Autonomous vehicles are now entering our roadways, and together with connectivity, electrification, and the sharing economy, they will fundamentally change how our society will transport people and goods in the future. Recent technological and engineering advances have enabled developers to achieve impressive feats in the autonomous systems sector; and at the same time there are critical gaps in the understanding of learning-based safety-critical systems, human-cyber-physical systems interactions, the resulting transportation system-level consequences, and the mathematical foundations needed to address those challenges. This workshop brings together experts in cyber-physical systems, machine learning, transportation engineering, and applied mathematics, both from academia and from industry, to help bridge the technical gaps and to facilitate exchange and collaboration across disciplinary boundaries.
(University of California, Berkeley (UC Berkeley), CITRIS)
Paola Goatin (Institut National de Recherche en Informatique Automatique (INRIA))
Jana Kosecka (George Mason University)
Benedetto Piccoli (Rutgers University)
Benjamin Seibold (Temple University, Mathematics)
Daniel Work (Vanderbilt University)