Dangerous driving event prediction on expressways using fuzzy attributed map matching

Chiung-Yao Fang, Bo Yan Wu, Jung Ming Wang, Sei-Wang Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

This paper presents a system for predicting dangerous driving events while driving on an expressway. There are three major tasks involved in the prediction system: (1) how to perceive driving events on the input sequence of driving conditions, (2) how to represent driving events, and (3) how to interpret driving events to decide whether or not they are hazardous. A directed acyclic graph, called the attributed driving relational map (ADRM), is introduced to represent driving events. The ADRM chronicles a driving event in terms of driving conditions. The prediction system evaluates the driving event to determine whether it is perilous or not by matching its ADRM against those of known dangerous driving events preserved in a database using a fuzzy attributed map matching technique. The database can automatically augment by including new dangerous driving events that approved any of the predefined danger criteria. A series of experiments with synthetic examples generated by a driving simulator have been conducted to demonstrate the feasibility and rationality of the proposed system.

Original languageEnglish
Title of host publication2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Pages2718-2723
Number of pages6
DOIs
Publication statusPublished - 2010 Nov 15
Event2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 - Qingdao, China
Duration: 2010 Jul 112010 Jul 14

Publication series

Name2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Volume5

Other

Other2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
CountryChina
CityQingdao
Period10/7/1110/7/14

Fingerprint

Simulators
Experiments

Keywords

  • Activation mechanism
  • Attributed driving relational map
  • Dangerous driving event prediction system
  • Driving safety assistance system
  • Fuzzy attributed map matching

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computational Theory and Mathematics
  • Human-Computer Interaction

Cite this

Fang, C-Y., Wu, B. Y., Wang, J. M., & Chen, S-W. (2010). Dangerous driving event prediction on expressways using fuzzy attributed map matching. In 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010 (pp. 2718-2723). [5580474] (2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010; Vol. 5). https://doi.org/10.1109/ICMLC.2010.5580474

Dangerous driving event prediction on expressways using fuzzy attributed map matching. / Fang, Chiung-Yao; Wu, Bo Yan; Wang, Jung Ming; Chen, Sei-Wang.

2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. 2010. p. 2718-2723 5580474 (2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010; Vol. 5).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Fang, C-Y, Wu, BY, Wang, JM & Chen, S-W 2010, Dangerous driving event prediction on expressways using fuzzy attributed map matching. in 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010., 5580474, 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, vol. 5, pp. 2718-2723, 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010, Qingdao, China, 10/7/11. https://doi.org/10.1109/ICMLC.2010.5580474
Fang C-Y, Wu BY, Wang JM, Chen S-W. Dangerous driving event prediction on expressways using fuzzy attributed map matching. In 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. 2010. p. 2718-2723. 5580474. (2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010). https://doi.org/10.1109/ICMLC.2010.5580474
Fang, Chiung-Yao ; Wu, Bo Yan ; Wang, Jung Ming ; Chen, Sei-Wang. / Dangerous driving event prediction on expressways using fuzzy attributed map matching. 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010. 2010. pp. 2718-2723 (2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010).
@inproceedings{67a7520ddd44485887ce0a44d5bc5c21,
title = "Dangerous driving event prediction on expressways using fuzzy attributed map matching",
abstract = "This paper presents a system for predicting dangerous driving events while driving on an expressway. There are three major tasks involved in the prediction system: (1) how to perceive driving events on the input sequence of driving conditions, (2) how to represent driving events, and (3) how to interpret driving events to decide whether or not they are hazardous. A directed acyclic graph, called the attributed driving relational map (ADRM), is introduced to represent driving events. The ADRM chronicles a driving event in terms of driving conditions. The prediction system evaluates the driving event to determine whether it is perilous or not by matching its ADRM against those of known dangerous driving events preserved in a database using a fuzzy attributed map matching technique. The database can automatically augment by including new dangerous driving events that approved any of the predefined danger criteria. A series of experiments with synthetic examples generated by a driving simulator have been conducted to demonstrate the feasibility and rationality of the proposed system.",
keywords = "Activation mechanism, Attributed driving relational map, Dangerous driving event prediction system, Driving safety assistance system, Fuzzy attributed map matching",
author = "Chiung-Yao Fang and Wu, {Bo Yan} and Wang, {Jung Ming} and Sei-Wang Chen",
year = "2010",
month = "11",
day = "15",
doi = "10.1109/ICMLC.2010.5580474",
language = "English",
isbn = "9781424465262",
series = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",
pages = "2718--2723",
booktitle = "2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010",

}

TY - GEN

T1 - Dangerous driving event prediction on expressways using fuzzy attributed map matching

AU - Fang, Chiung-Yao

AU - Wu, Bo Yan

AU - Wang, Jung Ming

AU - Chen, Sei-Wang

PY - 2010/11/15

Y1 - 2010/11/15

N2 - This paper presents a system for predicting dangerous driving events while driving on an expressway. There are three major tasks involved in the prediction system: (1) how to perceive driving events on the input sequence of driving conditions, (2) how to represent driving events, and (3) how to interpret driving events to decide whether or not they are hazardous. A directed acyclic graph, called the attributed driving relational map (ADRM), is introduced to represent driving events. The ADRM chronicles a driving event in terms of driving conditions. The prediction system evaluates the driving event to determine whether it is perilous or not by matching its ADRM against those of known dangerous driving events preserved in a database using a fuzzy attributed map matching technique. The database can automatically augment by including new dangerous driving events that approved any of the predefined danger criteria. A series of experiments with synthetic examples generated by a driving simulator have been conducted to demonstrate the feasibility and rationality of the proposed system.

AB - This paper presents a system for predicting dangerous driving events while driving on an expressway. There are three major tasks involved in the prediction system: (1) how to perceive driving events on the input sequence of driving conditions, (2) how to represent driving events, and (3) how to interpret driving events to decide whether or not they are hazardous. A directed acyclic graph, called the attributed driving relational map (ADRM), is introduced to represent driving events. The ADRM chronicles a driving event in terms of driving conditions. The prediction system evaluates the driving event to determine whether it is perilous or not by matching its ADRM against those of known dangerous driving events preserved in a database using a fuzzy attributed map matching technique. The database can automatically augment by including new dangerous driving events that approved any of the predefined danger criteria. A series of experiments with synthetic examples generated by a driving simulator have been conducted to demonstrate the feasibility and rationality of the proposed system.

KW - Activation mechanism

KW - Attributed driving relational map

KW - Dangerous driving event prediction system

KW - Driving safety assistance system

KW - Fuzzy attributed map matching

UR - http://www.scopus.com/inward/record.url?scp=78149294745&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78149294745&partnerID=8YFLogxK

U2 - 10.1109/ICMLC.2010.5580474

DO - 10.1109/ICMLC.2010.5580474

M3 - Conference contribution

AN - SCOPUS:78149294745

SN - 9781424465262

T3 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

SP - 2718

EP - 2723

BT - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010

ER -