Research on the Method of Reducing the False Alarm Rate of LDWS
MENG Ni
SHAN Yan
Abstract：For lane departure warning system(LDWS)cannot effectively distinguish between lane and unconscious lane de?parture problem,the data of vehicle lane changing and lane changing are obtained by real vehicle experiment,the steering wheel an?gle,the lateral velocity and the transverse distance are extracted as the identification parameters of the model,and the extended Kal?man filter,normalized and K mean clustering method are used to process the data. Support vector machine(SVM)is used to identi?fy lane departure behavior. In order to improve the recognition rate of SVM,particle swarm optimization(PSO)algorithm is used to optimize the SVM parameters. The results show that the recognition rate of the optimized model is more than 88% when the time win?dow is 3.5s,which can meet the application requirements of LDWS system.
Keywords：lane departureKalman filterSVMPSO
Publication Date：2019-01-01
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：5( 1930-1934 )