Mixed-Autonomy Mobility: Scalable Learning and Optimization
How will self-driving cars change urban mobility? This talk describes contributions in machine learning and optimization critical for enabling mixed-autonomy mobility, the gradual and complex integration of automated vehicles into the existing transportation system. The talk first explores and quantifies the potential impact of a small fraction of automated vehicles on low-level traffic flow dynamics such as traffic congestion, using novel techniques in model-free deep reinforcement learning. Second, the talk presents generic reinforcement learning techniques for improved variance reduction, developed for large-scale control systems such as traffic networks and robotic manipulation. To anchor this work in a broader mobility planning context, the coordination of automated vehicles relies on accurate traffic flow sensing. To this end, a new convex optimization method for cellular network measurements from AT&T for all of California is introduced to address a flow estimation problem previously believed to be intractable. Finally, automated vehicles are expected to increase transportation demand through a phenomenon called induced demand. To address this, joint work with Microsoft Research is presented, which provides theoretical justification for the application of widely used clustering algorithms to ridesharing problems, designed to mitigate the strain on existing infrastructure. Together, these contributions demonstrate, through principled learning and optimization methods, that a small number of vehicles and sensors can be harnessed for significant impact on urban mobility.
Cathy Wu is a PhD candidate in machine learning in Electrical Engineering and Computer Sciences (EECS) at UC Berkeley, the Berkeley Artificial Intelligence Research lab, Berkeley DeepDrive, California PATH, and the Berkeley RISELab. She is interested in developing principled computational tools to enable reliable and complex decision-making for critical societal infrastructure, such as transportation systems. Cathy received her Masters and Bachelors degrees in EECS from MIT. She is the recipient of several fellowships including the NSF graduate fellowship, the Berkeley Chancellor's fellowship, the NDSEG fellowship, and the Dwight David Eisenhower graduate fellowship. Her work was acknowledged by several awards, including the 2016 IEEE ITSC Best Paper Award and the 2017 ITS Outstanding Graduate Student Award. Her leadership, in particular as the Research Lead of the Learning Traffic Team at Berkeley, was recognized by numerous awards and invitations, such as the 2017 IEEE Leaders Summit and multiple NSF early-career investigator workshops on cyber-physical systems. Throughout her career, Cathy has collaborated or interned broadly across fields, including civil engineering, mechanical engineering, urban planning, and public policy, and institutions, including OpenAI, Microsoft Research, the Google Self-Driving Car Team, AT&T, Facebook, and Dropbox. As the founder and Chair of the Interdisciplinary Research Initiative within the ACM Future of Computing Academy, she is actively working to create international programs to further enable and support interdisciplinary research in computing.