AI RESEARCH
ICR-Drive: Instruction Counterfactual Robustness for End-to-End Language-Driven Autonomous Driving
arXiv CS.CL
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ArXi:2604.05378v1 Announce Type: new Recent progress in vision-language-action (VLA) models has enabled language-conditioned driving agents to execute natural-language navigation commands in closed-loop simulation, yet standard evaluations largely assume instructions are precise and well-formed. In deployment, instructions vary in phrasing and specificity, may omit critical qualifiers, and can occasionally include misleading, authority-framed text, leaving instruction-level robustness under-measured. We.