arXiv:2510.18034v3 Announce Type: replace-cross Abstract: Autonomous driving systems remain critically vulnerable to the long-tail of rare, out-of-distribution semantic anomalies. While VLMs have emerged as promising tools for perception, their application in anomaly detection remains largely restricted to prompting proprietary models - limiting reliability, reproducibility, and deployment feasibility. To address this gap, we introduce SAVANT (Semantic Anomaly Verification/Analysis Toolkit), a novel model-agnostic reasoning framework that reformulates anomaly detection as a layered semantic co
The increasing sophistication of VLMs and the critical need for robust anomaly detection in autonomous systems are converging, prompting research into more reliable and reproducible methods.
This development addresses a key vulnerability in autonomous driving by moving beyond proprietary models, promising more resilient and trustworthy AI for safety-critical applications.
The focus is shifting from direct VLM prompting to model-agnostic, structured reasoning frameworks for semantic anomaly detection, enhancing reliability and deployment feasibility.
- · Autonomous vehicle manufacturers
- · AI safety researchers
- · Developers of open-source AI frameworks
- · Proprietary VLM vendors (losing exclusivity on key applications)
- · Companies relying on ad-hoc anomaly detection methods
Improved safety and reliability of autonomous driving systems through better semantic anomaly detection.
Accelerated adoption and public trust in autonomous technologies as a result of enhanced safety.
Reduced regulatory hurdles for autonomous systems due to demonstrable improvements in handling rare, out-of-distribution events.
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Read at arXiv cs.AI