The invention discloses an online self-learning Markov vehicle speed prediction method which comprises the following steps: (10) constructing an off-line data selecting standard working conditions, and integrating and forming a vehicle running speed off-line database; (20) offline training of a state transition probability matrix: defining the acceleration of the vehicle as a Markov state, determining a prediction range, counting state transition events, and calculating the transition probability matrix; and (30) designing a state transition matrix real-time updating algorithm: deducing a state transition matrix recursion form, defining a self-learning factor, and realizing online updating of the state transition matrix by using vehicle acceleration historical information. And (40) online prediction: acquiring acceleration historical data of the vehicle, updating the transition probability matrix, and predicting the speed of the vehicle. By adopting the online self-learning Markov prediction method provided by the invention, the speed prediction precision and the calculation time efficiency can be improved; and meanwhile, the method is simple and easy to implement, high in working condition self-adaption and good in engineering application prospect.
本发明公开一种在线自学习型的马尔可夫车辆速度预测方法,包括如下步骤:(10)构建离线数据库:选取标准工况,整合并形成车辆行驶速度离线数据库。(20)状态转移概率矩阵离线训练:定义车辆加速度为马尔可夫状态,确定预测范围,统计状态转移事件,计算转移概率矩阵。(30)设计状态转移矩阵实时更新算法:推导状态转移矩阵递归形式,定义自学习因子,利用车辆加速度历史信息,实现状态转移矩阵在线更新。(40)在线预测:获取车辆的加速度历史数据,更新转移概率矩阵,预测车辆速度。采用本发明提出的在线自学习型马尔科夫预测方法,可以提升速度预测的精度与计算时效;同时该发明方法简单易实现,工况自适应强,具有良好的工程应用前景。
Online self-learning Markov vehicle speed prediction method
一种在线自学习型的马尔可夫车辆速度预测方法
2023-10-20
Patent
Electronic Resource
Chinese
IPC: | B60W CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION , Gemeinsame Steuerung oder Regelung von Fahrzeug-Unteraggregaten verschiedenen Typs oder verschiedener Funktion |
Vehicle Speed Prediction Using a Cooperative Method of Fuzzy Markov Model and Autoregressive Model
British Library Conference Proceedings | 2017
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