Automatic driving drivable area detection method and system and vehicle

Abstract

The invention provides a method for detecting a drivable area of an automatic driving vehicle, and the method comprises the steps: obtaining original point cloud information through employing a laserradar, and carrying out the coordinate conversion; longitudinally layering the point cloud information to filter out ground point cloud and impurities; longitudinally increasing the virtual point cloud according to the obtained point cloud distance resolution to obtain second point cloud information; mapping the point cloud into a polar coordinate grid, judging whether the point cloud exists in the grid or not, if not, adding a virtual point as point cloud data, and selecting a point, closest to the vehicle, in the grid as a candidate point. According to the method, the detection of the drivable area can be realized under the condition of weak light, and meanwhile, the obtained boundaries are all reliable boundaries.

Description

Technical field: [0001] The invention relates to the technical field of automatic detection, in particular to a method, device and system for detecting a drivable area based on a road surface image. Background technique: [0002] In recent years, with the rapid development of the automobile industry, traffic accidents have become a global problem. Therefore, unmanned driving applications integrating automatic control, artificial intelligence, pattern recognition and other technologies have emerged. In the field of unmanned driving, drivable area detection is one of the key parts of unmanned driving technology. [0003] In the prior art, a monocular solution based on deep learning is mainly used to detect the drivable area. Since the monocular solution based on deep learning inevitably needs to use GPU, real-time detection cannot be performed, and due to the lack of chip platforms and cost constraints, no products using this technology have been released yet. In addition, f...

AI Technical Summary

Problems solved by technology

Since the monocular solution based on deep learning inevitably uses the GPU, real-time detection cannot be performed, and due to the lack of chip platforms and cost constraints, there are currently no products using this technology.

In addition, for the monocular solution based on deep learning, the more complex the model and the better the training data, the better the detection effect will be, which will easily lead to slow detection speed and strong data dependence

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