15 papers
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…
ReSim: Reliable World Simulation for Autonomous Driving
Jiazhi Yang, Kashyap Chitta, Shenyuan Gao +7
How can we reliably simulate future driving scenarios under a wide range of ego driving behaviors? Recent driving world models, developed exclusively on real-world driving data com…