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

arXiv: https://arxiv.org/abs/2210.13064 · HTML: https://mlsanity.com/paper/2210.13064/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 16 of its 31 references that are in the map.
- First posted on arXiv 24 Oct 2022.

## Builds on (most cited first, 16 of 16)

- [Autonomous Agents Modelling Other Agents: A Comprehensive Survey and Open Problems](https://mlsanity.com/paper/1709.08071/) (2017)
- [SMARTS: Scalable Multi-Agent Reinforcement Learning Training School for Autonomous Driving](https://mlsanity.com/paper/2010.09776/) (2020)
- [Independent Policy Gradient for Large-Scale Markov Potential Games: Sharper Rates, Function Approximation, and Game-Agnostic Convergence](https://mlsanity.com/paper/2202.04129/) (2022)
- [Efficient Iterative Linear-Quadratic Approximations for Nonlinear Multi-Player General-Sum Differential Games](https://mlsanity.com/paper/1909.04694/) (2019)
- [On Improving Model-Free Algorithms for Decentralized Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2110.05707/) (2021)
- [Social Coordination and Altruism in Autonomous Driving](https://mlsanity.com/paper/2107.00200/) (2021)
- [Social Interactions for Autonomous Driving: A Review and Perspectives](https://mlsanity.com/paper/2208.07541/) (2022)
- [Interaction-aware Decision-making for Automated Vehicles using Social Value Orientation](https://mlsanity.com/paper/2207.05853/) (2022)
- [ALGAMES: A Fast Augmented Lagrangian Solver for Constrained Dynamic Games](https://mlsanity.com/paper/2104.08452/) (2021)
- [Inference-Based Strategy Alignment for General-Sum Differential Games](https://mlsanity.com/paper/2002.04354/) (2020)
- [Potential Game-Based Decision-Making for Autonomous Driving](https://mlsanity.com/paper/2201.06157/) (2022)
- [Potential iLQR: A Potential-Minimizing Controller for Planning Multi-Agent Interactive Trajectories](https://mlsanity.com/paper/2107.04926/) (2021)
- [Bounding Inefficiency of Equilibria in Continuous Actions Games using Submodularity and Curvature](https://mlsanity.com/paper/1903.00950/) (2019)
- [Newton's Method and Differential Dynamic Programming for Unconstrained Nonlinear Dynamic Games](https://mlsanity.com/paper/1906.09097/) (2019)
- [Learning Mixed Strategies in Trajectory Games](https://mlsanity.com/paper/2205.00291/) (2022)
- [First-Order Algorithms for Constrained Nonlinear Dynamic Games](https://mlsanity.com/paper/2001.01826/) (2020)

## Built on by (most cited first, 1 of 1)

- [Integrated Decision Making and Trajectory Planning for Autonomous Driving Under Multimodal Uncertainties: A Bayesian Game Approach](https://mlsanity.com/paper/2409.13993/) (2024)

## Abstract (arXiv)

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 impractical from a computational standpoint, in this work we study the (in)efficiency of any equilibrium players might agree to play. More specifically, we bound the equilibrium inefficiency by modeling driving games as particular type of congestion games over spatio-temporal resources. We obtain novel guarantees that refine existing bounds on the Price of Anarchy (PoA) as a function of problem-dependent game parameters. For instance, the relative trade-off between proximity costs and personal objectives such as comfort and progress. Although the obtained guarantees concern open-loop trajectories, we observe efficient equilibria even when agents employ closed-loop policies trained via decentralized multi-agent reinforcement learning.