通信学报2018，Vol.39Issue(3)：108-117,10.DOI:10.11959/j.issn.1000-436x.2018043
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
摘要
Abstract
Since the existing intelligent driving systems are lack of efficiency and accuracy when processing huge num-ber 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 segmenta-tion 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.关键词
驾驶数据/贝叶斯模型/惯性传感器/线性分段Key words
driving data/Bayesian model/inertial sensor/linear segmentation分类
信息技术与安全科学引用本文复制引用
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):108-117,10.基金项目
国家自然科学基金资助项目(No.U1764263)The National Natural Science Foundation of China (No.U1764263) （No.U1764263）
