SAGE: A hybrid vision-language and geometry framework for work-zone pathway prediction under temporary traffic control
This paper introduces SAGE (Semantic-to-Action Grounded Execution), a read-decide-execute-check framework that integrates vision-language model trajectories with geometric path candidates to navigate temporary traffic control zones. Trained on 2,306 real-world ROADWork validation episodes using MUTCD-compliant supervision, SAGE reduces average displacement error by 8.3% and final displacement error by 12.0% relative to a geometric ranker, while reducing hard-constraint violations by 37%. The system achieves a path within 10 pixels of human demonstrations in 87% of episodes and outperforms four other adapted driving VLMs in safety measures.