Mauro Salazar

Mauro Salazar


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 under a collaboration with the Ferrari Formula 1 team. He obtained a MSc and BSc in mechanical engineering from ETH Zürich in 2016 and 2013.

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.

In his free time, Mauro enjoys reading literature and philosophy, listening to good music, hiking in nature, and practicing yoga and meditation.

Awards:

  • Best Student Paper Award - IEEE Intelligent Transportation Systems Conference, 2018
  • ETH Medal for the Excellent Master Thesis
  • Outstanding Bachelor Award, Best Student at ETH Zürich
  • Excellence Scholarship & Opportunity Award at ETH Zürich

Currently at Eindhoven University of Technology

ASL Publications

  1. S. Wollenstein-Betech, M. Salazar, A. Houshmand, M. Pavone, I. C. Paschalidis, and C. G. Cassandras, “Routing and Rebalancing Intermodal Autonomous Mobility-on-Demand Systems in Mixed Traffic,” IEEE Robotics and Automation Letters, 2022. (Submitted)

    Abstract:

    @article{Wollenstein-BetechSalazarEtAl2022,
      author = {Wollenstein-Betech, S. and Salazar, M. and Houshmand, A. and Pavone, M. and Paschalidis, I. C. and Cassandras, C. G.},
      title = {Routing and Rebalancing Intermodal Autonomous Mobility-on-Demand Systems in Mixed Traffic},
      journal = {{IEEE Robotics and Automation Letters}},
      year = {2022},
      note = {Submitted},
      url = {https://drive.google.com/file/d/1ywtYsLluQ78UBl1bMW2prqioBrQ6fo0p/view},
      keywords = {sub},
      owner = {borisi},
      timestamp = {2021-09-19}
    }
    
  2. J. Luke, M. Salazar, R. Rajagopal, and M. Pavone, “Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting,” in Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Indianapolis, IN, 2021. (In Press)

    Abstract: Charging infrastructure is the coupling link between power and transportation networks, thus determining charging station siting is necessary for planning of power and transportation systems. While previous works have either optimized for charging station siting given historic travel behavior, or optimized fleet routing and charging given an assumed placement of the stations, this paper introduces a linear program that optimizes for station siting and macroscopic fleet operations in a joint fashion. Given an electricity retail rate and a set of travel demand requests, the optimization minimizes total cost for an autonomous EV fleet comprising of travel costs, station procurement costs, fleet procurement costs, and electricity costs, including demand charges. Specifically, the optimization returns the number of charging plugs for each charging rate (e.g., Level 2, DC fast charging) at each candidate location, as well as the optimal routing and charging of the fleet. From a case-study of an electric vehicle fleet operating in San Francisco, our results show that, albeit with range limitations, small EVs with low procurement costs and high energy efficiencies are the most cost-effective in terms of total ownership costs. Furthermore, the optimal siting of charging stations is more spatially distributed than the current siting of stations, consisting mainly of high-power Level 2 AC stations (16.8 kW) with a small share of DC fast charging stations and no standard 7.7kW Level 2 stations. Optimal siting reduces the total costs, empty vehicle travel, and peak charging load by up to 10%.

    @inproceedings{LukeSalazarEtAl2021,
      author = {Luke, J. and Salazar, M. and Rajagopal, R. and Pavone, M.},
      title = {Joint Optimization of Autonomous Electric Vehicle Fleet Operations and Charging Station Siting},
      booktitle = {{Proc. IEEE Int. Conf. on Intelligent Transportation Systems}},
      year = {2021},
      note = {In press},
      address = {Indianapolis, IN},
      month = sep,
      url = {http://arxiv.org/abs/2107.00165},
      keywords = {press},
      owner = {jthluke},
      timestamp = {2021-06-29}
    }
    
  3. S. Wollenstein-Betech, A. Houshmand, M. Salazar, M. Pavone, C. G. Cassandras, and I. C. Paschalidis, “Congestion-aware Routing and Rebalancing of Autonomous Mobility-on-Demand Systems in Mixed Traffic,” in Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Rhodes, Greece, 2020. (In Press)

    Abstract: This paper studies congestion-aware route-planning policies for Autonomous Mobility-on-Demand (AMoD) systems, whereby a fleet of autonomous vehicles provides on-demand mobility under mixed traffic conditions. Specifically, we first devise a network flow model to optimize the AMoD routing and rebalancing strategies in a congestion-aware fashion by accounting for the endogenous impact of AMoD flows on travel time. Second, we capture reactive exogenous traffic consisting of private vehicles selfishly adapting to the AMoD flows in a user-centric fashion by leveraging an iterative approach. Finally, we showcase the effectiveness of our framework with two case-studies considering the transportation sub-networks in Eastern Massachusetts and New York City. Our results suggest that for high levels of demand, pure AMoD travel can be detrimental due to the additional traffic stemming from its rebalancing flows, while the combination of AMoD with walking or micromobility options can significantly improve the overall system performance.

    @inproceedings{Wollenstein-BetechHoushmandEtAl2020,
      author = {Wollenstein-Betech, S. and Houshmand, A. and Salazar, M. and Pavone, M. and Cassandras, C. G. and Paschalidis, I. C.},
      title = {Congestion-aware Routing and Rebalancing of Autonomous Mobility-on-Demand Systems in Mixed Traffic},
      booktitle = {{Proc. IEEE Int. Conf. on Intelligent Transportation Systems}},
      year = {2020},
      note = {In press},
      address = {Rhodes, Greece},
      month = sep,
      url = {/wp-content/papercite-data/pdf/Wollenstein-Betech.ea.ITSC20.pdf},
      keywords = {presss},
      owner = {samauro},
      timestamp = {2020-07-02}
    }
    
  4. G. Zardini, N. Lanzetti, M. Salazar, A. Censi, E. Frazzoli, and M. Pavone, “On the Co-Design of AV-Enabled Mobility Systems,” in Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Rhodes, Greece, 2020.

    Abstract: The design of autonomous vehicles (AVs) and the design of AV-enabled mobility systems are closely coupled. Indeed, knowledge about the intended service of AVs would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management decisions. This calls for tools to study such a coupling and co-design AVs and AV-enabled mobility systems in terms of different objectives. In this paper, we instantiate a framework to address such co-design problems. In particular, we leverage the recently developed theory of co-design to frame and solve the problem of designing and deploying an intermodal Autonomous Mobility-on-Demand system, whereby AVs service travel demands jointly with public transit, in terms of fleet sizing, vehicle autonomy, and public transit service frequency. Our framework is modular and compositional, allowing one to describe the design problem as the interconnection of its individual components and to tackle it from a system-level perspective. To showcase our methodology, we present a real-world case study for Washington D.C., USA. Our work suggests that it is possible to create user-friendly optimization tools to systematically assess costs and benefits of interventions, and that such analytical techniques might gain a momentous role in policy-making in the future.

    @inproceedings{ZardiniEtAl2020,
      author = {Zardini, G. and Lanzetti, N. and Salazar, M. and Censi, A. and Frazzoli, E. and Pavone, M.},
      title = {On the Co-Design of AV-Enabled Mobility Systems},
      booktitle = {{Proc. IEEE Int. Conf. on Intelligent Transportation Systems}},
      year = {2020},
      address = {Rhodes, Greece},
      month = sep,
      url = {https://arxiv.org/abs/2003.04739},
      owner = {gzardini},
      timestamp = {2020-03-11}
    }
    
  5. F. Boewing, M. Schiffer, M. Salazar, and M. Pavone, “A Vehicle Coordination and Charge Scheduling Algorithm for Electric Autonomous Mobility-on-Demand Systems,” in American Control Conference, Denver, CO, United States, 2020. (In Press)

    Abstract: This paper presents an algorithmic framework to optimize the operation of an Autonomous Mobility-on-Demand system whereby a centrally controlled fleet of electric self-driving vehicles provides on-demand mobility. In particular, we first present a mixed-integer linear program that captures the joint vehicle coordination and charge scheduling problem, accounting for the battery level of the single vehicles and the energy availability in the power grid. Second, we devise a heuristic algorithm to compute near-optimal solutions in polynomial time. Finally, we apply our algorithm to realistic case studies for Newport Beach, CA. Our results validate the near optimality of our method with respect to the global optimum, whilst suggesting that through vehicle-to-grid operation we can enable a 100% penetration of renewable energy sources and still provide a high-quality mobility service.

    @inproceedings{BoewingSchifferEtAl2020,
      author = {Boewing, F. and Schiffer, M. and Salazar, M. and Pavone, M.},
      title = {A Vehicle Coordination and Charge Scheduling Algorithm for Electric Autonomous Mobility-on-Demand Systems},
      booktitle = {{American Control Conference}},
      year = {2020},
      note = {In press},
      address = {Denver, CO, United States},
      month = jun,
      url = {/wp-content/papercite-data/pdf/Boewing.ea.ACC20.pdf},
      keywords = {press},
      owner = {samauro},
      timestamp = {2020-03-19}
    }
    
  6. F. J. R. Verbruggen, M. Salazar, M. Pavone, and T. Hofman, “Joint Design and Control of Electric Vehicle Propulsion Systems,” in European Control Conference, St. Petersburg, Russia, 2020. (In Press)

    Abstract: This paper presents models and optimization methods for the design of electric vehicle propulsion systems. Specifically, we first derive a bi-convex model of a battery electric powertrain including the transmission and explicitly accounting for the impact of its components’ size on the energy consumption of the vehicle. Second, we formulate the energy-optimal sizing and control problem for a given driving cycle and solve it as a sequence of second-order conic programs. Finally, we present a real-world case study for heavy-duty electric trucks, comparing a single-gear transmission with a continuously variable transmission (CVT), and validate our approach with respect to state-of-the-art particle swarm optimization algorithms. Our results show that, depending on the electric motor technology, CVTs can reduce the energy consumption and the battery size of electric trucks between up to 10%, and shrink the electric motor up to 50%.

    @inproceedings{VerbruggenSalazarEtAl2019,
      author = {Verbruggen, F. J. R. and Salazar, M. and Pavone, M. and Hofman, T.},
      title = {Joint Design and Control of Electric Vehicle Propulsion Systems},
      booktitle = {{European Control Conference}},
      year = {2020},
      note = {In press},
      address = {St. Petersburg, Russia},
      month = may,
      url = {/wp-content/papercite-data/pdf/Verbruggen.Salazar.ea.ECC2020.pdf},
      keywords = {press},
      owner = {samauro},
      timestamp = {2020-02-27}
    }
    
  7. G. Zardini, N. Lanzetti, M. Salazar, A. Censi, E. Frazzoli, and M. Pavone, “Towards a Co-Design Framework for Future Mobility Systems,” in Annual Meeting of the Transportation Research Board, Washington D.C., United States, 2020.

    Abstract: The design of Autonomous Vehicles (AVs) and the design of AVs-enabled mobility systems are closely coupled. Indeed, knowledge about the intended service of AVs would impact their design and deployment process, whilst insights about their technological development could significantly affect transportation management decisions. This calls for tools to study such a coupling and co-design AVs and AVs-enabled mobility systems in terms of different objectives. In this paper, we instantiate a framework to address such co-design problems. In particular, we leverage the recently developed theory of co-design to frame and solve the problem of designing and deploying an intermodal Autonomous Mobility-on-Demand system, whereby AVs service travel demands jointly with public transit, in terms of fleet sizing, vehicle autonomy, and public transit service frequency. Our framework is modular and compositional, allowing to describe the design problem as the interconnection of its individual components and to tackle it from a system-level perspective. Moreover, it only requires very general monotonicity assumptions and it naturally handles multiple objectives, delivering the rational solutions on the Pareto front and thus enabling policy makers to select a solution through “political” criteria. To showcase our methodology, we present a real-world case study for Washington D.C., USA. Our work suggests that it is possible to create user-friendly optimization tools to systematically assess the costs and benefits of interventions, and that such analytical techniques might gain a momentous role in policy-making in the future.

    @inproceedings{ZardiniLanzettiEtAl2020,
      author = {Zardini, G. and Lanzetti, N. and Salazar, M. and Censi, A. and Frazzoli, E. and Pavone, M.},
      title = {Towards a Co-Design Framework for Future Mobility Systems},
      booktitle = {{Annual Meeting of the Transportation Research Board}},
      year = {2020},
      address = {Washington D.C., United States},
      month = jan,
      url = {https://arxiv.org/pdf/1910.07714.pdf},
      owner = {samauro},
      timestamp = {2019-10-22}
    }
    
  8. M. Salazar, M. Tsao, I. Aguiar, M. Schiffer, and M. Pavone, “A Congestion-aware Routing Scheme for Autonomous Mobility-on-Demand Systems,” in European Control Conference, Naples, Italy, 2019.

    Abstract: We study route-planning for Autonomous Mobility-on-Demand (AMoD) systems that accounts for the impact of road traffic on travel time. Specifically, we develop a congestion-aware routing scheme (CARS) that captures road-utilization-dependent travel times at a mesoscopic level via a piecewise affine approximation of the Bureau of Public Roads (BPR) model. This approximation largely retains the key features of the BPR model, while allowing the design of a real-time, convex quadratic optimization algorithm to determine congestion-aware routes for an AMoD fleet. Through a real-world case study of Manhattan, we compare CARS to existing routing approaches, namely a congestion-unaware and a threshold congestion model. Numerical results show that CARS significantly outperforms the other two approaches, with improvements in terms of travel time and global cost in the order of 20%.

    @inproceedings{SalazarTsaoEtAl2019,
      author = {Salazar, M. and Tsao, M. and Aguiar, I. and Schiffer, M. and Pavone, M.},
      title = {A Congestion-aware Routing Scheme for Autonomous Mobility-on-Demand Systems},
      booktitle = {{European Control Conference}},
      year = {2019},
      address = {Naples, Italy},
      month = nov,
      url = {/wp-content/papercite-data/pdf/Salazar.Tsao.ea.ECC19.pdf},
      owner = {samauro},
      timestamp = {2020-03-08}
    }
    
  9. J. Zgraggen, M. Tsao, M. Salazar, M. Schiffer, and M. Pavone, “A Model Predictive Control Scheme for Intermodal Autonomous Mobility-on-Demand,” in Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Auckland, New Zealand, 2019.

    Abstract: This paper presents a routing algorithm for intermodal Autonomous Mobility on Demand (AMoD) systems, whereby a fleet of self-driving cars provides on-demand mobility in coordination with public transit. Specifically, we present a time-variant flow-based optimization approach that captures the operation of an AMoD system in coordination with public transit. We then leverage this model to devise a model predictive control (MPC) algorithm to route customers and vehicles through the network with the objective of minimizing customers’ travel time. To validate our MPC scheme, we present a real-world case study for New York City. Our results show that servicing transportation demands jointly with public transit can significantly improve the service quality of AMoD systems. Additionally, we highlight the differences of our time-variant framework compared to existing mesoscopic, time-invariant models.

    @inproceedings{ZgraggenTsaoEtAl2019,
      author = {Zgraggen, J. and Tsao, M. and Salazar, M. and Schiffer, M. and Pavone, M.},
      title = {A Model Predictive Control Scheme for Intermodal Autonomous Mobility-on-Demand},
      booktitle = {{Proc. IEEE Int. Conf. on Intelligent Transportation Systems}},
      year = {2019},
      address = {Auckland, New Zealand},
      month = nov,
      url = {/wp-content/papercite-data/pdf/Zgraggen.Salazar.ea.ITSC19.pdf},
      owner = {samauro},
      timestamp = {2020-02-12}
    }
    
  10. M. Salazar, A. Houshmand, C. G. Cassandras, and M. Pavone, “Optimal Routing and Energy Management Strategies for Plug-in Hybrid Electric Vehicles,” in Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Auckland, New Zealand, 2019.

    Abstract: This paper presents eco-routing strategies for plug-in hybrid electric vehicles, whereby we jointly compute the routing and energy management strategy and the objective is a combination of travel time and energy consumption. Specifically, we first use Pontryagin’s principle to compute the optimal Pareto front in terms of achievable fuel and battery consumption for different types of road links. Second, we leverage these Pareto fronts to formulate a network flow optimization problem to compute the optimal routing and energy management strategy minimizing a combination of travel time and energy consumption. Finally, we present a real-world case-study for the Eastern Massachusetts highway sub-network. The proposed approach allows one to compute the optimal solution for different objectives, ranging from minimum time to minimum energy, revealing that by sacrificing little travel time significant improvements in fuel consumption can be achieved.

    @inproceedings{SalazarHoushmandEtAl2019,
      author = {Salazar, M. and Houshmand, A. and Cassandras, C. G. and Pavone, M.},
      title = {Optimal Routing and Energy Management Strategies for Plug-in Hybrid Electric Vehicles},
      booktitle = {{Proc. IEEE Int. Conf. on Intelligent Transportation Systems}},
      year = {2019},
      address = {Auckland, New Zealand},
      month = nov,
      url = {/wp-content/papercite-data/pdf/Salazar.Houshmand.ea.ITSC19.pdf},
      owner = {samauro},
      timestamp = {2020-02-12}
    }
    
  11. K. Solovey, M. Salazar, and M. Pavone, “Scalable and Congestion-aware Routing for Autonomous Mobility-on-Demand via Frank-Wolfe Optimization,” in Robotics: Science and Systems, Freiburg im Breisgau, Germany, 2019.

    Abstract: We consider the problem of vehicle routing for Autonomous Mobility-on-Demand (AMoD) systems, wherein a fleet of self-driving vehicles provides on-demand mobility in a given environment. Specifically, the task it to compute routes for the vehicles (both customer-carrying and empty travelling) so that travel demand is fulfilled and operational cost is minimized. The routing process must account for congestion effects affecting travel times, as modeled via a volume-delay function (VDF). Route planning with VDF constraints is notoriously challenging, as such constraints compound the combinatorial complexity of the routing optimization process. Thus, current solutions for AMoD routing resort to relaxations of the congestion constraints, thereby trading optimality with computational efficiency. In this paper, we present the first computationally-efficient approach for AMoD routing where VDF constraints are explicitly accounted for. We demonstrate that our approach is faster by at least one order of magnitude with respect to the state of the art, while providing higher quality solutions. From a methodological standpoint, the key technical insight is to establish a mathematical reduction of the AMoD routing problem to the classical traffic assignment problem (a related vehicle-routing problem where empty traveling vehicles are not present). Such a reduction allows us to extend powerful algorithmic tools for traffic assignment, which combine the classic Frank-Wolfe algorithm with modern techniques for pathfinding, to the AMoD routing problem. We provide strong theoretical guarantees for our approach in terms of near-optimality of the returned solution.

    @inproceedings{SoloveySalazarEtAl2019,
      author = {Solovey, K. and Salazar, M. and Pavone, M.},
      title = {Scalable and Congestion-aware Routing for Autonomous Mobility-on-Demand via Frank-Wolfe Optimization},
      booktitle = {{Robotics: Science and Systems}},
      year = {2019},
      address = {Freiburg im Breisgau, Germany},
      month = jun,
      url = {/wp-content/papercite-data/pdf/Solovey.Salazar.Pavone.RSS19.pdf},
      owner = {samauro},
      timestamp = {2019-02-02}
    }
    
  12. M. Tsao, D. Milojevic, C. Ruch, M. Salazar, E. Frazzoli, and M. Pavone, “Model Predictive Control of Ride-sharing Autonomous Mobility on Demand Systems,” in Proc. IEEE Conf. on Robotics and Automation, Montreal, Canada, 2019.

    Abstract: This paper presents a model predictive control (MPC) approach to optimize routes for Ride-sharing Autonomous Mobility-on-Demand (RAMoD) systems, whereby self-driving vehicles provide coordinated on-demand mobility, possibly allowing multiple customers to share a ride. Specifically, we first devise a time-expanded network flow model for RAMoD. Second, leveraging this model, we design a real-time MPC algorithm to optimize the routes of both empty and customer-carrying vehicles, with the goal of optimizing social welfare, namely, a weighted combination of customers’ travel time and vehicles’ mileage. Finally, we present a real-world case study for the city of San Francisco, CA, by using the microscopic traffic simulator MATSim. The simulation results show that a RAMoD system can significantly improve social welfare with respect to a single-occupancy AMoD system, and that the predictive structure of the proposed MPC controller allows it to outperform existing reactive ride-sharing coordination algorithms for RAMoD.

    @inproceedings{TsaoMilojevicEtAl2019,
      author = {Tsao, M. and Milojevic, D. and Ruch, C. and Salazar, M. and Frazzoli, E. and Pavone, M.},
      title = {Model Predictive Control of Ride-sharing Autonomous Mobility on Demand Systems},
      booktitle = {{Proc. IEEE Conf. on Robotics and Automation}},
      year = {2019},
      address = {Montreal, Canada},
      month = may,
      url = {/wp-content/papercite-data/pdf/Tsao.ea.ICRA19.pdf},
      owner = {samauro},
      timestamp = {2020-02-12}
    }
    
  13. M. Salazar, N. Lanzetti, F. Rossi, M. Schiffer, and M. Pavone, “Intermodal Autonomous Mobility-on-Demand,” IEEE Transactions on Intelligent Transportation Systems, Apr. 2019. (In Press)

    Abstract:

    @article{SalazarLanzettiEtAl2019,
      author = {Salazar, M. and Lanzetti, N. and Rossi, F. and Schiffer, M. and Pavone, M.},
      title = {Intermodal Autonomous Mobility-on-Demand},
      journal = {{IEEE Transactions on Intelligent Transportation Systems}},
      year = {2019},
      url = {https://ieeexplore.ieee.org/document/8894439},
      date = {2019-04-14},
      journaltitle = {Special Issue 21st IEEE Intelligent Transportation Systems Conference (ITSC 2018)},
      keywords = {press},
      owner = {samauro},
      timestamp = {2019-11-11}
    }
    
  14. M. Salazar, F. Rossi, M. Schiffer, C. H. Onder, and M. Pavone, “On the Interaction between Autonomous Mobility-on-Demand and the Public Transportation Systems,” in Proc. IEEE Int. Conf. on Intelligent Transportation Systems, Maui, Hawaii, 2018. (In Press)

    Abstract: In this paper we study models and coordination policies for intermodal Autonomous Mobility-on-Demand (AMoD), wherein a fleet of self-driving vehicles provides on-demand mobility jointly with public transit. Specifically, we first present a network flow model for intermodal AMoD, where we capture the coupling between AMoD and public transit and the goal is to maximize social welfare. Second, leveraging such a model, we design a pricing and tolling scheme that allows to achieve the social optimum under the assumption of a perfect market with selfish agents. Finally, we present a real-world case study for New York City. Our results show that the coordination between AMoD fleets and public transit can yield significant benefits compared to an AMoD system operating in isolation.

    @inproceedings{SalazarRossiEtAl2018,
      author = {Salazar, M. and Rossi, F. and Schiffer, M. and Onder, C. H. and Pavone, M.},
      title = {On the Interaction between Autonomous Mobility-on-Demand and the Public Transportation Systems},
      booktitle = {{Proc. IEEE Int. Conf. on Intelligent Transportation Systems}},
      year = {2018},
      note = {{Extended Version, Available} at \url{https://arxiv.org/abs/1804.11278}},
      address = {Maui, Hawaii},
      month = nov,
      url = {https://arxiv.org/pdf/1804.11278.pdf},
      keywords = {press},
      owner = {frossi2},
      timestamp = {2019-07-02}
    }