1 citations · 1 across the 3 of their papers we have counts for
3 papers
PRISM: Privileged Probabilistic Latent Supervision for End-to-End Autonomous Driving Motion Planning
Volodymyr Havrylov, Faris Janjoš, Andreas Look +2
End-to-end autonomous driving (E2E AD) systems integrate perception, prediction, and planning into a single differentiable architecture. While these models show great promise, thei…
The WayHome: Long-term Motion Prediction on Dynamically Scaled
Kay Scheerer, Thomas Michalke, Juergen Mathes
One of the key challenges for autonomous vehicles is the ability to accurately predict the motion of other objects in the surrounding environment, such as pedestrians or other vehi…
Scaling Planning for Automated Driving using Simplistic Synthetic Data
Martin Stoll, Markus Mazzola, Maxim Dolgov +2
We challenge the perceived consensus that the application of deep learning to solve the automated driving planning task necessarily requires huge amounts of real-world data or high…