34 citations · 135 across the 25 of their papers we have counts for
6 papers · 1 filter
STORM: Spatio-Temporal Reconstruction Model for Large-Scale Outdoor Scenes
Jiawei Yang, Jiahui Huang, Yuxiao Chen +10
We present STORM, a spatio-temporal reconstruction model designed for reconstructing dynamic outdoor scenes from sparse observations. Existing dynamic reconstruction methods often…
Closed-Loop Supervised Fine-Tuning of Tokenized Traffic Models
Zhejun Zhang, Peter Karkus, Maximilian Igl +4
Traffic simulation aims to learn a policy for traffic agents that, when unrolled in closed-loop, faithfully recovers the joint distribution of trajectories observed in the real wor…
Learning Multiple Initial Solutions to Optimization Problems
Elad Sharony, Heng Yang, Tong Che +3
Sequentially solving similar optimization problems under strict runtime constraints is essential for many applications, such as robot control, autonomous driving, and portfolio man…
System-Level Analysis of Module Uncertainty Quantification in the Autonomy Pipeline
Sampada Deglurkar, Haotian Shen, Anish Muthali +5
Modern autonomous systems with machine learning components often use uncertainty quantification to help produce assurances about system operation. However, there is a lack of conse…
LoRD: Adapting Differentiable Driving Policies to Distribution Shifts
Christopher Diehl, Peter Karkus, Sushant Veer +2
Distribution shifts between operational domains can severely affect the performance of learned models in self-driving vehicles (SDVs). While this is a well-established problem, pri…
DistillNeRF: Perceiving 3D Scenes from Single-Glance Images by Distilling Neural Fields and Foundation Model Features
Letian Wang, Seung Wook Kim, Jiawei Yang +7
We propose DistillNeRF, a self-supervised learning framework addressing the challenge of understanding 3D environments from limited 2D observations in outdoor autonomous driving sc…