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Alessandro Zanardi

7 accepted papers

2023

How Bad is Selfish Driving? Bounding the Inefficiency of Equilibria in Urban Driving Games

Alessandro Zanardi, Pier Giuseppe Sessa, Nando Käslin, Saverio Bolognani, Andrea Censi, Emilio Frazzoli
RA-L 2023

We consider the interaction among agents engaging in a driving task and we model it as general-sum game. This class of games exhibits a plurality of different equilibria posing the issue of equilibrium selection. While selecting the most efficient equilibrium (in term of social cost) is often imprac

Cited by 5Source ↗Scholar ↗
2022

Factorization of Dynamic Games over Spatio-Temporal Resources

Alessandro Zanardi, Saverio Bolognani, Andrea Censi, Florian Dorfler, Emilio Frazzoli
IROS 2022poster

Dynamic games feature a state-space complexity that scales superlinearly with the number of players. This makes this class of games often intractable even for a handful of players. We introduce the factorization process of dynamic games as a transformation leveraging the independence of players at e…

Cited by 7Source ↗Scholar ↗
2022

Posetal Games: Efficiency, Existence, and Refinement of Equilibria in Games With Prioritized Metrics

Alessandro Zanardi, Gioele Zardini, Sirish Srinivasan, Saverio Bolognani, Andrea Censi, Florian Dörfler +1
RA-L 2022

Modern applications require robots to comply with multiple, often conflicting rules and to interact with the other agents. We present Posetal Games as a class of games in which each player expresses a preference over the outcomes via a partially ordered set of metrics. This allows one to combine hie

Cited by 13Source ↗Scholar ↗
2021

Image Representation of a City and Its Taxi Fleet for End-To-End Learning of Rebalancing Policies

Joel Gächter, Alessandro Zanardi, Claudio Ruch, Emilio Frazzoli
ICRA 2021poster

In recent years, mobility on demand has experienced a major revival due to various ride-hailing companies entering the market. Competing in this field requires an efficient operation. Therefore, the applied policy, which cares for vehicle-to-customer assignment and vehicle repositioning, has to achi…

Cited by 1Source ↗Scholar ↗
2021

Urban Driving Games With Lexicographic Preferences and Socially Efficient Nash Equilibria

Alessandro Zanardi, Enrico Mion, Mattia Bruschetta, Saverio Bolognani, Andrea Censi, Emilio Frazzoli
RA-L 2021

We describe Urban Driving Games (UDGs) as a particular class of differential games that model the interactions and incentives of the urban driving task. The drivers possess a “communal” interest, such as not colliding with each other, but are also self-interested in fulfilling traffic rules and pers

Cited by 26Source ↗Scholar ↗
2019

Cross-Modal Learning Filters for RGB-Neuromorphic Wormhole Learning

Alessandro Zanardi, Andreas Jianhao Aumiller, Julian Zilly, Andrea Censi, Emilio Frazzoli
RSS 2019poster

Robots that need to act in an uncertain, populated, and varied world need heterogeneous sensors to be able to perceive and act robustly. For example, self-driving cars currently on the road are equipped with dozens of sensors of several types (lidar, radar, sonar, cameras, ...). All of this existing…

Cited by 18Source ↗PDF ↗Scholar ↗