Driving style identification based on typical working conditions and GMM algorithm
To improve the utilization rate of driving style information in vehicle control strategies,fuel economy,and driving safety,K-Means algorithm is applied to extract typical driving conditions based on a large amount of vehicle driving data.Gaussian mixture model(GMM)is used to recognize driving style,and contour coefficients are used to evaluate the recognition effect of GMM algorithm based on typical driving conditions and ordinary GMM algorithm.The results show that using the K-Means algorithm,three parameters including mean vehicle speed,standard deviation of vehicle speed,and idle ratio can effectively extract three typical working conditions:congestion condition,urban area condition,and high-speed condition.The GMM algorithm designed based on typical working conditions can distinguish driving styles into three categories:robust,normal,and aggressive.The differentiation of different driving styles is good,and the recognition effect is better than that of ordinary GMM algorithms.
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