基于贝叶斯模型的驾驶行为识别与预测

王新胜; 卞震

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

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基于贝叶斯模型的驾驶行为识别与预测

论文Ⅱ：学术论文 | 更新时间：2024-06-05

- 基于贝叶斯模型的驾驶行为识别与预测
- Driving behavior recognition and prediction based on Bayesian model
- 通信学报 2018年39卷第3期页码：108-117
- 作者机构：
  
  江苏大学计算机科学与通信工程学院，江苏镇江 212013
- 作者简介：
  
  [ "王新胜（1972-），男，江苏宿迁人，博士，江苏大学副教授，主要研究方向为无线传感器网络等。" ]
  [ "卞震（1992-），男，江苏淮安人，江苏大学硕士生，主要研究方向为车联网安全结构。" ]
- 基金信息：
  
  国家自然科学基金资助项目(U1764263)
- DOI：10.11959/j.issn.1000-436x.2018043
  中图分类号： TP393
- 网络出版日期：2018-03，
  
  纸质出版日期：2018-03-25
- 稿件说明：
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王新胜, 卞震. 基于贝叶斯模型的驾驶行为识别与预测[J]. 通信学报, 2018,39(3):108-117.

Xinsheng WANG, Zhen BIAN. Driving behavior recognition and prediction based on Bayesian model[J]. Journal on communications, 2018, 39(3): 108-117.
王新胜, 卞震. 基于贝叶斯模型的驾驶行为识别与预测[J]. 通信学报, 2018,39(3):108-117. DOI： 10.11959/j.issn.1000-436x.2018043.

Xinsheng WANG, Zhen BIAN. Driving behavior recognition and prediction based on Bayesian model[J]. Journal on communications, 2018, 39(3): 108-117. DOI： 10.11959/j.issn.1000-436x.2018043.

摘要

针对智能驾驶系统处理大量驾驶数据时出现的效率和精度不足的问题，提出一种基于贝叶斯模型来处理驾驶数据，识别和预测人类驾驶行为的方法。该方法可以无监管地通过驾驶数据对应地推断出具体驾驶行为，共分为2步：第一步，通过贝叶斯模型分割算法将惯性传感器收集到驾驶数据分割为近线性分段；第二步，通过LDA拓展模型将线性分段聚集为具体的驾驶行为（如制动、转弯、加速和惯性滑行）。离线实验和在线实验结果表明，在处理大量驾驶数据的情况下，该方法效率和识别精度更高。

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.

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references

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