Naturalistic driving data were applied to study driver acceleration behaviour, and a probability model of the driver was proposed. First, the question of whether the database is large enough is resolved using kernel density estimation and Kullback-Liebler divergence. Next, the convergence database is utilised to achieve the bivariate acceleration distribution pattern. Subsequently, two probability models are proposed to explain the pattern. Finally, the statistical characteristics of the acceleration behaviours are studied to verify the probability models. The longitudinal and lateral acceleration behaviours always approximate a similar Pareto distribution. The braking, accelerating, and steering manoeuvres become more intense at first and then less intense as the velocity increases. These behaviours characteristics reveal the mechanism of the quadrangle bivariate acceleration distribution pattern. The bivariate acceleration behaviour of the driver will never reach a circle-shaped pattern. The bivariate Pareto distribution model can be applied to describe the bivariate acceleration behaviour of the driver.
翻译：自然驱动数据被用于研究驱动加速行为,并提出了一个驱动器的概率模型。首先,使用内核密度估计和 Kullback-Liebler 差异解决数据库是否足够大的问题。接下来,利用趋同数据库实现双轨加速加速分布模式。随后,提出两个概率模型来解释该模式。最后,研究加速行为的统计特征以核实概率模型。纵向和横向加速行为总是接近相似的Pareto分布。制动、加速和方向动作随着速度的增加而开始变得更为密集。这些行为特征揭示了四边形双轨加速分布模式的装置。驱动器的双轨加速行为永远不会达到圆形模式。双轨Pareto分布模型可用于描述驱动器的双轨加速行为。
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