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Large vision–language models (LVLMs) can describe driving scenes and support decisions [Li25], but they sometimes hallucinate objects, relations, or events that are not present [Liu24,Liu25]. In a
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this context, the role of subjective and objective measures, and their integration, is crucial for creating robust and interpretable AI models. Today, the complexity of multi-dimensional data and the need
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they sometimes hallucinate objects, relations, or events that are not present [Liu24,Liu25]. In a safety-critical domain, reducing hallucinations and improving robustness and trustworthiness are essential
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other sources. This enables the sensor to capture fine details about an object's position, velocity, and movement direction, which can be invaluable for applications such as medical imaging, autonomous