Automatic driving method based on image recognition

Abstract

The invention discloses an automatic driving method based on image recognition, which comprises an image shooting module, an image recognition processing module, a control module and an early warning module, and comprises the following steps: S1, acquiring a live image of a driving direction through the image shooting module; s2, the live image in the S1 is analyzed and processed through an image recognition processing module, then a speed limit identifier is recognized, and a speed limit value is obtained; s3, the current vehicle degree is compared with the speed limit value, whether danger exists or not is judged, and the actual situation is displayed in real time through visual equipment; s4, when danger occurs, the automobile is controlled to conduct deceleration operation, and driving safety is ensured; otherwise, the vehicle runs normally. The real-time image of the driving direction is obtained through the image shooting module, the requirement for assisting automatic driving of the vehicle can be well met, and meanwhile when driving potential safety hazards exist in the vehicle, the safety performance of automatic driving can be greatly improved.

Description

technical field [0001] The invention relates to the technical field of automatic driving, and specifically provides an automatic driving method based on image recognition. [0002] technical background [0003] With the continuous development of artificial intelligence, image recognition technology has gradually replaced manual work, and has gradually been applied to the field of vehicle automatic driving to assist vehicle automatic driving, such as identifying traffic signs in images through image recognition technology to guide vehicles Automatically adjust the driving state. [0004] At present, since the deep learning algorithm in image recognition technology is established on the basis of large-scale data training, the efficiency and accuracy of image recognition technology mainly depend on the data samples used in the training process. [0005] The existing way of making data samples is usually to manually go out to take photos with target objects, and classify the pho...

AI Technical Summary

Problems solved by technology

[0006] However, in the prior art, classification by manual shooting and manual labeling is labor-intensive and inefficient, and it is difficult to cover target objects in various complex scenes with manually produced sample images of target objects, that is to say , the coverage rate of the sample image of the target object cannot meet the needs of the automatic driving of the auxiliary vehicle.

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