Vision-Language World Models for Risk-Aware Decision Making in Autonomous Driving

Abstract

Autonomous driving has increasingly moved from modular perception-planning pipelines toward learned systems that must operate under profound uncertainty. Vision-language world models offer a promising mechanism for anticipating future states, interpreting traffic situations through natural language, and supporting risk-aware decisions. This paper examines their role from a systems perspective, focusing on architectural trade-offs, data infrastructure, operational deployment, and governance rather than narrowly algorithmic improvements. We argue that unifying visual understanding, language-grounded reasoning, and predictive generation changes the nature of risk assessment because the model can represent counterfactual futures, contextualize ambiguous observations, and explain potential conflicts in human-interpretable terms. However, these capabilities introduce structural challenges including calibration of multimodal uncertainty, integration with existing safety cases, and dependence on large heterogeneous datasets. We discuss how reinforcement learning, generative modeling, and instruction tuning can be coupled to improve decision quality under uncertainty while preserving oversight. The analysis covers robustness against distribution shift and adversarial perturbation, fairness across road users and environments, sustainability of large-scale training, and the policy implications of deploying such models in public infrastructure. We conclude that vision-language world models should be treated as socio-technical control systems rather than isolated perception modules. Their adoption requires continuous monitoring, standardized evaluation across risk dimensions, transparent documentation, and regulatory alignment with existing vehicle safety frameworks. The paper contributes a cross-disciplinary synthesis of architecture, governance, and deployment concerns intended to inform researchers, engineers, and policymakers.