Abstract
This project addresses the critical challenges of deploying Connected and Automated Vehicles (CAVs) in adverse Midwest winter conditions by developing a novel hierarchical framework that integrates Large Vision-Language Models for robust, context-aware decision-making. Real-world pilot experiments demonstrate that this architecture significantly enhances vehicle safety and personalization, effectively translating abstract human commands into precise control adjustments on low-friction, snow-covered surfaces. Furthermore, a specialized high-fidelity dataset capturing unique environmental edge cases was established to bridge the domain gap in existing resources and support the future development of resilient all-weather autonomous technologies.
Keywords
Autonomous vehicles, connected vehicles, and artificial intelligence
DOI
10.5703/1288284318562
Date of this Version
1-2026
Recommended Citation
Zhou, Yupeng; Cui, Can; Yang, Zichong; Peng, Juntong; and Wang, Ziran, "CAV Pilot Development and Deployment in Midwest Winter" (2026). Center for Connected and Automated Transportation. Paper 56.
http://dx.doi.org/10.5703/1288284318562