Environmental Sensitivity Evaluation of Neural Networks in Unmanned Vehicle Perception Module

Yuru Li, Dongliang Duan, Chen Chen, Xiang Cheng, Liuqing Yang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

For autonomous driving of unmanned vehicles in intelligent transportation systems, multi-vehicle cooperative perception supported by vehicular networks can greatly improve the accuracy and reliability of the perception decisions. Currently, the perception decisions for a single vehicle are mostly provided by neural networks. Therefore, in order to fuse the perception decisions from multiple vehicles, the credibility of the neural network outputs needs to be studied. Among various factors, the environment is one of the most important affecting vehicles' perception decisions. In this paper, we propose a new evaluation criteria for the neural networks used in the perception module of unmanned vehicles. This criterion is termed as Environmental Sensitivity (ES), indicates the sensitivity of the network to environmental changes. We design an algorithm to quantitatively measure the ES value of different perception networks based on the extracted features. Experimental results show that our algorithm can well capture the sensitivity of the network in different environments and the ES values will be helpful to the subsequent decision fusion process.

Original languageEnglish (US)
Title of host publication2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728131061
DOIs
StatePublished - May 2020
Externally publishedYes
Event2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Seoul, Korea, Republic of
Duration: May 25 2020May 28 2020

Publication series

NameIEEE Wireless Communications and Networking Conference, WCNC
Volume2020-May
ISSN (Print)1525-3511

Conference

Conference2020 IEEE Wireless Communications and Networking Conference, WCNC 2020
CountryKorea, Republic of
CitySeoul
Period5/25/205/28/20

Keywords

  • decision fusion
  • Environmental sensitivity
  • multi-vehicle cooperative perception
  • vehicular network

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  • Cite this

    Li, Y., Duan, D., Chen, C., Cheng, X., & Yang, L. (2020). Environmental Sensitivity Evaluation of Neural Networks in Unmanned Vehicle Perception Module. In 2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings [9120722] (IEEE Wireless Communications and Networking Conference, WCNC; Vol. 2020-May). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/WCNC45663.2020.9120722