Non-linear intelligent fuzzy decision-making system for blind spot estimation
| Autor: | M. Suresh, Tammineedi Venkata Satya Vivek, Yalla Venkat, Mohan Chokkalingam |
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| Rok vydání: | 2023 |
| Předmět: | |
| Zdroj: | Journal of Intelligent & Fuzzy Systems. 44:139-148 |
| ISSN: | 1875-8967 1064-1246 |
| DOI: | 10.3233/jifs-213426 |
| Popis: | The lack of awareness of blind spots in vehicle transport results in more deaths nowadays. To address this issue, the multi-obstacle detection and measurement of the depth of the nearing vehicle, height, and width is necessary. In recent years, Fuzzy logic is being used to access smart decision-making for control actions. To handle the specific task efficiently, ambiguous and imprecise linguistic data is required. In this context, a non-linear intelligent fuzzy decision-making system has been proposed to estimate blind spots. An inference engine, a defuzzification interface to identify the blind spot both day and night, and a fuzzy rule-base are included. Shadows and edges can be used as linguistic parameters to identify vehicles in the daytime. The lamps are elevated higher than the air dams to avoid casting a shadow under the car at night. One in-sourcing vehicle and three out-sourcing vehicles are tested to determine the driver’s blind spot and a more comfortable driver’s seat and a rear-view mirror using the proposed system. A fuzzy matrix with a triangular number obtained from the crisp matrix is used to alert the driver of the likelihood of a collision using LEDs or buzzers. |
| Databáze: | OpenAIRE |
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