Sound Perception of Driving Environment Based on ResNet and Chan Algorithm
With the rapid development of autonomous driving technology and the increasing demand for road traffic safety,vehicles have higher and higher requirements for the perception of environmental information.The per-ception of road sound is an important part of the maturity of autonomous driving and has a wide application prospect.In this paper,ResNet18 network model is used to identify specific acoustic events,noise with different variances is added to the data set to simulate the real driving environment,and multi-layer perceptron model and Chan algorithm are combined for positioning,a Net-Chan positioning method is proposed,and the time delay estimate is input into the network model.By using the Chan algorithm to calculate the final target position,the sound perception of the driving environment of autonomous vehicles has been achieved,with high recognition accuracy,improved positioning accuracy and noise resistance.It can be used as a supplement and fusion of environmental information for visual systems when limited by the environment.
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