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RESEARCH PRODUCT

Fuzzy logic approach to predict vehicle crash severity from acceleration data

Bernard B. MunyazikwiyeHamid Reza KarimiKjell G. Robbersmyr

subject

Control and OptimizationComputer scienceSIGNAL (programming language)CrashAccelerometerCollisionFuzzy logicFuzzy logic; Jerk and Kinetic energy; vehicle crash severity; Artificial Intelligence; Control and Optimization; Discrete Mathematics and CombinatoricsFuzzy logicJerk and Kinetic energyAccelerationVariable (computer science)JerkArtificial IntelligenceDiscrete Mathematics and CombinatoricsSimulationvehicle crash severity

description

Vehicle crash is a complex behavior to be investigated as a challenging topic in terms of dynamical modeling. On this aim, fuzzy logic can be utilized to analyze the crash dynamics rapidly and simply. In this paper, the experimental data of the frontal crash is recorded using an accelerometer located at the centre of the gravity of the vehicle. The acceleration signal was the raw data from which the collision intensity expressed by the kinetic energy and the jerk were derived. The fuzzy logic model was then developed from the two inputs namely kinetic energy and jerk. The output variable is the crash severity expressed as the dynamic crash. The result shows that the jerk contributes much to the crash than the kinetic energy of the vehicle.

yearjournalcountryeditionlanguage
2015-11-012015 International Conference on Fuzzy Theory and Its Applications (iFUZZY)
https://doi.org/10.1109/ifuzzy.2015.7391892