Road feel simulation method based on GMM and CART regression tree

A road sense simulation and regression tree technology, applied in the field of vehicles, can solve problems such as low accuracy and complex model structure.

Pending Publication Date: 2021-04-09
浙江天行健智能科技有限公司
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  • Abstract
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  • Claims
  • Application Information

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Problems solved by technology

[0005] The object of the present invention is to provide a kind of road feeling simulation method based on GMM and CART regression tree, carry out modeling with real vehicle test data, Gaussian mixture model (GMM) classificati

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  • Road feel simulation method based on GMM and CART regression tree
  • Road feel simulation method based on GMM and CART regression tree
  • Road feel simulation method based on GMM and CART regression tree

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Embodiment Construction

[0079] In order to enable those skilled in the art to better understand the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments. Obviously, the described embodiments are only the embodiments of the present invention Some examples, but not all examples. Based on the embodiments of the present invention, all other embodiments obtained under the premise of equivalent changes and modifications made by those skilled in the art shall fall within the protection scope of the present invention.

[0080] see Figure 1 to Figure 3 , this embodiment provides a road feeling simulation method based on GMM and CART regression tree, including modeling steps S1-S7, and model application steps. The following combination figure 1 Steps S1-S7 of the modeling process are described in detail.

[0081] S 1. Carry out real vehicle test and collect data:...

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Abstract

The invention discloses a road feeling simulation method based on a GMM and a CART regression tree. The method comprises the steps: carrying out the real vehicle testing and data collection, carrying out the preprocessing of test data, carrying out the normalization of test data clustering, dividing a training and testing data set, training and testing a road feeling simulation model based on the GMM and the CART regression tree, and judging whether the obtained road feeling model meets the requirements or not. performing road feel simulation according to the obtained road feel simulation model based on the GMM and the CART regression tree, wherein the input variables of the CART regression tree model are longitudinal vehicle speed, vehicle transverse acceleration, vehicle yaw velocity, vehicle vertical load, steering wheel angle and steering wheel angular velocity, and the output variables of the CART regression tree model are steering wheel torque. Tests prove that the road feel simulation model based on the GMM and the CART regression tree, which is obtained by the method, is relatively high in precision, the modeling process is easy to implement, and the defects in the prior art are overcome to a certain extent.

Description

technical field [0001] The invention relates to the field of vehicles, in particular to a road feeling simulation method based on GMM and CART regression tree. Background technique [0002] Steering road feel, also known as steering force feeling and steering wheel feedback torque, refers to the reverse resistance torque felt by the driver through the steering wheel feedback torque. The steering sense can reflect the state of the road surface and the running state of the vehicle, allowing the driver to make correct decisions in line with the current driving conditions, thereby ensuring driving safety. Therefore, for the vehicle simulator driver and the vehicle using the steer-by-wire system, an indispensable function is to provide a more realistic road feeling, which can satisfy the driver's information on vehicle driving under the premise of ensuring the driver's safety. In order to make the driver drive rationally, ensure driving safety or make the driver's behavior more ...

Claims

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Application Information

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IPC IPC(8): G06F30/15G06F17/18G06F30/27G06K9/62G06F119/02
CPCG06F30/15G06F30/27G06F17/18G06F2119/02G06F18/23G06F18/214
Inventor 赵蕊蔡锦康邓伟文丁娟
Owner 浙江天行健智能科技有限公司
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