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Driver visual perception prediction model generation method based on regression learning

A predictive model and visual perception technology, applied in the field of visual perception, to achieve good autonomy and intelligence

Inactive Publication Date: 2017-03-08
BEIJING UNION UNIVERSITY
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Problems solved by technology

[0005] The purpose of the present invention is to provide a driver's visual perception prediction model generation method about the relationship between vehicle speed and field of view, vehicle speed and dynamic vision, and vehicle speed and gaze point, so as to solve the problem of intelligent driving behavior

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  • Driver visual perception prediction model generation method based on regression learning
  • Driver visual perception prediction model generation method based on regression learning
  • Driver visual perception prediction model generation method based on regression learning

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Embodiment Construction

[0040] Such as figure 1 As shown, the embodiment of the present invention discloses a method for predicting the driver's visual perception based on regression learning, including the following steps:

[0041] Step 1. Obtain sample data, and carry out regression modeling on the relationship between vehicle speed and gaze point, vehicle speed and dynamic vision, and vehicle speed and field of vision. The regression modeling includes: logarithmic regression modeling, exponential regression modeling, polynomial regression modeling Modular, linear regression modeling;

[0042] Step 2: Carry out regression diagnosis on the different regression modeling to obtain the best prediction model.

[0043] The second step specifically includes the following steps:

[0044] S1. For the regression diagnosis of the above-mentioned regression equations between vehicle speed and field of view, vehicle speed and dynamic vision, and vehicle speed and fixation point, it is first necessary to judge...

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Abstract

The present invention discloses a driver visual perception prediction model generation method based on regression learning. The method comprises: acquiring sample data, performing regression modeling on relationships between a vehicle speed and a fixation point, between the vehicle speed and dynamic vision, and between the vehicle speed and a visual field, wherein the regression modeling comprises logarithmic regression modeling, exponential regression modeling, polynomial regression modeling and linear regression modeling; and performing regression diagnosis on different regression models, to obtain a best prediction model. By adoption of the method provided by the technical scheme of the present invention, intelligent vehicles has higher autonomy and intelligence through knowledge acquisition and representation of driving behaviors, and with the theory of cognition applied to the driving behavior research field, the inherent mechanism of driving operations is analyzed, so that intelligent driving behaviors can be better explained and understood.

Description

technical field [0001] The invention belongs to the field of visual perception, in particular to a method for generating a driver's visual perception prediction model based on regression learning. Background technique [0002] During the driving process, the driver mainly relies on the sensory organs eyes, ears, nose, tongue, and skin to perceive and obtain various traffic information in a timely manner. Obtaining insufficient information or missing necessary information may lead to traffic accidents. Therefore, obtaining information is the most important psychological process in driving behavior. The visual perception model mainly solves three problems: first, in the actual driving activities, where is the driver's visual focus; second, how large is the driver's visual range. Drivers always focus on the road ahead when driving in a straight line, and the higher the speed, the more concentrated the attention and the more difficult it is to transfer. As the vehicle speed i...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06F19/00
CPCG16Z99/00
Inventor 鲍泓关泉珍马楠徐歆凯阳钧
Owner BEIJING UNION UNIVERSITY
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