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A fault-tolerant estimation method of vehicle state based on long short-term memory neural network

A long-short-term memory and neural network technology, applied in the field of system state estimation, can solve the problem of low confidence in vehicle state estimation, achieve strong fault tolerance, and ensure the effect of estimation accuracy

Active Publication Date: 2022-07-26
NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
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Problems solved by technology

[0004] Aiming at the deficiencies of the above-mentioned prior art, the object of the present invention is to provide a vehicle state fault-tolerant estimation method based on long-short-term memory neural network to solve the problem of low confidence in vehicle state estimation caused by the lack of GPS signals in the prior art

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  • A fault-tolerant estimation method of vehicle state based on long short-term memory neural network
  • A fault-tolerant estimation method of vehicle state based on long short-term memory neural network
  • A fault-tolerant estimation method of vehicle state based on long short-term memory neural network

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[0066] In order to facilitate the understanding of those skilled in the art, the present invention will be further described below with reference to the embodiments and the accompanying drawings, and the contents mentioned in the embodiments are not intended to limit the present invention.

[0067] refer to figure 1 As shown, a method for fault-tolerant estimation of vehicle state based on long-short-term memory neural network of the present invention, the steps are as follows:

[0068] Step 1) Based on the vehicle two-degree-of-freedom dynamic model and the vehicle longitudinal kinematics model, establish a one-step prediction model at any k time;

[0069] The one-step prediction model at any time k is:

[0070]

[0071] In the formula, the system state quantity is: X s (k)=[v x (k) v y (k) ω z (k)] T , the system input is

[0072] U m (k)=[δ fm (k) a xm (k)] T , k, k+1 represents the timestamp, T represents the sampling time, C f Indicates the cornering stiffn...

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Abstract

The invention discloses a vehicle state fault-tolerant estimation method based on long-short-term memory neural network. The measurement value of the rear wheel speed sensor, the measurement value of the right rear wheel speed sensor, and the absolute vehicle speed of the fault-tolerant vehicle are used to establish the observation model; the one-step prediction model and the observation model are combined with the Kalman filter theory to estimate the longitudinal direction of the vehicle at any time. Vehicle speed, lateral speed and yaw rate. The method of the invention estimates the state of the vehicle by fusing the vehicle-mounted sensors and the GPS signal through the multi-sensor fusion technology, and uses the LSTM-RNN to solve the reconstructed absolute vehicle speed signal when the GPS is missing, so as to solve the problem of the estimation system failure caused by the lack of the GPS. It ensures the accuracy of vehicle state estimation and has strong fault tolerance.

Description

technical field [0001] The invention belongs to the technical field of system state estimation, and in particular relates to a vehicle state fault-tolerant estimation method based on a long-short-term memory neural network. Background technique [0002] In high-level autonomous vehicles, the real-time awareness of the vehicle's own state is crucial for vehicle energy saving and safety. Key vehicle state variables, such as vehicle longitudinal speed, vehicle lateral speed, and vehicle yaw rate, are the basis for the normal operation of vehicle safety systems such as vehicle ESP, ABS, and lateral stability control, and are an important branch of vehicle intelligence research. [0003] Due to factors such as measurement noise and inherent bias of a single sensor, it is difficult for a vehicle to accurately measure its own state through a single sensor. Therefore, multi-sensor fusion technology is developed to make full use of the advantages of multiple sensors and improve the c...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G07C5/08G01S19/14G06N3/04
CPCG07C5/0808G01S19/14G06N3/047G06N3/048G06N3/044
Inventor 章波赵万忠高犇胡犇周长志
Owner NANJING UNIV OF AERONAUTICS & ASTRONAUTICS