【摘要】 驾驶风格评价是智能交通领域的重要研究课题,一般采用频域或时域的分析方法对其进行定性的分类和识别,缺乏客观定量的评价体系。提出一种基于相空间重构的驾驶风格定量分析方法。首先,采用汽车测试数据和局部性神经网络建立个性化的驾驶员模型;然后,将个性化驾驶员模型应用于标准驾驶周期测试工况的速度跟随试验,以实现驾驶行为的标准化;最后,对标准化驾驶行为进行相空间重构,提出一种基于关联维数的驾驶风格指数,用于定量评估驾驶的激进程度,并应用于驾驶员风格的识别,仿真结果验证了所提方法的有效性。更多还原

【Abstract】 Driving style evaluation is an important research topic in the field of intelligent transportation,which is usually recognized and classified qualitatively using time or frequency domain analysis methods and lacksobjective, quantitative evaluation system. A quantitative analysis method for driving style of the driver based on phase space reconstruction is proposed. Firstly,the vehicle test data and local neural network are adopted to establish a personalized driver model. Secondly,the personalized driver model is applied to the speed tracking test in normalizeddriving cycle test to achieve the normalization of driving behavior. Finally, the phase space reconstruction of the normalized driving behavior is conducted,a driving style index is proposed based on the correlation dimensionand used for quantitatively evaluating the aggressiveness of the driving behavior. The driving style index is further applied to the recognition of driving styles. Simulation results verify the effectiveness of the proposed method.更多还原