Vehicle stability index estimation method of based on depth learning

A deep learning and stability technology, applied in neural architecture, biological neural network models, etc., can solve the problems of filtering divergence, reduced result accuracy, inaccurate observation noise of the model, etc., and achieve small transient deviation of estimation, good estimation, and estimation Excellent precision

Active Publication Date: 2018-10-30
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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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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  • Vehicle stability index estimation method of based on depth learning
  • Vehicle stability index estimation method of based on depth learning

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Embodiment Construction

[0022] Below in conjunction with accompanying drawing, technical scheme of the present invention is described in further detail:

[0023] This invention may be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the scope of the invention to those skilled in the art. In the drawings, components are exaggerated for clarity.

[0024] like figure 1 and figure 2 As shown, the present invention discloses a method for estimating vehicle stability index based on deep learning, comprising the following steps:

[0025] Step 1), set up the GPS system, gyroscope, front wheel angle sensor and yaw rate sensor on the vehicle, the GPS system is used to obtain the Doppler frequency shift based on the satellite signal to determine the speed of the vehicle, the gyroscope For detecting the longitudinal acceleration and l...

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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...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): B60W40/00B60W40/064B60W40/105B60W40/107B60W40/109G06N3/04
CPCB60W40/00B60W40/064B60W40/105B60W40/107B60W40/109G06N3/045
Inventor 汪吴树凡魏民祥严明月张佳佳张凤娇周东季昊成刘锐贝太学
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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