Carnegie Mellon University

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Optimization for Smart Mobility Systems: From Formula 1 Racing to Urban Mobility

Join us for a seminar with Mauro Salazar, a postdoctoral scholar at the Autonomous Systems Lab in the Department of Aeronautics and Astronautics at Stanford University. 

Date and Location

Friday, January 24, 2020, 11:00 AM - 12:00 PM
Scaife Hall 224 | 
Carnegie Mellon University | 5000 Forbes Ave. | Pittsburgh PA, 15213

 

Abstract

Nowadays mobility is facing challenges ranging from urban traffic to environmental pollution. The advent of new cyber-physical technologies such as autonomous driving, wireless communications and powertrain electrification might provide us with promising opportunities to face these challenges. Yet, how to successfully combine such technologies in order to design and deploy economically-viable, socially-inclusive and environmentally-friendly mobility solutions is still unclear.

In this context, this talk will show how Salazar's team leveraged optimization methods on research projects ranging from the single-vehicle level to the transportation system level. In particular, Salazar will first briefly present models and control algorithms devised during his PhD at ETH Zürich in cooperation with the Ferrari Formula 1 team to control the hybrid electric power unit of the F1 car - one of the most complex and fuel-efficient powertrains in the world - in a time-optimal fashion. Second, Salazar will give an overview on the work being done at the Autonomous Systems Laboratory at Stanford University on the broad topic of Autonomous Mobility-on-Demand - namely, a mobility system whereby self-driving cars provide on-demand mobility in coordinated fashion - including optimization models to analyse the societal benefits stemming from these new mobility paradigms, and operational control algorithms to route the vehicles and service travel demands in a socially-optimal fashion.

Biography

Mauro Salazar is a postdoctoral scholar at the Autonomous Systems Lab in the Department of Aeronautics and Astronautics at Stanford University. He received the Ph.D. degree in Mechanical Engineering from ETH Zürich in 2019. Mauro’s research is at the interface of control theory and optimization, and is aimed at the development of a comprehensive set of tools for the design, the deployment and the operation of future mobility systems. Specifically, his area of expertise includes optimal control theory, hybrid electric vehicles, and autonomous mobility-on-demand. Mauro received the Outstanding Bachelor Award and the Excellence Scholarship and Opportunity Award from ETH Zürich. His Master and PhD thesis were recognized with the ETH Medal. He was awarded the Best Student Paper award at the 2018 Intelligent Transportation Systems Conference.