Plenary Speakers

Human-Centered Autonomous Mobility for Sustainable Transportation: A Symbiotic Systems Perspective

Prof. Mohan M. Trivedi
Distinguished Professor
Electrical and Computer Engineering
University of California San Diego

Abstract

Autonomous mobility and intelligent transportation systems are central to the future of sustainable transportation. Yet autonomy is still too often framed as a problem of building ever-smarter, vehicle-centric technologies. Persistent challenges in safety, scalability, and public trust point to a deeper limitation: intelligence has been isolated where it should be shared. This keynote challenges the prevailing paradigm and argues that sustainable autonomy will emerge only through human-centered, symbiotic transportation systems, in which cognition, perception, control, and interaction are seamlessly distributed across humans, machines, and infrastructure.

A central thesis of this keynote is that many of the hardest problems in autonomous mobility are not failures of algorithms, but failures of system boundaries—what we choose to sense, where we place intelligence, and how responsibility is shared between humans and machines. Drawing on principles from distributed, embodied, and embedded cognition, the talk reframes autonomous mobility as a systems-level challenge spanning robotics, artificial intelligence, human–computer interaction, and transportation engineering.

It highlights Distributed Interactive Sensor Arrays and multi-level semantic processing architectures as foundational enablers—scalable, wide-area, multimodal systems that provide persistent situational awareness beyond the limits of single-vehicle perception. These platforms support advances in multi-view and multimodal computer vision, activity and intent recognition, and machine learning for multi-agent trajectory and behavior prediction under real-world operational constraints.

The talk concludes by outlining open research challenges, including multimodal foundation models for traffic ecosystems, principled human–AI co-adaptation, continual learning under domain shift, and system-level evaluation frameworks essential for trustworthy autonomous mobility and a sustainable future.

Bio

Prof. Mohan Trivedi is a Distinguished Professor of Electrical and Computer Engineering at University of California San Diego and founding director of the Computer Vision and Robotics Research Laboratory (est. 1986), as well as the Laboratory for Intelligent and Safe Automobiles (LISA) (est. 2001). Trivedi and his team are pursuing research in intelligent vehicles, human-centered autonomous driving, machine perception, machine learning, human-robot interactivity, and advanced driver assistance. Trivedi has received Distinguished Alumnus awards from BITS-Pilani, India and Utah State University. He has given over 130 keynote/plenary talks. He regularly serves as a consultant to various industry and government agencies in the US and abroad. He frequently serves on panels dealing with technological, strategic, privacy, and ethical issues surrounding research areas he is involved in.

Trivedi has served as the Chair of the Robotics Technical Committee of the IEEE Computer Society, Governing Board member of the IEEE Systems, Man & Cybernetics, and IEEE ITSC societies. Trivedi is a Fellow of IEEE (life), SPIE, and IAPR.

Wireless Battery Management Systems for Electric Vehicle and Storage Applications

Prof. Chris Mi, Fellow IEEE & SAE
Distinguished Professor
Electrical and Computer Engineering
San Diego State University

Abstract

Battery management systems (BMS) are critical in maintaining the safety and longevity of
lithium-ion batteries in electric vehicles and energy storage system. Currently, wired BMS is
prevalent. However, wired BMS must connect each battery cell with the BMS via wires, resulting
in bucky wire harnessing, large voltage drops which impact the accuracy of measurement and
state calculations. In addition, wired BMS is heavy, costly, and contain more failure points. On
the contrary, wireless BMS remove the wiring in the BMS, hence, reduce weight, cost, and
failure points; increase measurement accuracy, reliability, and scalability. However, wireless
BMS, if not designed properly, will be suspectable to signal interference, latency, and EMI
issues. In this talk, we will review the state-of-the-art wireless BMS technology and demonstrate
how wireless BMS can help increase reliability and measurement accuracy and reduce weight
and cost.

Bio

Prof. Chris Mi is the Distinguished Professor of Electrical and Computer Engineering at San Diego State University. He is also the Director of the Caili &; Daniel Chang Center for Electric Drive Transportation at SDSU. Dr. Mi is a world-renowned expert in battery management. He has published five books, one book chapter, 226+ journal papers, 130 conference papers, and 20+ issued and pending patents. He served as Editor-in-Chief, Area Editor, Guest Editor, and Associate Editor of multiple IEEE Transactions and international journals, as well as the General Chair of over ten IEEE international conferences. Dr. Mi has won numerous awards, including the “Distinguished Teaching Award” and “Distinguished Research Award” from the University of Michigan-Dearborn, IEEE Region 4 “Outstanding Engineer Award,” IEEE Southeastern Michigan Section “Outstanding Professional Award,” and SAE “Environmental Excellence in Transportation (E2T) Award.” He is the recipient of three Best Paper Awards from IEEE Transactions on Power Electronics and the 2017 ECCE Student Demonstration Award. In 2019, he received the Inaugural IEEE Power Electronics Emerging Technology Award. In 2022, he received the Albert W. Johnson Research Lectureship and was named the Distinguished Professor, the highest honor given to an SDSU faculty member, and only one award is given each year. He received the 2023 IEEE PELS Vehicle and Transportation Systems Achievement Award, the IEEE Transactions on Industry Applications Best Paper Award, and the SDSU Innovator of the Year Award. In 2024, he received the prestigious Alumni Distinguished Faculty Award from SDSU. In 2026, he received the prestigious Wang Family Excellence Award from the California State University System.

Resilient and Sustainable Intelligent Transportation System Solutions

Prof. Matthew Barth

Esther & Daniel Hays Chair
Distinguished Professor

Electrical and Computer Engineering

University of California, Riverside

Abstract

A large number of Intelligent Transportation System (ITS) applications are being designed, developed, and deployed in order to greatly improve our transportation systems in terms of safety, mobility, and reducing environmental impacts. These benefits can be quantified by a variety of performance measures that are often cited in the literature. Unfortunately, many ITS applications are static in design and limited to specific traffic scenarios and conditions. ITS applications that can adapt to different conditions are much more resilient, and can be “tunable” for different societal needs will have much greater impact and versatility. In this presentation, we examine various sustainable ITS solutions we have studied over the years and focus on how we can make them more resilient under a variety of conditions. Often, this leads to identifying co-benefits and tradeoffs of current ITS applications and how they can be designed to have greater flexibility and resiliency when it comes to deployment.

Bio

Prof. Matthew Barth is the Esther & Daniel Hays Distinguished Professor at the Bourns College of Engineering, University of California-Riverside. He is part of the intelligent systems faculty in Electrical and Computer Engineering and conducts his research at the Center for Environmental Research and Technology (CE-CERT), UCR’s largest multi-disciplinary research center. Dr. Barth’s research focuses on Intelligent Transportation Systems, with the goal of improving environmental sustainability. His current research interests and teaching portfolio includes sustainable transportation, connected and automated vehicles, cooperative perception systems, advanced navigation, shared mobility, and vehicle electrification.