128 citations · 137 across the 8 of their papers we have counts for
11 papers
TrajFlow: Multi-modal Motion Prediction via Flow Matching
Qi Yan, Brian Zhang, Yutong Zhang +8
Efficient and accurate motion prediction is crucial for ensuring safety and informed decision-making in autonomous driving, particularly under dynamic real-world conditions that ne…
Learning to Drive via Asymmetric Self-Play
Chris Zhang, Sourav Biswas, Kelvin Wong +5
Large-scale data is crucial for learning realistic and capable driving policies. However, it can be impractical to rely on scaling datasets with real data alone. The majority of dr…
MotionLM: Multi-Agent Motion Forecasting as Language Modeling
Ari Seff, Brian Cera, Dian Chen +6
Reliable forecasting of the future behavior of road agents is a critical component to safe planning in autonomous vehicles. Here, we represent continuous trajectories as sequences…
COOPERNAUT: End-to-End Driving with Cooperative Perception for Networked Vehicles
Jiaxun Cui, Hang Qiu, Dian Chen +2
Optical sensors and learning algorithms for autonomous vehicles have dramatically advanced in the past few years. Nonetheless, the reliability of today's autonomous vehicles is hin…
Contrastive Test-Time Adaptation
Dian Chen, Dequan Wang, Trevor Darrell +1
Test-time adaptation is a special setting of unsupervised domain adaptation where a trained model on the source domain has to adapt to the target domain without accessing source da…
Learning from All Vehicles
Dian Chen, Philipp Krähenbühl
In this paper, we present a system to train driving policies from experiences collected not just from the ego-vehicle, but all vehicles that it observes. This system uses the behav…