Obstacle recognition method and device, storage medium and electronic device

Abstract

The invention discloses an obstacle recognition method and device, a storage medium and an electronic device. The obstacle recognition method comprises the steps: in the automatic driving process of atarget vehicle, obtaining point cloud data, wherein the point cloud data are obtained by scanning the driving environment of the target vehicle through a sensor on the target vehicle; converting thepoint cloud data into a first image for representing a driving environment; and identifying a first object in the driving environment from the first image through a first neural network model, the first neural network model being obtained by training a second neural network model by using a second image marked with a second object, and the first object and the second object being movable obstacles. According to the invention, the technical problem of low obstacle recognition accuracy in the prior art is solved.

Description

technical field [0001] The present invention relates to the field of automatic driving, in particular, to an obstacle identification method and device, a storage medium, and an electronic device. Background technique [0002] Self-driving cars (full name in English are Autonomous vehicles or Self-piloting automobiles), also known as driverless cars, computer-driven cars, or wheeled mobile robots, are intelligent cars that realize unmanned driving through computer systems. With decades of history, the beginning of the 21st century showed a trend close to practicality. [0003] Self-driving cars can rely on artificial intelligence, visual computing, radar, monitoring devices and global positioning systems to work together, allowing computers to automatically and safely operate motor vehicles without any active human operation. In the process of automatic driving of the car, obstacles such as pedestrians and bicycles can be detected through laser radar, such as collecting poin...

AI Technical Summary

Problems solved by technology

[0004] In the fields of autonomous driving and semi-autonomous driving, when using the above scheme for obstacle detection, it is necessary to manually design relevant features and corresponding thresholds when analyzing point cloud data, and pedestrians and cyclists have great differences in the shape of laser point clouds. , it is difficult to find direct and effective features and corresponding thresholds, so the accuracy of obstacle recognition is low

[0005] For the above problems, no effective solution has been proposed

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