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A collision avoidance system with fuzzy danger level detection
conference contribution
posted on 2017-07-28, 00:00 authored by Z Wang, S Ramyar, Syed Moshfeq Salaken, A Homaifar, Saeid Nahavandi, A Karimoddini© 2017 IEEE. Collision avoidance is an essential component in advanced driving assistance systems, as it ensures the safety of the vehicle in near crash or crash scenarios. In this study, a collision avoidance system for lane change events is proposed which plans the trajectory based on the level of danger. The danger level is computed by a fuzzy inference system developed with naturalistic driving data to better capture the real-world factors, which may cause an accident. In addition, a fault determination classifier is introduced in order to determine the responsible driver in a near crash event. This system is evaluated on simulated naturalistic near crash events and the results demonstrate good performance of the proposed system.
History
Event
Intelligent Vehicles. Symposium (2017 : IV : Redondo Beach, California)Pagination
283 - 288Publisher
IEEELocation
Redondo Beach, CaliforniaPlace of publication
Piscataway, N.J.Publisher DOI
Start date
2017-06-11End date
2017-06-14ISBN-13
9781509048045Language
engPublication classification
E1 Full written paper - refereedCopyright notice
2017, IEEETitle of proceedings
Proceedings of the 2017 IEEE Intelligent Vehicles SymposiumUsage metrics
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