Robust Estimation of Connected Automated Vehicles While Performing Cooperative Tasks in Presence of Malicious Agents
This research focuses on the identification and mitigation of malicious vehicles in cooperative tasks between automated connected vehicles. The approach aims to design estimators which employ the number and diversity of sensors that autonomous vehicles are equipped with in order to enrich the knowledge of the surrounding environment of each vehicle in the wireless communication network range. Since an event-triggered communication network is considered to increase the overall performance of the communication channels, the estimators have to be designed to take into account aperiodic and asynchronous measurements.
How to Cite
This work is licensed under a Creative Commons Attribution 3.0 Unported License.
The Prognostic and Health Management Society advocates open-access to scientific data and uses a Creative Commons license for publishing and distributing any papers. A Creative Commons license does not relinquish the author’s copyright; rather it allows them to share some of their rights with any member of the public under certain conditions whilst enjoying full legal protection. By submitting an article to the International Conference of the Prognostics and Health Management Society, the authors agree to be bound by the associated terms and conditions including the following:
As the author, you retain the copyright to your Work. By submitting your Work, you are granting anybody the right to copy, distribute and transmit your Work and to adapt your Work with proper attribution under the terms of the Creative Commons Attribution 3.0 United States license. You assign rights to the Prognostics and Health Management Society to publish and disseminate your Work through electronic and print media if it is accepted for publication. A license note citing the Creative Commons Attribution 3.0 United States License as shown below needs to be placed in the footnote on the first page of the article.
First Author et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.