Bench2Drive-Robust is a new closed-loop benchmark that evaluates end-to-end autonomous driving models under deployment perturbations from camera failures, ego-state errors, and compute delays, showing substantial performance degradation beyond image-level tests.
RoboDriveVLM: A novel benchmark and baseline towards robust vision-language mod- els for autonomous driving
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GuardAD reduces accident rates by 32% in autonomous driving MLLMs by using n-th order Markovian logic to infer latent hazards and revise actions.
Changes in Chain-of-Causation explanations under sensor perturbations correlate with 5.3× higher trajectory deviation in a driving VLA, and enabling such explanations yields 11.8% better accuracy.
citing papers explorer
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Bench2Drive-Robust: Benchmarking Closed-Loop Autonomous Driving under Deployment Perturbations
Bench2Drive-Robust is a new closed-loop benchmark that evaluates end-to-end autonomous driving models under deployment perturbations from camera failures, ego-state errors, and compute delays, showing substantial performance degradation beyond image-level tests.
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GuardAD: Safeguarding Autonomous Driving MLLMs via Markovian Safety Logic
GuardAD reduces accident rates by 32% in autonomous driving MLLMs by using n-th order Markovian logic to infer latent hazards and revise actions.
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Lost in Fog: Sensor Perturbations Expose Reasoning Fragility in Driving VLAs
Changes in Chain-of-Causation explanations under sensor perturbations correlate with 5.3× higher trajectory deviation in a driving VLA, and enabling such explanations yields 11.8% better accuracy.