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Local pavement adhesion coefficient estimation method based on image segmentation

A road surface adhesion coefficient and image segmentation technology, applied in neural learning methods, calculations, computer components, etc., to achieve good real-time performance, improve safety, reduce time costs and economic costs

Pending Publication Date: 2021-02-05
JIANGSU UNIV
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Problems solved by technology

[0005] In order to solve the defects of the existing road surface adhesion coefficient estimation method, the present invention proposes a local road surface adhesion coefficient estimation method based on image segmentation

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  • Local pavement adhesion coefficient estimation method based on image segmentation
  • Local pavement adhesion coefficient estimation method based on image segmentation
  • Local pavement adhesion coefficient estimation method based on image segmentation

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Embodiment Construction

[0023] The present invention will be further described below in conjunction with accompanying drawing. It should be understood that the specific examples described here are only used to explain the present invention, not to limit the present invention.

[0024] The general technical process of a local road surface adhesion coefficient estimation method based on image segmentation is shown in the attached figure 1 shown, including:

[0025] Step 1: Offline pre-training image segmentation network, specifically including: a. Use CARLA software to collect road surface images in different weather conditions; b. Locally label the collected road surface images in different weather conditions to form a data set for local road surface adhesion coefficient estimation , c. Use deep learning to build a deep learning algorithm network framework for image segmentation, d. Use the data set estimated by the local road adhesion coefficient to conduct end-to-end training for the deep learning ...

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Abstract

The invention discloses a local pavement adhesion coefficient estimation method based on image segmentation, and the method comprises the steps: 1, carrying out the offline pre-training of an image segmentation network, and specifically comprises the steps: a, collecting pavement images of different weather conditions through CARLA software, b, carrying out the local marking of the collected pavement images of different weather conditions, and forming a data set of local pavement adhesion coefficient estimation, c, building a deep learning algorithm network model of image segmentation by usinga deep learning means; and d, carrying out end-to-end training on a deep learning algorithm network framework of image segmentation by using a data set estimated by a local pavement adhesion coefficient. Step 2, acquiring a real-time road surface image, and estimating the local adhesion coefficient of the road surface in real time, specifically comprising: a, acquiring the real-time road surfaceimage by using a vehicle-mounted camera, b, classifying the real-time acquired image by using a pre-trained image segmentation network and positioning different categories to form a real-time road condition map, and c, performing local road surface adhesion coefficient estimation on the real-time road condition map according to the road surface type.

Description

technical field [0001] The invention belongs to the field of image segmentation, in particular to a method for estimating local road surface adhesion coefficient based on image segmentation. Background technique [0002] Better estimation of pavement adhesion coefficient has been a very challenging problem. The road surface adhesion coefficient not only affects the dynamics and braking performance of the vehicle, but also affects the operational stability of the vehicle when driving. Real-time and accurate identification of the road surface adhesion coefficient can greatly improve the safety and comfort of the vehicle during driving. . With the continuous advancement of the industry towards intelligence, the accurate estimation of the road surface adhesion coefficient will also greatly affect the path planning and decision-making of intelligent vehicles and robots and other systems. It can be seen that the high real-time and accurate estimation of the road surface adhesion...

Claims

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Application Information

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IPC IPC(8): G06K9/00G06K9/34G06K9/62G06N3/08
CPCG06N3/08G06V20/588G06V10/267G06F18/214G06F18/24
Inventor 王海蔡柏湘蔡英凤李祎承陈龙陈小波刘擎超孙晓强
Owner JIANGSU UNIV
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