Vehicle stability index estimation method of based on depth learning

Abstract

The invention discloses a vehicle stability index estimation method based on depth learning, which comprises a high-precision GPS system, a gyroscope and a front wheel angle sensor, a preliminary model of a long and short term memory (LSTM) neural network is established based on a software platform, the real vehicle experimental sample data is used to train the LSTM neural network to generate a time-delay non-linear prediction model, and after meeting the accuracy of the vehicle gauge level, a complete estimation module is packaged, and the centroid side deflection angle and the yaw rate valueof the vehicle are automatically output according to the real time input of the sensor information so as to realize the vehicle state estimation. According to the invention, the model has the capabilities of on-line learning and dynamic updating while estimating the vehicle state, the estimation precision is continuously improved through self-learning, and the development of the active safety control of the automobiles is promoted.

Description

technical field [0001] The invention relates to the field of automobile active safety, in particular to a method for estimating vehicle stability indicators based on deep learning. Background technique [0002] With the advent of the era of artificial intelligence, unmanned driving has become a research hotspot today. The biggest problem for unmanned vehicles on the road is safety, and the active safety technology of automobiles is bound to attract more and more people's attention. Among them, the stability of the vehicle during driving is one of the core issues of active safety research. The primary issue of its control is to collect important parameters such as the current yaw rate of the vehicle and the side slip angle of the center of mass. For the acquisition of the above-mentioned key state parameters, the most widely used method in current research is state estimation based on various algorithms. These methods mainly include extended Kalman filter, fuzzy extended Ka...

AI Technical Summary

Problems solved by technology

However, these methods are based on the traditional vehicle dynamics model or assume that the algorithm parameters (observation noise covariance) are fixed to perform state estimation. The inaccuracy of the model and the randomness of the observation noise will lead to a decrease in the accuracy of the results, and may even lead to filter divergence

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