Applying embedded hybrid ANFIS/quantum-tuned BPNN prediction to collision warning system for motor vehicle safety

Bao Rong Chang; Chung Ping Young; Hsiu Fen Tsai; Jian Jr Lin

doi:10.1109/ISDA.2008.271

Applying embedded hybrid ANFIS/quantum-tuned BPNN prediction to collision warning system for motor vehicle safety

Bao Rong Chang
, Chung Ping Young
, Hsiu Fen Tsai
, Jian Jr Lin

Institute of Medical Informatics

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

7 Citations (Scopus)

Abstract

This study is to explore how to realize highperformance collision warning system (CWS), providing the precaution against traffic crash in transit. An embedded hybrid adaptive network-based fuzzy inference system (ANFIS) plus quantum-tuned back-propagation neural network (QT-BPNN) built in the platform with Davinci+XScale-NAV270 was employed to realize collision warning system and we also installed motor vehicle event data recorder (MVEDR). Finally, experiments and verification of the proposed approach were successfully done to achieve better accuracy and more effectiveness on warning level and event data record to motor vehicle.

Original language	English
Title of host publication	Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008
Pages	3-8
Number of pages	6
DOIs	https://doi.org/10.1109/ISDA.2008.271
Publication status	Published - 2008
Event	8th International Conference on Intelligent Systems Design and Applications, ISDA 2008 - Kaohsiung, Taiwan Duration: 2008 Nov 26 → 2008 Nov 28

Publication series

Name	Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008
Volume	1

Other

Other	8th International Conference on Intelligent Systems Design and Applications, ISDA 2008
Country/Territory	Taiwan
City	Kaohsiung
Period	08-11-26 → 08-11-28

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

All Science Journal Classification (ASJC) codes

Artificial Intelligence
Control and Systems Engineering

Access to Document

10.1109/ISDA.2008.271

Cite this

Chang, B. R., Young, C. P., Tsai, H. F., & Lin, J. J. (2008). Applying embedded hybrid ANFIS/quantum-tuned BPNN prediction to collision warning system for motor vehicle safety. In Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008 (pp. 3-8). Article 4696167 (Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008; Vol. 1). https://doi.org/10.1109/ISDA.2008.271

Chang, Bao Rong ; Young, Chung Ping ; Tsai, Hsiu Fen et al. / Applying embedded hybrid ANFIS/quantum-tuned BPNN prediction to collision warning system for motor vehicle safety. Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008. 2008. pp. 3-8 (Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008).

@inproceedings{66b5a0ad0df34bf3a1ad77740ea66a99,

title = "Applying embedded hybrid ANFIS/quantum-tuned BPNN prediction to collision warning system for motor vehicle safety",

abstract = "This study is to explore how to realize highperformance collision warning system (CWS), providing the precaution against traffic crash in transit. An embedded hybrid adaptive network-based fuzzy inference system (ANFIS) plus quantum-tuned back-propagation neural network (QT-BPNN) built in the platform with Davinci+XScale-NAV270 was employed to realize collision warning system and we also installed motor vehicle event data recorder (MVEDR). Finally, experiments and verification of the proposed approach were successfully done to achieve better accuracy and more effectiveness on warning level and event data record to motor vehicle.",

author = "Chang, \{Bao Rong\} and Young, \{Chung Ping\} and Tsai, \{Hsiu Fen\} and Lin, \{Jian Jr\}",

year = "2008",

doi = "10.1109/ISDA.2008.271",

language = "English",

isbn = "9780769533827",

series = "Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008",

pages = "3--8",

booktitle = "Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008",

note = "8th International Conference on Intelligent Systems Design and Applications, ISDA 2008 ; Conference date: 26-11-2008 Through 28-11-2008",

}

Chang, BR, Young, CP, Tsai, HF & Lin, JJ 2008, Applying embedded hybrid ANFIS/quantum-tuned BPNN prediction to collision warning system for motor vehicle safety. in Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008., 4696167, Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008, vol. 1, pp. 3-8, 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008, Kaohsiung, Taiwan, 08-11-26. https://doi.org/10.1109/ISDA.2008.271

Applying embedded hybrid ANFIS/quantum-tuned BPNN prediction to collision warning system for motor vehicle safety. / Chang, Bao Rong; Young, Chung Ping; Tsai, Hsiu Fen et al.
Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008. 2008. p. 3-8 4696167 (Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008; Vol. 1).

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

TY - GEN

T1 - Applying embedded hybrid ANFIS/quantum-tuned BPNN prediction to collision warning system for motor vehicle safety

AU - Chang, Bao Rong

AU - Young, Chung Ping

AU - Tsai, Hsiu Fen

AU - Lin, Jian Jr

PY - 2008

Y1 - 2008

N2 - This study is to explore how to realize highperformance collision warning system (CWS), providing the precaution against traffic crash in transit. An embedded hybrid adaptive network-based fuzzy inference system (ANFIS) plus quantum-tuned back-propagation neural network (QT-BPNN) built in the platform with Davinci+XScale-NAV270 was employed to realize collision warning system and we also installed motor vehicle event data recorder (MVEDR). Finally, experiments and verification of the proposed approach were successfully done to achieve better accuracy and more effectiveness on warning level and event data record to motor vehicle.

AB - This study is to explore how to realize highperformance collision warning system (CWS), providing the precaution against traffic crash in transit. An embedded hybrid adaptive network-based fuzzy inference system (ANFIS) plus quantum-tuned back-propagation neural network (QT-BPNN) built in the platform with Davinci+XScale-NAV270 was employed to realize collision warning system and we also installed motor vehicle event data recorder (MVEDR). Finally, experiments and verification of the proposed approach were successfully done to achieve better accuracy and more effectiveness on warning level and event data record to motor vehicle.

UR - https://www.scopus.com/pages/publications/67449119459

UR - https://www.scopus.com/pages/publications/67449119459#tab=citedBy

U2 - 10.1109/ISDA.2008.271

DO - 10.1109/ISDA.2008.271

M3 - Conference contribution

AN - SCOPUS:67449119459

SN - 9780769533827

T3 - Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008

SP - 3

EP - 8

BT - Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008

T2 - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008

Y2 - 26 November 2008 through 28 November 2008

ER -

Chang BR, Young CP, Tsai HF, Lin JJ. Applying embedded hybrid ANFIS/quantum-tuned BPNN prediction to collision warning system for motor vehicle safety. In Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008. 2008. p. 3-8. 4696167. (Proceedings - 8th International Conference on Intelligent Systems Design and Applications, ISDA 2008). doi: 10.1109/ISDA.2008.271

Applying embedded hybrid ANFIS/quantum-tuned BPNN prediction to collision warning system for motor vehicle safety

Abstract

Publication series

Other

UN SDGs

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this