Research and evaluation on comprehensive obstacle-avoiding behavior for unmanned vehicles based on the interval neutrosophic number multiple attribute group decision making
Affiliations: College of Information, Guangdong Communication Polytechnic, Guangdong, P. R. China
Correspondence:
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Corresponding author. Feng Li, College of Information, Guangdong Communication Polytechnic, Guangdong, 510630, P. R. China. E-mail: elsalee@gdcp.edu.cn.
Abstract: With the advent of the information age, the development direction of automobiles has gradually changed, both from the domestic and foreign policy support attitude, or from the actual actions of the automotive industry and scientific research institutes’ continuous efforts, it is not difficult to see that driverless vehicle. At this time, the testing and evaluation of the intelligent behavior of driverless vehicles is particularly important. It is particularly important not only to regulate the intelligent behavior of unmanned vehicles, but also to promote the key It can not only regulate the intelligent behavior of unmanned vehicles, but also promote the improvement of key technologies of unmanned vehicles and the research and development of driver assistance systems. The evaluation of comprehensive obstacle-avoiding behavior for unmanned vehicles is often considered as a multi-attribute group decision making (MAGDM) problem. In this paper, the EDAS method is extended to the interval neutrosophic sets (INSs) setting to deal with MAGDM and the computational steps for all designs are listed. Then, the criteria importance through intercriteria correlation (CRITIC) is defined to obtain the attribute’s weight. Finally, the evaluation of comprehensive obstacle-avoiding behavior for unmanned vehicles is given to demonstrate the interval neutrosophic number EDAS (INN-EDAS) model and some good comparative analysis is done to demonstrate the advantages of INN-EDAS.
Keywords: Multi-attribute group decision making (MAGDM), interval neutrosophic sets (INSs), EDAS method, comprehensive obstacle-avoiding behavior, unmanned vehicles
