A collision avoidance system with fuzzy danger level detection

Zihao Wang; Saina Ramyar; Syed Moshfeq Salaken; Abdollah Homaifar; Saeid Nahavandi; Ali Karimoddini

doi:10.1109/IVS.2017.7995733

A collision avoidance system with fuzzy danger level detection

Zihao Wang, Saina Ramyar, Syed Moshfeq Salaken, Abdollah Homaifar, Saeid Nahavandi, Ali Karimoddini

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

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.

Original language	English
Title of host publication	IV 2017 - 28th IEEE Intelligent Vehicles Symposium
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	283-288
Number of pages	6
ISBN (Electronic)	9781509048045
DOIs	https://doi.org/10.1109/IVS.2017.7995733
State	Published - Jul 28 2017
Externally published	Yes
Event	28th IEEE Intelligent Vehicles Symposium, IV 2017 - Redondo Beach, United States Duration: Jun 11 2017 → Jun 14 2017

Publication series

Name	IEEE Intelligent Vehicles Symposium, Proceedings

Conference

Conference	28th IEEE Intelligent Vehicles Symposium, IV 2017
Country/Territory	United States
City	Redondo Beach
Period	06/11/17 → 06/14/17

Access to Document

10.1109/IVS.2017.7995733

Cite this

Wang, Z., Ramyar, S., Salaken, S. M., Homaifar, A., Nahavandi, S., & Karimoddini, A. (2017). A collision avoidance system with fuzzy danger level detection. In IV 2017 - 28th IEEE Intelligent Vehicles Symposium (pp. 283-288). Article 7995733 (IEEE Intelligent Vehicles Symposium, Proceedings). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/IVS.2017.7995733

@inproceedings{bc8b00b095944ab2b4ffb3e1d39b693a,

title = "A collision avoidance system with fuzzy danger level detection",

abstract = "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.",

author = "Zihao Wang and Saina Ramyar and Salaken, \{Syed Moshfeq\} and Abdollah Homaifar and Saeid Nahavandi and Ali Karimoddini",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 28th IEEE Intelligent Vehicles Symposium, IV 2017 ; Conference date: 11-06-2017 Through 14-06-2017",

year = "2017",

month = jul,

day = "28",

doi = "10.1109/IVS.2017.7995733",

language = "English",

series = "IEEE Intelligent Vehicles Symposium, Proceedings",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

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}

Wang, Z, Ramyar, S, Salaken, SM, Homaifar, A, Nahavandi, S & Karimoddini, A 2017, A collision avoidance system with fuzzy danger level detection. in IV 2017 - 28th IEEE Intelligent Vehicles Symposium., 7995733, IEEE Intelligent Vehicles Symposium, Proceedings, Institute of Electrical and Electronics Engineers Inc., pp. 283-288, 28th IEEE Intelligent Vehicles Symposium, IV 2017, Redondo Beach, United States, 06/11/17. https://doi.org/10.1109/IVS.2017.7995733

A collision avoidance system with fuzzy danger level detection. / Wang, Zihao; Ramyar, Saina; Salaken, Syed Moshfeq et al.
IV 2017 - 28th IEEE Intelligent Vehicles Symposium. Institute of Electrical and Electronics Engineers Inc., 2017. p. 283-288 7995733 (IEEE Intelligent Vehicles Symposium, Proceedings).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - A collision avoidance system with fuzzy danger level detection

AU - Wang, Zihao

AU - Ramyar, Saina

AU - Salaken, Syed Moshfeq

AU - Homaifar, Abdollah

AU - Nahavandi, Saeid

AU - Karimoddini, Ali

PY - 2017/7/28

Y1 - 2017/7/28

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85028056943

U2 - 10.1109/IVS.2017.7995733

DO - 10.1109/IVS.2017.7995733

M3 - Conference contribution

T3 - IEEE Intelligent Vehicles Symposium, Proceedings

SP - 283

EP - 288

BT - IV 2017 - 28th IEEE Intelligent Vehicles Symposium

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 28th IEEE Intelligent Vehicles Symposium, IV 2017

Y2 - 11 June 2017 through 14 June 2017

ER -

A collision avoidance system with fuzzy danger level detection

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