Automobile dynamic property and economy expectation quantification method

A quantitative method and dynamic technology, applied in the field of vehicle driving intention recognition, can solve the problems that the accuracy of the quantitative results needs to be improved, and the membership function cannot be optimized.

Active Publication Date: 2020-04-17
XIHUA UNIV
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AI Technical Summary

Problems solved by technology

[0007] Existing recognition technologies are mostly used to classify driving behaviors or intentions, while a small number of technologies that quantify power and/or economic

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  • Automobile dynamic property and economy expectation quantification method
  • Automobile dynamic property and economy expectation quantification method
  • Automobile dynamic property and economy expectation quantification method

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Embodiment Construction

[0117] Explanation of terms:

[0118] 1. Dynamic expectations and economical expectations: the driver’s performance as reflected by the characteristic parameters of driving manipulation (such as accelerator pedal opening and its rate of change, brake pedal strength, etc.) and vehicle operating status information (such as vehicle speed, engine speed, etc.) The degree of inclination towards vehicle power performance and economic performance. The quantified values ​​of dynamic expectations and economic expectations are called dynamic expectations and economic expectations respectively. In this scheme, the value range of dynamic expectation value and economic expectation value is defined as [0,1], and the sum of the two is 1.

[0119] 2. Fuzzification: Fuzzification is the process of converting a certain value into its corresponding fuzzy language variable value.

[0120] 3. Defuzzification: also known as defuzzification or defuzzification, is to transform the fuzzified language...

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Abstract

The invention discloses an automobile dynamic property and economy expectation quantification method, which belongs to the technical field of driving intention identification. The method is divided into two stages of offline modeling and real-time quantification. In the off-line modeling stage, the automobile road test speed, the accelerator opening degree and the dynamic property expectation subjective evaluation of different drivers are collected and processed; a fuzzy neural network is adopted to establish a driver dynamic property expectation quantification model; and a driving manipulation characteristic parameter and vehicle operation state parameter prediction model are established based on the exponential smoothing-Markov model. In the real-time quantification stage, the vehicle speed and the accelerator opening degree are collected in real time; parameter prediction is carried out through a driving manipulation characteristic parameter and vehicle running state parameter prediction model established offline; the predicted value is input into a driver dynamic property expectation quantification model to obtain a dynamic property expectation value; and the expected economicvalue is further calculated. The method can continuously quantify the expectation of the driver for the dynamic property and economy of the automobile, and has the characteristic of high quantizationprecision.

Description

technical field [0001] The invention relates to an automobile driving intention recognition technology, specifically a method for quantifying the driver's power and economic expectations, and selecting different shift schedules based on the quantification results. Background technique [0002] In "A Driving Intention Recognition Method Based on Improved HMM and SVM Double-layer Algorithm" (CN106971194A), the invention uses the improved HMM and SVM double-layer algorithm for off-line training, and the driver's sudden left lane change and normal left change It recognizes driving intentions such as lane keeping, lane keeping, normal right lane change and sudden right lane change. [0003] In "A Driver's Intention Recognition Method" (CN103318181A), the invention uses a multi-dimensional discrete hidden Markov model to propose a two-layer recognition structure to recognize the driver's acceleration / braking behavior and steering / lane-changing behavior. [0004] In "A Shift Corre...

Claims

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Application Information

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IPC IPC(8): G06K9/62G06N3/04G06N3/08
CPCG06N3/08G06N3/043G06N3/045G06F18/295Y02T10/84
Inventor 阴晓峰罗位刘阳李海波
Owner XIHUA UNIV
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