基于移动群智感知的极端驾驶行为感知方法

Detecting Extreme Driving Behaviors Based on Mobile Crowdsensing

为提高驾驶安全性,减少交通事故的发生,本文提出一种基于移动群智感知的极端驾驶行为识别方法。对收集到的用户智能手机相关传感器的数据进行预处理,进而利用动态步数检测和随机森林等方法来识别乘客的用户情境信息。针对不同的极端驾驶行为,选取不同位置乘客的智能手机来进行数据的收集,综合考虑乘客的手机放置位置因素所造成的相关影响,实现多特征融合的极端驾驶行为感知。针对不同位置的乘客所感知的结果不一致问题,提出采用贝叶斯投票模型来解决。通过真实数据实验,结果表明本文方法能够有效地识别出司机的极端驾驶行为。

In order to improve driving safety and reduce traffic accidents, this paper proposes an extreme driving behavior recognition method based on mobile crowdsensing.The collected data of sensors related to users' smart phones are preprocessed, and then the context information of passengers is identified by means of the dynamic step number detection and the random forest methods.According to different extreme driving behaviors, smart phones of passengers in different positions are selected for data collection, and the impact of different phone places and multi-feature fusion is also considered in designing the extreme driving behavior detection method.Regarding the potential inconsistency of the results from passengers in different positions, a Bayesian voting model is proposed to solve the problem.Experimental results from real world datasets indicate that our method can effectively identify the extreme driving behavior of drivers.