Showing cs.ROShow all
2 papers · 1 filter
cs.RO2025
TreeIRL: Safe Urban Driving with Tree Search and Inverse Reinforcement Learning
Momchil S. Tomov, Sang Uk Lee, Hansford Hendrago +14
We present TreeIRL, a novel planner for autonomous driving that combines Monte Carlo tree search (MCTS) and inverse reinforcement learning (IRL) to achieve state-of-the-art perform…
cs.RO2025
Lab2Car: A Versatile Wrapper for Deploying Experimental Planners in Complex Real-world Environments
Marc Heim, Francisco Suarez-Ruiz, Ishraq Bhuiyan +2
Human-level autonomous driving is an ever-elusive goal, with planning and decision making -- the cognitive functions that determine driving behavior -- posing the greatest challeng…