NAR neural network vehicle speed prediction method based on driving intention recognition

Abstract

The invention discloses an NAR (Nonlinear Autoregressive Models) neural network vehicle speed prediction method based on driving intention recognition. The method comprises the following steps of driving intention classification and recognition parameter selection; fuzzy reasoning recognition of the driving intention; NAR neural network off-line training; and NAR neural network on-line vehicle speed prediction: firstly performing driving intention recognition, and then inputting the driving intention obtained through recognition and the vehicle speed time sequence into an NAR neural network together so as to realize the vehicle speed prediction of the vehicle in a period of time in future. The NAR neural network vehicle speed prediction method has the advantages that the NAR neural network is used for performing vehicle speed prediction; the neural network input includes the network output feedback; the method is suitable to be used for solving the nonlinear problem on the time sequence; and the multi-step prediction precision can be obviously improved. The driving intention time sequence and the vehicle speed are introduced to be jointly used as the input; the fuzzy reasoning is used for analyzing the pedaling operation of a driver; the expectation of the driver on the future change trends of the vehicle speed is sufficiently shown; and the vehicle speed prediction accuracy is improved.

Description

technical field [0001] The invention relates to a vehicle speed prediction method, in particular to a nonlinear autoregressive (Nonlinear Autoregressive Models, NAR) neural network vehicle speed prediction method. Background technique [0002] In the research of intelligent vehicles and automobile energy saving, vehicle speed prediction is widely used in automobile automatic transmission gear control, path planning and navigation, safety assisted driving and predictive control strategy of hybrid electric vehicles, so as to improve the safety and fuel economy of automobiles. performance and emissions. Therefore, it is of great significance to accurately predict the speed of a moving vehicle in the future. Vehicle speed prediction is highly time-varying and non-linear, and is a typical time series prediction problem. Most of the existing prediction methods use feedforward neural network, such as BP neural network and RBF neural network, but they only predict the output accor...

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Problems solved by technology

In addition, for the input of the neural network, most scholars generally use the data collected by the car GPS and the statistical analysi

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