EgoDyn-Bench reveals a perception bottleneck in vision-centric foundation models: ego-motion logic derives from language while visual input adds negligible signal, with explicit trajectories restoring consistency.
In: 2011 IEEE Intelligent Vehicles Symposium (IV)
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A reproducible pipeline produces physical adversarial traffic signs that successfully attack production-grade traffic sign recognition systems in a real car under black-box conditions.
LLM-assisted pipeline jointly generates logical formulas and executable predicates for rule-based verification of HD map transformations in CommonRoad, evaluated on synthetic bridge and slope scenarios.
citing papers explorer
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EgoDyn-Bench: Evaluating Ego-Motion Understanding in Vision-Centric Foundation Models for Autonomous Driving
EgoDyn-Bench reveals a perception bottleneck in vision-centric foundation models: ego-motion logic derives from language while visual input adds negligible signal, with explicit trajectories restoring consistency.
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Fooling a Real Car with Adversarial Traffic Signs
A reproducible pipeline produces physical adversarial traffic signs that successfully attack production-grade traffic sign recognition systems in a real car under black-box conditions.
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LLM-Assisted Tool for Joint Generation of Formulas and Functions in Rule-Based Verification of Map Transformations
LLM-assisted pipeline jointly generates logical formulas and executable predicates for rule-based verification of HD map transformations in CommonRoad, evaluated on synthetic bridge and slope scenarios.