WorldVLM: Combining World Model Forecasting and Vision-Language Reasoning
Autonomous driving systems depend on on models that can reason about high-level scene contexts and accurately predict the dynamics of their surrounding environment. Vision- Language Models (VLMs) have recently emerged as promising tools for decision-making and scene understanding, offering strong capabilities in contextual reasoning. However, their limited spatial comprehension constrains their effectiveness as end-to-end driving models. World Models (WM) internalize environmental dynamics to predict future scene evolution. Recently explored as ego-motion predictors and foundation models for autonomous driving, they represent a promising direction for addressing key challenges in the fiel...
| Authors | Stefan Englmeier, Katharina Winter, Fabian B. Flohr |
|---|---|
| Primary category | cs.CV |
| Published | 15 March 2026 |
| Last updated at source | 17 March 2026 |
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