Autonomous vehicle (AV) research and development has achieved a similar status in terms of money invested, societal excitement, and media coverage as space travel and exploration. At the same time, AV research is not rocket science; it is more complicated: while in itself, an AV is no more complex than a spacecraft, it must reliably interact and communicate with many other agents, particularly humans both inside and outside of the vehicle, much of it in a decentralized fashion. Hence, AVs, and their impact on us humans and our transportation systems, incur some of the most complicated science and engineering challenges that we shall face in the near future. At the same time, there is some disconnect across the various research communities: professional product development is highly opaque, and public expectations and media communications are frequently inaccurate or exaggerated.
This long program aims to address these problems by connecting research communities, bridging gaps between theory and practice, exposing software experts to hardware and vice versa, and bringing mathematicians, other scientists, and engineers together to shape the research and development agenda on AVs, both in terms of individual and holistic components.
Key mathematical themes in this program are:
• Robustness of machine learning
• Connecting micro and the macro scales
• Reinventing traffic flow theory in a non-local world
• Multi-agents systems, sparse controls, distributed leaders
• Fleet optimization and routing in a fully connected world
• Mathematics of societal impact of AVs
(University of California, Berkeley (UC Berkeley), CITRIS)
Paola Goatin (Institut National de Recherche en Informatique Automatique (INRIA))
Jana Kosecka (George Mason University)
Hani Mahmassani (Northwestern University)
Benedetto Piccoli (Rutgers University)
Benjamin Seibold (Temple University, Mathematics)
Daniel Work (Vanderbilt University)