Design and Implementation of a Fire Detection andControl System with Enhanced Security and Safety for Automobiles Using Neuro-Fuzzy Logic

Autor: George Osae, Benedict Agyarkwa, Robert A. Sowah, Koudjo M. Koumadi, Gilbert Nortey, Kwaku O. Apeadu, Amewugah M. Bempong, Abdul Ofoli
Rok vydání: 2018
Předmět:
Zdroj: 2018 IEEE 7th International Conference on Adaptive Science & Technology (ICAST).
Popis: Automobiles provide comfort and mobility to owners.While they make life more meaningful they also pose challenges and risks intheir safety and security mechanisms. Some modern automobiles are equippedwith anti-theft systems and enhanced safety measures to safeguard itsdrivers.But at times, these mechanisms for safety and secured operation ofautomobiles are insufficientdueto various mechanisms used by intruders andcar thieves to defeat them. Drunk drivers cause accidents on our roads andthus the need to safeguard the driver when he is intoxicated and renderthecar to be incapable of being driven. These issues merit an integratedapproach to safety andsecurity of automobiles. In the light of thesechallenges, an integrated microcontroller-based hardware and software systemfor safety and security of automobiles to be fixed into existing vehiclearchitecture, was designed, developed and deployed. The system submodulesare: (1) Two-step ignition for automobiles, namely: (a) biometric ignitionand (b) alcohol detection with engine control, (2) Global Positioning System(GPS) based vehicle tracking and (3) Multisensor-based fire detection usingneuro-fuzzy logic. All submodules of the system were implemented using onemicrocontroller, the Arduino Mega 2560, as the central control unit. Themicrocontroller was programmed using C++11.The developed systemperformed quite well with the tests performed on it. Given the rightconditions, the alcohol detection subsystem operated with a 92%efficiency. The biometric ignitionsubsystem operated with about 80% efficiency. The fire detection subsystem operated with a 95% efficiencyin locations registered with the neuro-fuzzy system. The vehicle trackingsubsystem operated with an efficiency of 90%.
Databáze: OpenAIRE