ICR-Drive reveals substantial performance drops in end-to-end language-driven driving models when instructions are paraphrased, made ambiguous, noised, or misleading.
Simulation-based adversarial test genera- tion for autonomous vehicles with machine learning com- ponents
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ICR-Drive: Instruction Counterfactual Robustness for End-to-End Language-Driven Autonomous Driving
ICR-Drive reveals substantial performance drops in end-to-end language-driven driving models when instructions are paraphrased, made ambiguous, noised, or misleading.