학술논문

Research on Safety Classification for Vehicle Anti-collision Data by Improved Interval Fuzzy Reasoning
Document Type
Conference
Source
2021 3rd International Conference on Robotics and Computer Vision (ICRCV) Robotics and Computer Vision (ICRCV), 2021 3rd International Conference on. :82-85 Aug, 2021
Subject
Robotics and Control Systems
Computer vision
Computational modeling
Fuzzy set theory
Cognition
Safety
Automobiles
Fuzzy reasoning
Safety data classification
Interval-valued reasoning
Fuzzy classifier
Language
Abstract
The automobile anti-collision safety system can reduce the traffic accident rate and maintain traffic safety. Research of it is very practical and important. This paper introduces a new safety data classification method. A new weighted interval value matching classification method was proposed based on interval-valued fuzzy set theory. The weights of each feature are determined by the relationship between classes and inside classes. The matching function is achieved by common interval intersection operations. At last, simulation is given to illustrate the effectiveness. The new fuzzy classifier can be applied to safety data classification for vehicle crash avoidance.