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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.