A method and device for detecting a driving area
A driving area and equipment technology, applied in the field of automatic driving, can solve problems such as large environmental impact, inability to accurately detect the driving area, inability to obtain effective and accurate three-dimensional distance information, etc., to achieve good ease of use and robustness. strong effect
Active Publication Date: 2019-07-05
深兰人工智能芯片研究院(江苏)有限公司
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[0005] The present invention provides a method and equipment for detecting a driving area, which is used to solve the problem in the prior art that the current driving area detection based on a camera sensor cannot obtain effective and accurate three-dimensional distance information, and is greatly affected by the environment and cannot accurately detect the driving area. regional problems
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[0068] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments . Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.
[0069] The following is an explanation of some words that appear in the text:
[0070] 1. The term "and / or" in the embodiment of the present invention describes the association relationship of associated objects, indicating that there may be three relationships, for example, A and / or B, which may mean: A exists alone, A and B exist simultaneously, and There are three cases of B. The character " / " generally indicates that the contextual ob...
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The invention discloses a method and equipment for detecting a driving area, which relates to the technical field of automatic driving, and is used to solve the problem that the current driving area detection cannot obtain accurate three-dimensional distance information and accurately detect the driving area. The method of the invention includes: According to the average reflection intensity and height coding features of the grid in the bird's-eye view feature map, the first feature information of the road surface points and the second feature information of the road shoulder points in the bird's-eye view feature map are determined through the neural network segmentation model, where the bird's-eye view feature map is the pair of points The cloud map is obtained by rasterization; according to the first feature information, the road point corresponding to the road point in the bird's-eye view feature map is determined in the point cloud map, and the road point corresponding to the bird's-eye view feature in the point cloud map is determined according to the second feature information The shoulder point corresponding to the shoulder point in the figure; the road surface point and the shoulder point in the point cloud map are carried out geometric model fitting to determine the driving area, because the present invention adopts the method of deep learning to detect the driving area, the accuracy is high.
Description
technical field [0001] The invention relates to the technical field of automatic driving, in particular to a method and equipment for detecting a driving area. Background technique [0002] In recent years, with the rapid development of the automobile industry, traffic accidents have become a global problem. It is estimated that the number of deaths and injuries in traffic accidents in the world exceeds more than 500,000 people every year. Born for human driving applications. In order to perceive environmental information in real time and reliably, unmanned vehicles are equipped with various active and passive sensors, including cameras, lidar, millimeter-wave radar and GPS (Global Positioning System, Global Positioning System). One of the key parts in technology. [0003] Image-based driving area detection technology is mainly based on camera sensors. Through image and video analysis technology, such as image pixel-level semantic segmentation technology, the pixel points ...
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IPC IPC(8): G06K9/32G06K9/34G06K9/62
Inventor 陈海波
Owner 深兰人工智能芯片研究院(江苏)有限公司