Dangerous prediction by case-based approach on expressways

C. Y. Fang; P. Y. Wu; S. L. Chang; S. W. Chen

doi:10.1109/ITSC.2008.4732684

Dangerous prediction by case-based approach on expressways

C. Y. Fang, P. Y. Wu, S. L. Chang, S. W. Chen

Department of Computer Science and Information Engineering

Research output: Contribution to conference › Paper › peer-review

2 Citations (Scopus)

Abstract

The purpose of this system is to reduce the rate of dangerous events caused by various driving factors. We compose a driving relational map as the system inputs factors of driver behavior, nearby vehicles and roadway factors, and then put this driving relational map into the matching process with dangerous driving relational maps. If the similarity between the driving relational map and one of the dangerous driving relational maps is high, a dangerous event may occur. At this time the system will warn the driver to watch out for this dangerous event. Along with the learning process based on case-base reasoning, the system will become a flawless danger prediction system.

Original language	English
Pages	1118-1123
Number of pages	6
DOIs	https://doi.org/10.1109/ITSC.2008.4732684
Publication status	Published - 2008
Event	11th International IEEE Conference on Intelligent Transportation Systems, ITSC 2008 - Beijing, China Duration: 2008 Dec 10 → 2008 Dec 12

Other

Other	11th International IEEE Conference on Intelligent Transportation Systems, ITSC 2008
Country/Territory	China
City	Beijing
Period	2008/12/10 → 2008/12/12

ASJC Scopus subject areas

Automotive Engineering
Mechanical Engineering
Computer Science Applications

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10.1109/ITSC.2008.4732684

Cite this

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title = "Dangerous prediction by case-based approach on expressways",

abstract = "The purpose of this system is to reduce the rate of dangerous events caused by various driving factors. We compose a driving relational map as the system inputs factors of driver behavior, nearby vehicles and roadway factors, and then put this driving relational map into the matching process with dangerous driving relational maps. If the similarity between the driving relational map and one of the dangerous driving relational maps is high, a dangerous event may occur. At this time the system will warn the driver to watch out for this dangerous event. Along with the learning process based on case-base reasoning, the system will become a flawless danger prediction system.",

author = "Fang, {C. Y.} and Wu, {P. Y.} and Chang, {S. L.} and Chen, {S. W.}",

year = "2008",

doi = "10.1109/ITSC.2008.4732684",

language = "English",

pages = "1118--1123",

note = "11th International IEEE Conference on Intelligent Transportation Systems, ITSC 2008 ; Conference date: 10-12-2008 Through 12-12-2008",

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AU - Wu, P. Y.

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N2 - The purpose of this system is to reduce the rate of dangerous events caused by various driving factors. We compose a driving relational map as the system inputs factors of driver behavior, nearby vehicles and roadway factors, and then put this driving relational map into the matching process with dangerous driving relational maps. If the similarity between the driving relational map and one of the dangerous driving relational maps is high, a dangerous event may occur. At this time the system will warn the driver to watch out for this dangerous event. Along with the learning process based on case-base reasoning, the system will become a flawless danger prediction system.

AB - The purpose of this system is to reduce the rate of dangerous events caused by various driving factors. We compose a driving relational map as the system inputs factors of driver behavior, nearby vehicles and roadway factors, and then put this driving relational map into the matching process with dangerous driving relational maps. If the similarity between the driving relational map and one of the dangerous driving relational maps is high, a dangerous event may occur. At this time the system will warn the driver to watch out for this dangerous event. Along with the learning process based on case-base reasoning, the system will become a flawless danger prediction system.

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T2 - 11th International IEEE Conference on Intelligent Transportation Systems, ITSC 2008

Y2 - 10 December 2008 through 12 December 2008

ER -

Dangerous prediction by case-based approach on expressways

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