics Engineers Inc. ISBN 9781467365956 Source IEEE Conference on Intelligent Transportation Systems, Proceedings, ITSC, 2015-October, 1995-2000 Article number 7313415 Document type conference paper Files To receive the publication files, please send an e-mail request to TNO Library.