This paper presents the design of a crash detection system based on the principles of continuous-mode Hidden Markov Models (HMM) with real-valued emission parameters. Our design utilizes log-likelihood for optimizing HMM parameters including the number of states in the model and the accelerometer crash-pulse buffer size resulting in lower costs and complexity of the crash detection system. Cross validation technique based on Jackknifing is utilized to estimate the crash pulse detection rate for a variety of crash events involving rigid as well as offset deformable barriers with head-on and oblique angle impacts. The system is simulated using Matlab and Simulink, and the proposed model is able to accurately classify crash-events within 10 ms from the time of the impact.


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    Titel :

    Crash Detection System Using Hidden Markov Models


    Weitere Titelangaben:

    Sae Technical Papers


    Beteiligte:
    Chou, Clifford C. (Autor:in) / Singh, Gautam B. (Autor:in) / Song, Haiping (Autor:in)

    Kongress:

    SAE 2004 World Congress & Exhibition ; 2004



    Erscheinungsdatum :

    2004-03-08




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Print


    Sprache :

    Englisch




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