Research on Safety Early Warning Technology for Road Sections with Poor Sight Distance based on Acoustic Signals

DOI

10.2991/978-94-6463-514-0_39How to use a DOI?

Keywords

Traffic safety; Vehicle identification; MFCC; Improved BP algorithm

Abstract

In order to study the technical issues of traffic safety early warning on roads with poor sight distance, we first collect the sounds of vehicles running on the road, select representative vehicle sounds as recognition objects, pre-emphasize the vehicle sound signals, add windows into frames, and calculate the power spectrum. Input the Mel filter bank to obtain the MFCC of the vehicle sound signal and construct a feature vector with its characteristic values; then the BP neural network algorithm is improved to classify and identify the vehicle signal, thereby achieving the purpose of vehicle identification. Experiments show that the accuracy of the proposed voice recognition technology reaches more than 90%. This technology can be applied to road sections with poor sight distance to identify passing vehicles.

Copyright

© 2024 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Xiaonan Cheng
AU  - Xin Chen
AU  - Jian Li
AU  - Jielong Song
AU  - Xiankang Tang
PY  - 2024
DA  - 2024/09/28
TI  - Research on Safety Early Warning Technology for Road Sections with Poor Sight Distance based on Acoustic Signals
BT  - Proceedings of the 2024 7th International Symposium on Traffic Transportation and Civil Architecture (ISTTCA 2024)
PB  - Atlantis Press
SP  - 392
EP  - 399
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-514-0_39
DO  - 10.2991/978-94-6463-514-0_39
ID  - Cheng2024
ER  -