5 papers
SHIELD: Secure Hypernetworks for Incremental Expansion Learning Defense
Patryk Krukowski, Åukasz Gorczyca, Piotr Helm +2
Continual learning under adversarial conditions remains an open problem, as existing methods often compromise either robustness, scalability, or both. We propose a novel framework…
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…
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…
Collaboration Between the City and Machine Learning Community is Crucial to Efficient Autonomous Vehicles Routing
Anastasia Psarou, Ahmet Onur Akman, Åukasz Gorczyca +3
Autonomous vehicles (AVs), possibly using Multi-Agent Reinforcement Learning (MARL) for simultaneous route optimization, may destabilize traffic networks, with human drivers potent…
RouteRL: Multi-agent reinforcement learning framework for urban route choice with autonomous vehicles
Ahmet Onur Akman, Anastasia Psarou, Åukasz 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…