Intelligent vehicle condition monitoring method based on deep learning
A technology of intelligent monitoring and deep learning, applied in the direction of vehicle components, input parameters of external conditions, circuits or fluid pipelines, etc., can solve the problem of not recognizing the lane markings, losing the ability to distinguish potential dangers, and increasing the driving danger of the vehicle. and other problems, to achieve a good early warning effect, reduce maintenance difficulty and time, and achieve good accuracy and performance.
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example 1
[0056] Example 1: For automotive fault diagnosis
[0057] Suppose the car is driving normally on the road and suddenly feels a loud noise from the exhaust pipe when the car is accelerating. The system analyzes the car’s condition through cloud data in real time, the display shows a fault, and the speaker prompts the intake manifold pressure sensor malfunction.
example 2
[0058] Example 2: Used in car auxiliary safety
[0059] Such as figure 2 As shown, assuming that the car is running on the road, the data of the ADAS sensor is initially processed by the information processing module, and the convolutional neural network is used to obtain figure 2 The obstacle detection results of (a) and (b), the detection results from left to right in the figure are the obstacle category, the horizontal distance between the obstacle and the vehicle, and the longitudinal distance between the obstacle and the vehicle, and m represents the length in meters; then The data is uploaded to the remote cloud platform, combined with the current speed of the vehicle and its relative distance to the vehicle, the remote cloud platform uses BP neural network to analyze that the vehicle is in good condition, and the obstacles detected above will not collide with the vehicle .
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