16 citations · 47 across the 4 of their papers we have counts for
5 papers
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…
Multimodal Trajectory Prediction Conditioned on Lane-Graph Traversals
Nachiket Deo, Eric M. Wolff, Oscar Beijbom
Accurately predicting the future motion of surrounding vehicles requires reasoning about the inherent uncertainty in driving behavior. This uncertainty can be loosely decoupled int…
NuPlan: A closed-loop ML-based planning benchmark for autonomous vehicles
Holger Caesar, Juraj Kabzan, Kok Seang Tan +6
In this work, we propose the world's first closed-loop ML-based planning benchmark for autonomous driving. While there is a growing body of ML-based motion planners, the lack of es…
Motion Prediction using Trajectory Sets and Self-Driving Domain Knowledge
Freddy A. Boulton, Elena Corina Grigore, Eric M. Wolff
Predicting the future motion of vehicles has been studied using various techniques, including stochastic policies, generative models, and regression. Recent work has shown that cla…
CoverNet: Multimodal Behavior Prediction using Trajectory Sets
Tung Phan-Minh, Elena Corina Grigore, Freddy A. Boulton +2
We present CoverNet, a new method for multimodal, probabilistic trajectory prediction for urban driving. Previous work has employed a variety of methods, including multimodal regre…