From the 1 of 12 linked papers with an AI index.
12 papers
Randomized routing strategies of fleets of CAVs may prove market efficient
Grzegorz Jamróz, Åukasz Gorczyca, RafaÅ Kucharski
The paper studies how randomized routing strategies for fleets of connected autonomous vehicles can improve market efficiency, especially when human driver attitudes vary, and prop…
PC3D: Zero-Shot Cooperation Across Variable Rosters via Personalized Context Distillation
Ahmet Onur Akman, RafaÅ Kucharski
Cooperative multi-agent reinforcement learning often assumes a fixed execution team, yet many decentralized systems must operate with varying numbers of active agents during deploy…
Generalising Travel Time Prediction To Varying Route Choices In Urban Networks
Åukasz Gorczyca, Kacper Drozd, MichaÅ Bujak +1
Previous methods that predict system-wide travel time, predominantly grounded in graph neural networks, remain limited to typical and recurring demand patterns. While they successf…
Market share maximizing strategies of CAV fleet operators may cause chaos in our cities
Grzegorz Jamróz, RafaŠKucharski, 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…
URB -- Urban Routing Benchmark for RL-equipped Connected Autonomous Vehicles
Ahmet Onur Akman, Anastasia Psarou, MichaÅ 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…
Equilibria in routing games with connected autonomous vehicles will not be strong, as exclusive clubs may form
RafaÅ 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…