Method for testing intelligence of unmanned vehicle based on bench

A technology for bench testing and unmanned vehicles, applied in the field of unmanned vehicles, can solve the problems of insufficient testing of the intelligent type of unmanned vehicles and a single environment model, and achieve high authenticity, accurate test basis, and low cost of data collection. Effect

Active Publication Date: 2018-12-25
CHANGAN UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Existing bench-based testing methods for unmanned vehicle intelligence simulate a

Method used

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[0023] The present invention will be further described in detail below through specific embodiments in conjunction with the accompanying drawings. Wherein similar elements in different embodiments have used associated similar element numbers. In the following embodiments, many details are described so that the present application can be better understood. However, those skilled in the art will readily recognize that some of the features may be omitted under different circumstances, or may be replaced by other elements, materials, and methods. In some cases, some operations related to the present application are not shown or described in the specification, in order to avoid the core part of the present application from being overwhelmed by excessive description, and for those skilled in the art, these are described in detail. The relevant operations are not necessary, and they can fully understand the relevant operations according to the descriptions in the specification and gene

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Abstract

The invention provides a method for testing intelligence of an unmanned vehicle based on a bench. The method comprises the following steps of: arranging the unmanned vehicle to be tested on the test bench to simulate driving; constructing a virtual scene and each data type in the virtual scene; acquiring, by a host vehicle, operation parameters of the unmanned vehicle to be tested and road surfaceinformation simulated by the test bench and simulating the host vehicle into the virtual scene; interacting the host vehicle with the each data type in the virtual scene to generate virtual driving behavior data; and taking the driving behavior data generated by the host vehicle as a sample set, and adopting a random forest algorithm to take a category with most output times in the decision treeas the category of the test sample. The data acquisition cost is low due to the driving behavior data between the host vehicle and the each data type is acquired by adopting data driving, the providedoriginal data has higher authenticity, and the controllability of the data realizes reproduction of the detection scene.

Description

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Claims

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Application Information

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Owner CHANGAN UNIV
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