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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 - 288

Publisher

IEEE

Location

Redondo Beach, California

Place of publication

Piscataway, N.J.

Start date

2017-06-11

End date

2017-06-14

ISBN-13

9781509048045

Language

eng

Publication classification

E1 Full written paper - refereed

Copyright notice

2017, IEEE

Title of proceedings

Proceedings of the 2017 IEEE Intelligent Vehicles Symposium

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