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 language||English (US)|
|Title of host publication||2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Proceedings|
|Publisher||Institute of Electrical and Electronics Engineers Inc.|
|State||Published - May 2020|
|Event||2020 IEEE Wireless Communications and Networking Conference, WCNC 2020 - Seoul, Korea, Republic of|
Duration: May 25 2020 → May 28 2020
|Name||IEEE Wireless Communications and Networking Conference, WCNC|
|Conference||2020 IEEE Wireless Communications and Networking Conference, WCNC 2020|
|Country/Territory||Korea, Republic of|
|Period||5/25/20 → 5/28/20|
Bibliographical noteFunding Information:
This work was in part supported by the Ministry National Key Research and Development Project under Grant 2017YFE0121400, Guandong Key R&D Project under Grant 2019B010153003, the open research fund of Key Laboratory of Wireless Sensor Network & Communication under Grant 2017003, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, and the National Science Foundation under Grants CNS-1932413 and CNS-1932139.
© 2020 IEEE.
- Environmental sensitivity
- decision fusion
- multi-vehicle cooperative perception
- vehicular network