Localized Conformal Safety Monitoring with Vision-Language Models for Autonomous Driving
Monitoring planned driving trajectories requires accurately estimating the collision likelihood with actors whose motion is itself impacted by the ego motion. Existing classical approaches are often limited by the quality of their forecasting model. Vision-language models (VLMs) have shown promise in reasoning about the consequences of high-level actions, yet their approximate predictions are unsuitable for safety-critical applications such as autonomous driving. Conformal prediction (CP) has emerged as a data-driven framework for quantifying the uncertainty of black-box model predictions. We propose Split Label-Localized Conformal Prediction (SLLCP), a post-hoc calibration layer over fro...
| Authors | Luís Marques, Rong Fang, Disha Kamale et al. |
|---|---|
| Primary category | cs.RO |
| Published | 2 October 2026 |
| Last updated at source | 2 October 2026 |
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