基于贝叶斯模型的驾驶行为识别与预测

doi:10.11959/j.issn.1000-436x.2018043

Abstract

Abstract:

Since the existing intelligent driving systems are lack of efficiency and accuracy when processing huge number of driving data,a brand new approach of processing driving data was developed to identify and predicate human driving behavior based on Bayesian model.The approach was proposed to take two steps to deduce the specific driving behavior from driving data correspondingly without any supervision,the first step being using Bayesian model segmentation algorithm to divide driving data that inertial sensor collected into near-linear segments with the help of Bayesian model segmentation algorithm,and the second step being using extended LDA model to aggregate those linear segments into specific driving behavior (such as braking,turning,acceleration and coasting).Both offline and online experiments are conducted to verify this approach and it turns out that approach has higher efficiency and recognition accuracy when dealing with numerous driving data.

Key words: driving data, Bayesian model, inertial sensor, linear segmentation

CLC Number:

TP393

Xinsheng WANG,Zhen BIAN. Driving behavior recognition and prediction based on Bayesian model[J]. Journal on Communications, 2018, 39(3): 108-117.

Figures/Tables 9

时间	加速度/(rad·s^?2)	z轴角速度/(rad·s^?1)	x轴角速度/(rad·s^?1)
11:20:24	0.454	0.031	0.003
11:20:25	0.497	0.022	0.006
11:20:25	0.015	0.008	0.002
11:20:25	0.278	?0.000	?0.006
11:20:25	0.477	?0.010	?0.004
11:20:25	0.167	?0.003	0.003
11:20:26	0.637	?0.021	0.001
$⋮$	$⋮$	$⋮$	$⋮$

References 23

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研究方案	计算复杂度	聚集方法	监管性	长期适用性	结果精度
基于分段自回归模型	较低	未提供聚集方法	需要	较差	较低
基于贝叶斯非参数模型	较高	LDA模型聚集主题	不需要	较好	较高
利用惯性传感器和相机	低	使用相机聚集主题	需要	差	低
利用阈值和车辆信号	低	使用阈值聚集主题	需要	较差	较低

Driving behavior recognition and prediction based on Bayesian model

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