LDW false and omitted alarm test method and system based on convolutional neural network

Abstract

The invention discloses a LDW false and omitted alarm test method based on a convolutional neural network (CNN). The method comprises steps of S1, disposing a camera; S2, setting a maximum lateral distance L, and averagely discretizing the same into n categories; S3, acquiring a real-time image A, inputting the same to a deep CNN model, calculating the actual distance di of a lane line; S4, determining whether a LDW system has false or omitted alarms; and S5, obtaining the misoperation rate of the LDW system. A test system comprises an image acquisition device, an onboard data acquisition mechanism, an analyzer, and an operation processor. The image acquisition device is connected to the analyzer, and the operation processor is connected to the analyzer and the onboard data acquisition mechanism. The method is easy to operate, high in recognition speed and high in recognition precision, applicable to the lanes in various road conditions. The test system can be just provided with the image acquisition device, the onboard data acquisition mechanism, the analyzer and the operation processor in the simplest manner, can fully automatically identify deviations without an extra lane linemark ruler.

Description

technical field [0001] The invention relates to the technical field of automobile driving alarm system testing, in particular to a convolutional neural network-based LDW false-missing-alarm testing method and testing system. Background technique [0002] Lane departure warning system (LDW) refers to the driving assistance system that the system gives the driver an alarm when the vehicle is about to deviate from the predetermined driving track. Especially in today's driverless cars, LDW can play an important role in the direction of the car. Therefore, The correct rate of LDW is very important to the safe driving of the car. [0003] Existing LDW tests mostly use manual testing for false positives and false negatives. Human eyes are not accurate enough in judging lane departure, and the test results may not necessarily meet the standard; and some electronic road condition recognition test devices are also based on The test is carried out on roads with road scales, but it can...

AI Technical Summary

Problems solved by technology

[0003] Existing LDW tests mostly use manual testing for false positives and false negatives. Human eyes are not accurate enough in judging lane departure, and the test results may not necessarily meet the standard; and some electronic road condition recognition test devices are also based on Tested on roads with road scales, it cannot be applied to roads without scales

[0004] Disadvantages of the existing technology: lack of a method for completely autonomously identifying the distance from the vehicle to the sideline and judging LDW false negatives

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