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
Y1 - 2010
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
T2 - 2010 International Conference on Machine Learning and Cybernetics, ICMLC 2010
Y2 - 11 July 2010 through 14 July 2010
ER -