Semper in Motu: Transforming Mobility Through Learning and Control: 2024 Bode Prize Lecture

The transformation of the transport sector is continuously in motion. Through advances in sensing, connectivity, computing, and electrification, the control community has been and will continue to be actively engaged in the shaping of a sustainable and efficient infrastructure for moving people and goods. Although self-driving technologies have garnered significant attention, achieving widespread and safe deployment remains a challenge. Meanwhile, innovations are continuing to occur to optimize and improve the resilience of transport systems, highlighting the broader impact of control technology on mobility. This lecture explores intelligent transport and the influences of the emerging field of cyberphysical–human systems, using three case studies: 1) vehicle automation and occlusion, 2) traffic control using physics-informed machine learning, and 3) rollout-based planning for electric truck charging coordination. These case studies are used to illustrate how uncertainty can be represented and mitigated using safety-first reachable set computations, a mix of physics- and data-driven models, and stochastic forecasting with dynamic programming (DP). The article highlights joint work with students, postdoctoral researchers, and collaborators in academia and industry.