Toward Collective Intelligence for Resource-Constrained Autonomous Vehicles
Toward Collective Intelligence for Resource-Constrained Autonomous Vehicles
Speaker: Prof. Marco Levorato, Professor in the Computer Science department at the University of California, Irvine Date: Wednesday, February 12, 2025 Time: Social Hour: 6:00 p.m. Dinner: 6:30 p.m. Presentation: 7:15 p.m. Location: Lakeside Orange County Airport Hotel 7 Hutton Centre Drive, Santa Ana, CA, 92707-5794 714-751-2400 Please RSVP at https://www.eventbrite.com/e/toward-collective-intelligence-for-resource-constrained-autonomous-vehicles-tickets-1231738546149?aff=oddtdtcreator Cost: First 10 early-birds are free! After that, $20 for non-members with dinner, $10 for IEEE members with dinner, $5 for student-members with dinner, free for presentation only. For more information on current and future events, please visit Orange County ComSig chapter website at: http://comsig.chapters.comsoc.org Abstract The practical deployment of autonomous, semi-autonomous and teleoperated vehicles in contested environments faces several technical challenges connected to the scarce communication and computing resources available to these systems. On the one hand, small vehicles - e.g., expendable and attritable UxVs - have obvious hardware, energy, and memory constraints that limit their ability to execute complex mission logics. On the other hand, offloading the execution of these logics to compute-capable devices requires the transmission of information-rich signals over capacity limited and volatile wireless channels. Similarly, larger vehicles face challenges in processing and fusing the large volume of data produced by the many onboard sensors needed to make them mission-capable in a broad range of conditions, as well as in transporting them to remote operators and control centers. In this talk, I will provide an overview of the techniques and frameworks that my research group developed to allow flexible, efficient and resilient distributed neural computing for robotic perception and autonomous navigation. Our approaches deeply integrate system solutions and machine learning to obtain practical frameworks deployable on real-world hardware platforms and applications. Speaker Biography Marco Levorato is a Professor in the Computer Science department at the University of California, Irvine. He completed the PhD in Electrical Engineering at the University of Padova, Italy, in 2009. Between 2010 and 2012, he was a postdoctoral researcher Jointly at Stanford and the University of Southern California. Prof. Levorato’s research interests are focused on distributed computing over unreliable wireless systems, especially for autonomous vehicles and robotic applications. In this area of research, he has more than 170 papers in IEEE and ACM venues. His work received the best paper award at IEEE GLOBECOM (2012). He received the UC Hellman Foundation Award in 2016, the Dean mid-career research award in 2019, and the UCI Innovator Award in 2024. His research has been funded by the National Science Foundation, the Department of Defense, Intel and Cisco. In 2020-2021, he was the vice chair of the IEEE Technical Committee on Smart Grid Communications. He serves in the TPC of IEEE Infocom, IEEE Secon, IEEE Percom, IEEE ICDCS and ACM MobiHoc, is an editor of the IEEE/ACM Transactions on Networking, and was part of the organizing committee of several IEEE and ACM conferences. Contact: IEEE OC ComSig Chapter Chair lan.nguyen@linquest.com Lan Nguyen, Ph.D.