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A Novel User behavior prediction Model Based on Automatic Annotated behavior Recognition in Smart Home Systems

China Communications 2022年第9期19卷 116-132页

作者： Ningbo Zhang Yajie Yan Xuzhen Zhu Jing Wang Key Laboratory of Universal Wireless Communications Ministry of EducationBeijing University of Posts and TelecommunicationsBeijing 100876China School of Artificial Intelligence Beijing Normal UniversityBeijing 100875China

User behavior prediction has become a core element to Internet of Things(IoT)and received promising attention in the related fields.Many existing IoT systems(e.***.smart home systems)have been deployed various sensors and the user’s behavior can be predicted through the sensor data.However,most of the existing sensor-based systems use the annotated behavior data which requires human intervention to achieve the behavior prediction.Therefore,it is a challenge to provide an automatic behavior prediction model based on the original sensor data.To solve the problem,this paper proposed a novel automatic annotated user behavior prediction(AAUBP)model.The proposed AAUBP model combined the Discontinuous Solving Order Sequence Mining(DVSM)behavior recognition model and behavior prediction model based on the Long Short Term Memory(LSTM)network.To evaluate the model,we performed several experiments on a real-world dataset tuning the parameters.The results showed that the AAUBP model can effectively recognize behaviors and had a good performance for behavior prediction.

关键词： Internet of Things behavior recognition behavior prediction LSTM smart home systems

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A Study on Fake and Real Lane Change behavior prediction Based on Discrete Hidden Markov Model

A Study on Fake and Real Lane Change Behavior Prediction Bas...

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第40届中国控制会议

作者： Xianjian Jin Qikang Wang Zeyuan Yan Hang Yang Zhiwei Li Zhaoran Wang School of Mechatronic Engineering and Automation Shanghai Key Laboratory of Intelligent Manufacturing and Robotics Shanghai University State Key Laboratory of Automotive Simulation and Control Jilin University

In advanced assisted driving, an effective understanding of the driver’s behavior can effectively improve the safety of the driving process. In this paper, we use discrete hidden Markov models to predict lane change and real lane change behavior by collecting natural driving data from a driving simulator. First, we build a lane change road on the Prescan software, collect data from 12 groups of drivers and filter the data, select whether the four characteristic parameters of steering wheel angle,steering wheel angular velocity, yaw rate and yaw acceleration are true for lane change perform analysis. Then, on the effective data set training, the viterbi algorithm is used to predict the fake lane change and the real lane change behavior of the discrete hidden Markov model. The prediction results show that the model can use the above four characteristic parameters to predict fake lane change and real lane change behavior. The accuracy of the prediction respectively is 89.83%,91.20%,88.52%,80.99%,and it has good accuracy.

关键词： DHMM fake lane change real lane change behavior prediction

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