4 papers
Explainable deep learning improves human mental models of self-driving cars
Eoin M. Kenny, Akshay Dharmavaram, Sang Uk Lee +6
Self-driving cars increasingly rely on deep neural networks to achieve human-like driving. The opacity of such black-box planners makes it challenging to accurately anticipate when…
Reason to Play: Behavioral and Brain Alignment Between Frontier LRMs and Human Game Learners
Botos Csaba, Sreejan Kumar, Austin Tudor David Andrews +6
Humans rapidly learn abstract knowledge when encountering novel environments and flexibly deploy this knowledge to guide efficient and intelligent action. Can modern AI systems lea…
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…
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…