基于AIOHMM模型的驾驶行为预测

【摘要】 驾驶员行为预测能够有效减少交通事故发生,因此,研究驾驶员的意图是非常有必要的。本文根据驾驶行为受到驾驶员驾驶意图和车外部驾驶环境的影响,在时间上,先有驾驶意图后有驾驶行为,利用驾驶意图在行为实验过程中的时间差和车外部环境建立自回归输入输出隐性马尔科夫模型(Autoregressive Input-Output HMM)。首先根据采集到车内外的视频数据提取特征值,利用建立的模型进行模型训练,学习得到模型参数,然后用得到的参数对驾驶员换道、转弯和保持直行等行为进行预测。利用Matlab仿真,得出该模型在驾驶行为预测中具有更高的准确性,花费了更少的预测时间,降低了预测的召回率。更多还原

【Abstract】 Driver behavior prediction can effectively reduce traffic accidents, so it is very necessary to study the driver’s intentions. The driving behavior is influenced by the driver’s driving intention and the driving environment outside the vehicle. In time, there is driving behavior after driving intention. The time difference between driving intention and behavioral experiment and the external environment of the vehicle is used to establish autoregressive input and output hidden Autoregressive InputOutput HMM. Firstly, based on the video data collected inside and outside the vehicle, the feature values are extracted, the model is trained by using the established model, the model parameters are learned, and the obtained parameters are used to predict the behaviors such as lane change, turn and keep straight. Using Matlab, the model has higher accuracy in driving behavior prediction, which takes less prediction time and reduces the predicted recall rate.更多还原