SOURCE-LINKED INTELLIGENCE

Observe Before You Alert: Adaptive Driver Alerting with Vision-Language Models

arXiv · AI, language, vision and robotics · article · Sep 8, 2026 · UTC

Driver alerting from dashcam video requires sequential decision-making under partial observability: a system must decide not only whether a scene is risky, but also when the evidence is sufficient to warn. Most existing accident anticipation models output a binary risk score, leaving ambiguous scenes to be handled by thresholding. We propose VLAlert, a vision-language alerting framework that casts warning generation as a tri-action policy over SILENT, OBSERVE, and ALERT. The OBSERVE action acts as an internal evidence-gathering decision that delays uncertain warnings and changes the next obser

Read original source ↗ Open in workspace

recordType

paper

region

Global