In this paper, the personalized and common acceleration distribution characteristics of the human driver are presented by using China-FOT. Firstly, how much data can get a convergent acceleration distribution of human driver is discussed. Multivariate kernel density estimation and kullback-leibler divergence are utilized to compare the acceleration distribution of each driver between datasets which consist of the different amount of data. It is found that 95% drivers can achieve convergent distributions within $200\times 104$ driving data. Secondly, $200\times 104$ driving data of each driver are selected to obtain the acceleration distributions of different drivers. The common acceleration distribution characteristics include: (1) the acceleration distributions of all the drivers follow the so-called dual triangle distribution; (2) the 1st percentile contour interior areas of all the drivers resemble each other and are an approximately diamond shape. The personalized acceleration distribution characteristics include: (1) the 1st percentile contour exterior areas of each driver have many differences, which means that the distribution area and distribution shape differ from driver to driver; (2) the distribution area is strong linearly correlated to the maximum lateral acceleration, and the distribution shape is correlated to the distribution area.


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    Title :

    Personalized and Common Acceleration Distribution Characteristic of Human Driver


    Contributors:
    Liu, Rui (author) / Zhu, Xichan (author) / Liu, Lin (author) / Wu, Biao (author)


    Publication date :

    2018-11-01


    Size :

    840530 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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