205 citations · 230 across the 4 of their papers we have counts for
4 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…
Importance is in your attention: agent importance prediction for autonomous driving
Christopher Hazard, Akshay Bhagat, Balarama Raju Buddharaju +5
Trajectory prediction is an important task in autonomous driving. State-of-the-art trajectory prediction models often use attention mechanisms to model the interaction between agen…
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…
One Thousand and One Hours: Self-driving Motion Prediction Dataset
John Houston, Guido Zuidhof, Luca Bergamini +6
Motivated by the impact of large-scale datasets on ML systems we present the largest self-driving dataset for motion prediction to date, containing over 1,000 hours of data. This w…