Dangerous Prediction by Case-Based Approach on Expressways

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.

[1]  Juan M. Corchado,et al.  Hybrid artificial intelligence methods in oceanographic forecast models , 2002, IEEE Trans. Syst. Man Cybern. Part C.

[2]  Edwin R. Hancock,et al.  Graph Matching With a Dual-Step EM Algorithm , 1998, IEEE Trans. Pattern Anal. Mach. Intell..

[3]  Sei-Wang Chen,et al.  Attributed concept maps: fuzzy integration and fuzzy matching , 2001, IEEE Trans. Syst. Man Cybern. Part B.

[4]  Liam Maguire,et al.  Fault diagnosis of electronic system using artificial intelligence , 2002 .

[5]  Swarup Medasani,et al.  Content-based image retrieval based on a fuzzy approach , 2004, IEEE Transactions on Knowledge and Data Engineering.

[6]  T. Martin McGinnity,et al.  Fault diagnosis of electronic systems using intelligent techniques: a review , 2001, IEEE Trans. Syst. Man Cybern. Part C.