Generative World Models for Autonomous Navigation in Dynamic and Uncertain Environments
Keywords:
generative world models; autonomous navigation; uncertainty quantification; safety governance; socio-technical systems; deployment infrastructure; model-based reinforcement learningAbstract
Autonomous navigation in dynamic and uncertain environments remains an open systems problem because perception, prediction, planning, and control must operate coherently under partial observability, nonstationary conditions, and evolving social and physical constraints. Generative world models have recently emerged as a promising architectural response to this challenge. Unlike purely reactive policies or deterministic geometric maps, generative world models learn to simulate plausible futures conditioned on action sequences, thereby enabling agents to evaluate possible maneuvers before execution. This paper provides a system-level examination of generative world models for autonomous navigation, focusing on architecture, representational trade-offs, uncertainty management, governance, deployment infrastructure, robustness, fairness, and policy implications. It argues that the value of generative world models lies not only in predictive accuracy but also in their capacity to integrate multimodal evidence, support latent reasoning over long horizons, and create auditable simulation layers for safety assurance. However, these benefits are accompanied by systemic risks, including distributional brittleness, computational intensity, opaque failure modes, and uneven deployment readiness across infrastructure contexts. The paper develops a conceptual framework that connects learned dynamics, action-conditioned rollouts, uncertainty calibration, regulatory oversight, and lifecycle management. It draws on cross-domain evidence from model-based reinforcement learning, foundation models, autonomous vehicle safety, and algorithmic accountability. The analysis emphasizes that generative world models should be treated as socio-technical infrastructures rather than isolated predictive components, requiring governance mechanisms that address not only technical performance but also fairness, explainability, operational resilience, and long-term sustainability.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
