LAD achieves 3x lower latency than previous driving language models by generating textual reasoning and motion plans at up to 20 Hz.
March 31, 2026
Original Paper
RAD-LAD: Rule and Language Grounded Autonomous Driving in Real-Time
arXiv · 2603.28522
The Takeaway
It enables real-time, closed-loop deployment of language-action planners in autonomous driving, setting a new state-of-the-art on hard nuPlan benchmarks while providing interpretable reasoning.
From the abstract
We present LAD, a real-time language--action planner with an interruptible architecture that produces a motion plan in a single forward pass (~20 Hz) or generates textual reasoning alongside a motion plan (~10 Hz). LAD is fast enough for real-time closed-loop deployment, achieving ~3x lower latency than prior driving language models while setting a new learning-based state of the art on nuPlan Test14-Hard and InterPlan. We also introduce RAD, a rule-based planner designed to address structural l