基于ResNet和Chan算法的行车环境声音感知
Sound Perception of Driving Environment Based on ResNet and Chan Algorithm
朱振飞 1葛动元 1姚锡凡2
作者信息
- 1. 广西科技大学机械与汽车工程学院,广西 柳州 545006
- 2. 华南理工大学机械与汽车工程学院,广东 广州 510640
- 折叠
摘要
随着自动驾驶技术的飞快发展和道路交通安全的要求,车辆对环境信息感知的要求也越来越高.道路声音的感知是自动驾驶走向成熟的重要组成部分,具有广泛应用前景.利用ResNet18 网络模型对特定声学事件进行识别,给数据集添加不同方差的噪音以模拟真实行车环境,并联合多层感知机模型和Chan算法进行定位,提出了一种Net-Chan定位方法,将时延估计值输入网络模型.通过Chan算法解算得到最终目标位置,实现了自动驾驶车辆行车环境的声音感知,具有较高的识别准确度,提高了定位精度和抗噪性,可作为视觉系统在受环境限制时的环境信息的收集补充与融合.
Abstract
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.
关键词
自动驾驶/残差网络/声音识别/声音定位Key words
Autopilot/Residual networks/Sound recognition/Sound localisation引用本文复制引用
出版年
2024
NETL
NSTL
万方数据