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QoE-based customized automatic driving parameter optimal setting method

A technology of automatic driving and driving parameters, which is applied in the direction of registration/indication of vehicle operation, instrumentation, registration/instruction, etc. It can solve the problem that the Internet of Things cannot improve the training and configuration of automatic driving of automobiles, and ignore the individual needs and user experience of users Quality, difficult to guarantee the passenger experience and other issues, to achieve the effect of low cost, short time, and improve driving efficiency

Active Publication Date: 2015-04-01
YUNNAN UNIV
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Problems solved by technology

However, the current autopilot training technology cannot make full use of the intelligent characteristics of each car, and cannot improve the training and configuration of autopilot through the Internet of Things, and this configuration of driving parameters completely ignores the user's personalized needs. Requirements and user quality of experience (QoE), it is difficult to ensure that passengers can get the best experience in every section of the road and every autonomous driving

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  • QoE-based customized automatic driving parameter optimal setting method
  • QoE-based customized automatic driving parameter optimal setting method
  • QoE-based customized automatic driving parameter optimal setting method

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Embodiment Construction

[0030] The present invention will be further described now in conjunction with accompanying drawing, and the whole flow process of the present invention can refer to accompanying drawing Figure 4 , the specific implementation steps are as follows:

[0031] 1) Route segmentation and environmental parameter acquisition

[0032] Information such as the slope, curvature, and wind direction of the distance traveled by the car during driving is different in different road sections. This step divides the driving route into several different driving sections according to the road gradient and curvature between the starting point and the destination set by the driver, and each driving section has information such as approximate slope and curvature, such as figure 2 shown. By detecting these environmental parameters, the environmental information feature vector of the road section is obtained: F h ={F h1 , F h2 , F h2 ...F hn}, n is the dimension of the vector, the feature vect...

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Abstract

The invention discloses a QoE-based customized automatic driving parameter optimal setting method. The method comprises the following steps of 1, route segmentation and environmental parameter acquisition, 2, search of historical data similar to the current environmental parameters in cloud, 3, QoE-based customized automatic driving parameter optimal selection and 4, automatic driving parameter setting. The QoE-based customized automatic driving parameter optimal setting method utilizes historical driving data of the same type of vehicles and road information stored in the cloud to help a novel vehicle to fast set driving parameters and improve driving efficiency so that time is short and a cost is low.

Description

technical field [0001] The present invention proposes a method for automatically configuring driving parameters of a self-driving car by learning historical driving data and road information of the same type of vehicle stored in the cloud, which belongs to the field of machine learning and intelligent control. It is suitable for providing automatic parameter configuration services based on cloud servers for autonomous vehicles. Background technique [0002] With the continuous maturity of automobile autonomous driving technology, autonomous vehicles will have broad market application value in the future. How to effectively configure the driving parameters is an important problem to be solved urgently. At present, the self-driving vehicle is usually allowed to acquire relevant parameters through a series of training and learning in the driving environment. This method requires a lot of time and financial resources to study and train different vehicles separately. The trainin...

Claims

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Application Information

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IPC IPC(8): G07C5/08
Inventor 张德海张德刚
Owner YUNNAN UNIV
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