An automatic driving decision-making method and system based on a switch-type deep learning network model

Abstract

The invention discloses an automatic driving decision-making method based on a switch-type deep learning network model, which includes: collecting the driving environment data of the vehicle under each navigation command through a plurality of cameras; step S11, the collected driving environment data , and the real-time navigation instructions are imported into the pre-trained and optimized automatic driving decision-making module, the automatic driving decision-making module adopts a switch-type deep learning network model, and the switch-type deep learning network model includes a multi-level convolutional neural network (CNN) layer, feature selection layer, long-short-term memory neural network (LSTM) layer and output layer; Step S12, described automatic driving decision-making module outputs steering wheel angle and vehicle according to the type of driving environment data and real-time navigation instruction inputted Desired driving speed to control the vehicle to realize automatic driving. The invention also discloses a corresponding system. The present invention can select and activate different characteristic switches according to different driving instructions, and can realize automatic driving under complex road conditions.

Description

technical field [0001] The invention belongs to the field of automobile automatic driving, and relates to an automatic driving decision-making method and system based on a switch type deep learning network model. Background technique [0002] The method of using deep learning to realize automatic driving of vehicles is the cutting-edge automatic driving algorithm model in the industry. The general idea is to design a deep learning network, then use the original image collected by the sensor as the input of the deep learning network, and then output operations such as braking, acceleration, and steering through the network as outputs, and then train the deep learning network. The advantage is that the model can respond directly to the sensory input without the intervention of humans writing rules. This deep learning autonomous driving technology means that as long as people provide enough training data, the system can automatically learn driving skills. [0003] But in some...

AI Technical Summary

Problems solved by technology

However, the existing deep learning automatic driving system uses the original image as input and outputs the steering wheel angle. It can only drive in a single lane, and cannot complete driving tasks such as intersections, ramps, and lane changes.

Moreover, the existing deep learning automatic driving system only uses the steering wheel angle as the output, without controlling the brake and accelerator, and the achieved automatic driving ability is relatively weak.

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