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Towards Systematic Qualification of Vision-Language Models for Automotive Perception Systems — arXiv:2609.25945

Source: arXiv (en)
Original: https://arxiv.org/abs/2609.25945
License (as archived): CC0-1.0
Archived: 2026-10-08T15:16:15.989Z
SHA-256 of the archived text: 8ff1d3870d9628bba2ec3691459968c78b4f82bf2b6c2d4910c2dd547f05d115
Stable URL of this page: https://metraipolis.com/documento/dc_ebcdf282bf72

How to cite

Towards Systematic Qualification of Vision-Language Models for Automotive Perception Systems — arXiv:2609.25945 — arXiv (en), https://arxiv.org/abs/2609.25945 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_ebcdf282bf72 (Metraipolis archive dc_ebcdf282bf72, sha256 8ff1d3870d9628bba2ec3691459968c78b4f82bf2b6c2d4910c2dd547f05d115)

BibTeX

@misc{dcebcdf282bf72,
  title = {Towards Systematic Qualification of Vision-Language Models for Automotive Perception Systems — arXiv:2609.25945},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2609.25945},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_ebcdf282bf72; sha256 8ff1d3870d9628bba2ec3691459968c78b4f82bf2b6c2d4910c2dd547f05d115},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2609.25945. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

The field of Artificial Intelligence has been adopted for many application domains. Vision Language Models are one of the recently advanced AI techniques that have been explored to support automotive features such as vehicle perception, and safety assurance. However, such language models are prone to hallucinations, posing a potential threat to the safety of automotive systems that may incorporate them. Within the automotive domain, VLMs could not only hallucinate traffic objects, but could also fail to identify traffic objects that are actually present, which may potentially lead to dangerous situations. Though we have observed a growing body of literature that proposes verification and validation techniques for safe and trustworthy AI, these methods are often studied in isolation, focusing either on run-time or design-time phases. Such isolated techniques could be insufficient in safety-critical, realistic contexts such as automotive perception systems. In this paper, we analyze design-time and run-time verification and validation techniques based on a taxonomy presented by Huang et al. We present an automotive study in which a design-time qualification workflow is proposed to complement run-time monitoring. This workflow combines a fixed safety-relevant ontology-based structured annotation system together with a synonym-based evaluation process to statistically evaluate three state-of-the-art VLMs against data from the nuScenes dataset. We observed that the proposed technique enables deterministic and repeatable quantification of the hallucinations VLMs generate in automotive perception-related tasks. The proposed workflow supports model comparison and deployment-oriented engineering decisions within the design-time verification and validation process and will contribute to a holistic verification strategy that strives towards trustworthy automotive perception systems

Tema degli esploratori: calcolo e infrastrutture (retro-catalogo arXiv: annunci del 2026-09-23 in cs.SE).
Categorie arXiv: Computer Vision and Pattern Recognition (cs.CV) ; Software Engineering (cs.SE).
Pagina del lavoro: https://arxiv.org/abs/2609.25945 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-08T14:42:48.190Z dalla pagina https://arxiv.org/catchup/cs.SE/2026-09-23?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).