Xiang Huaikun, Zhu Jiafeng, Liang Guoyuan, Shen Yingjun
School of Automotive and Transportation Engineering, Shenzhen Polytechnic, Shenzhen, China.
Center for Intelligent Biomimetic Systems, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China.
Front Neurorobot. 2021 Apr 29;15:641007. doi: 10.3389/fnbot.2021.641007. eCollection 2021.
Dangerous driving behavior is the leading factor of road traffic accidents; therefore, how to predict dangerous driving behavior quickly, accurately, and robustly has been an active research topic of traffic safety management in the past decades. Previous works are focused on learning the driving characteristic of drivers or depended on different sensors to estimate vehicle state. In this paper, we propose a new method for dangerous driving behavior prediction by using a hybrid model consisting of cloud model and Elman neural network (CM-ENN) based on vehicle motion state estimation and passenger's subjective feeling scores, which is more intuitive in perceiving potential dangerous driving behaviors. To verify the effectiveness of the proposed method, we have developed a data acquisition system of driving motion states and apply it to real traffic scenarios in ShenZhen city of China. Experimental results demonstrate that the new method is more accurate and robust than classical methods based on common neural network.
危险驾驶行为是道路交通事故的主要因素；因此，如何快速、准确且稳健地预测危险驾驶行为在过去几十年一直是交通安全管理领域的一个活跃研究课题。以往的工作主要集中在学习驾驶员的驾驶特性或依赖不同传感器来估计车辆状态。在本文中，我们基于车辆运动状态估计和乘客主观感受评分，提出了一种使用由云模型和埃尔曼神经网络（CM-ENN）组成的混合模型来预测危险驾驶行为的新方法，该方法在感知潜在危险驾驶行为方面更具直观性。为了验证所提方法的有效性，我们开发了一个驾驶运动状态数据采集系统，并将其应用于中国深圳市的实际交通场景。实验结果表明，该新方法比基于普通神经网络的经典方法更准确、更稳健。
