9 citations · 13 across the 5 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…
Driving in Real Life with Inverse Reinforcement Learning
Tung Phan-Minh, Forbes Howington, Ting-Sheng Chu +10
In this paper, we introduce the first learning-based planner to drive a car in dense, urban traffic using Inverse Reinforcement Learning (IRL). Our planner, DriveIRL, generates a d…