1 citations · 1 across the 2 of their papers we have counts for
3 papers
Mitigating Covariate Shift in Imitation Learning for Autonomous Vehicles Using Latent Space Generative World Models
Alexander Popov, Alperen Degirmenci, David Wehr +9
We propose the use of latent space generative world models to address the covariate shift problem in autonomous driving. A world model is a neural network capable of predicting an…
Planning-aligned Token Compression for Long-Context Autonomous Driving
Zhixuan Liang, Yuxiao Chen, Yurong You +12
Monolithic vision-action models represent an emerging paradigm in autonomous driving. However, this architecture produces token sequences that quickly exceed real-time computationa…
Data Scaling Laws for End-to-End Autonomous Driving
Alexander Naumann, Xunjiang Gu, Tolga Dimlioglu +7
Autonomous vehicle (AV) stacks have traditionally relied on decomposed approaches, with separate modules handling perception, prediction, and planning. However, this design introdu…