Driving Trajectory Prediction Method Based on Adaboost-Markov Model Optimization

摘要：

　　The driving trajectory prediction method based on the traditional prediction algorithm model has the disadvantages of small prediction accuracy and low matching rate.This paper proposes an improved driving trajectory prediction method based on Adaboost-Markov model.The method adaptively determines the model order m,and uses the Adaboost algorithm to determine the weight coefficients to form a multi-order Markov model.The experimental results show that compared with the fixed-order Markov model,the average prediction accuracy of the Adaboost-Markov model is significantly improved,and it has lower algorithm complexity,which is suitable for vehicle driving trajectory prediction under massive data.

关键词： Position prediction trajectory matching rate Adaboost multi-order Markov adaptive weight ratio

作者: Shengnan Song Yongjun Zhang

作者单位: State Key Laboratory of Information and Optical Communications Beijing University of Post and Telecommunications Beijing,China

会议类型: 国际会议

会议名称: 2019 2nd International Conference on Mechanical Engineering, Industrial Materials and Industrial Electronics (MEIMIE 2019)2019年第二届机械工程、工业材料和工业电子国际会议(Meimie 2019)

会议地点: 大连

会议语种:英文

页码: 351-357

在线出版日期: 2019-03-29（万方平台首次上网日期，不代表论文的发表时间）