9 papers · 1 filter
Market share maximizing strategies of CAV fleet operators may cause chaos in our cities
Grzegorz Jamróz, Rafał Kucharski, David Watling
We study the dynamics and equilibria of a new kind of routing games, where players - drivers of future autonomous vehicles - may switch between individual (HDV) and collective (CAV…
Equilibria in routing games with connected autonomous vehicles will not be strong, as exclusive clubs may form
Rafał Kucharski, Anastasia Psarou, Natello Descormier
User Equilibrium is the standard representation of the so-called routing game in which drivers adjust their route choices to arrive at their destinations as fast as possible. Askin…
Autonomous vehicles need social awareness to find optima in multi-agent reinforcement learning routing games
Anastasia Psarou, Łukasz Gorczyca, Dominik Gaweł +1
Previous work has shown that when multiple selfish Autonomous Vehicles (AVs) are introduced to future cities and start learning optimal routing strategies using Multi-Agent Reinfor…
Impact of Collective Behaviors of Autonomous Vehicles on Urban Traffic Dynamics: A Multi-Agent Reinforcement Learning Approach
Ahmet Onur Akman, Anastasia Psarou, Zoltán György Varga +2
This study examines the potential impact of reinforcement learning (RL)-enabled autonomous vehicles (AV) on urban traffic flow in a mixed traffic environment. We focus on a simplif…
Wardropian Cycles make traffic assignment both optimal and fair by eliminating price-of-anarchy with Cyclical User Equilibrium for compliant connected autonomous vehicles
Michał Hoffmann, Michał Bujak, Grzegorz Jamróz +1
Connected and Autonomous Vehicles (CAVs) open the possibility for centralised routing with full compliance, making System Optimal traffic assignment attainable. However, as System…
Detection of coordinated fleet vehicles in route choice urban games. Part I. Inverse fleet assignment theory
Grzegorz Jamróz, Rafał Kucharski
Detection of collectively routing fleets of vehicles in future urban systems may become important for the management of traffic, as such routing may destabilize urban networks lead…
URB -- Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles
Ahmet Onur Akman, Anastasia Psarou, Michał Hoffmann +5
Connected Autonomous Vehicles (CAVs) promise to reduce congestion in future urban networks, potentially by optimizing their routing decisions. Unlike for human drivers, these decis…
RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles
Ahmet Onur Akman, Anastasia Psarou, Łukasz Gorczyca +3
RouteRL is a novel framework that integrates multi-agent reinforcement learning (MARL) with a microscopic traffic simulation, facilitating the testing and development of efficient…
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing
Anastasia Psarou, Ahmet Onur Akman, Łukasz Gorczyca +3
Autonomous vehicles (AVs), possibly using Multi-Agent Reinforcement Learning (MARL) for simultaneous route optimization, may destabilize traffic networks, with human drivers potent…