Can Cui

Direct predictions in driving world models can lead to substantial inaccuracies in counterfactual scenarios, revealing a critical gap in current methodologies.

Post-training techniques could be the key to overcoming the limitations of traditional imitation learning in autonomous driving, ensuring safer and more reliable vehicle behavior in complex environments.

Even slight variations in natural language instructions can cause language-driven autonomous driving models to fail dramatically, revealing a critical reliability gap for real-world deployment.

Ditch slow, verbose language-based action planning: this diffusion model directly predicts discrete, kinematically feasible waypoints for faster, more precise autonomous driving.

Finally, a real-world testing platform that can rigorously evaluate full-stack VLM-integrated autonomous driving systems, offering configurable scenarios and closed-loop control.