28 citations · 136 across the 33 of their papers we have counts for
7 papers · 1 filter
Guiding End-to-End Driving Models with Endpoint-Constrained Trajectory Optimization
Brayden Zhang, Mahsa Golchoubian, Igor Gilitschenski +2
End-to-end driving policies are commonly trained through open-loop behavior cloning, yet they must ultimately operate in closed-loop when deployed on a vehicle, creating a fundamen…
OPTED: On-Policy Fine-Tuning for End-to-End Driving using a Render-Free Teacher
Damiano Da Col, Maximilian Igl, Peter Karkus +5
As scaling pre-training data alone yields diminishing returns, post-training is becoming increasingly important across physical AI domains such as autonomous driving. End-to-end dr…
Valdi: Value Diffusion World Models
Christopher Lindenberg, Kashyap Chitta
World models can enable Model Predictive Control (MPC), but this requires dynamics prediction that is both fast enough for online use and expressive enough to represent uncertain f…
World Engine: Towards the Era of Post-Training for Autonomous Driving
Tianyu Li, Li Chen, Caojun Wang +16
Autonomous vehicles must operate safely in the real world, where errors can have severe consequences. Although modern end-to-end driving policies excel in routine scenarios, their…
DriveJudge: Rethinking Autonomous Driving Evaluation with Vision-Language Models
Xinglong Sun, Kevin Xie, Jenny Schmalfuss +5
Autonomous driving has shifted towards end-to-end policy learning, where reliable, interpretable policy evaluation is a fundamental challenge as driving quality is highly context-d…
123D: Unifying Multi-Modal Autonomous Driving Data at Scale
Daniel Dauner, Valentin Charraut, Bastian Berle +10
The pursuit of autonomous driving has produced one of the richest sensor data collections in all of robotics. However, its scale and diversity remain largely untapped. Each dataset…
Dynamics Distillation for Efficient and Transferable Control Learning
Xunjiang Gu, Kashyap Chitta, Mahsa Golchoubian +2
Robust control policy learning for autonomous driving requires training environments to be both physically realistic and computationally scalable, properties that existing simulato…