By adapting vehicle control systems to the skill level of the driver, the overall vehicle active safety provided to the driver can be further enhanced for the existing active vehicle controls, such as ABS, Traction Control, Vehicle Stability Enhancement Systems. As a follow-up to the feasibility study in [1], this paper provides some recent results on data-driven driving skill characterization. In particular, the paper presents an enhancement of discriminant features, the comparison of three different learning algorithms for recognizer design, and the performance enhancement with decision fusion. The paper concludes with the discussions of the experimental results and some of the future work.


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    Title :

    Data-Driven Driving Skill Characterization: Algorithm Comparison and Decision Fusion


    Additional title:

    Sae Technical Papers


    Contributors:

    Conference:

    SAE World Congress & Exhibition ; 2009



    Publication date :

    2009-04-20




    Type of media :

    Conference paper


    Type of material :

    Print


    Language :

    English




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