AffordDrive3D: Affordance-Aware World-Action Modeling with Spatial Understanding

World-action models have recently improved autonomous driving by jointly learning future scene prediction and trajectory generation. Most existing approaches model the future primarily through RGB appearance, and recent works have begun to incorporate geometric prediction to improve spatial understanding. However, dense geometry describes the spatial layout of the entire scene without indicating which parts are most relevant to the ego vehicle’s action. For driving, the model must also identify and anticipate where it can safely move and which regions may pose collision risks. When action-relevant regions are jointly learned with future geometry, the policy can capture both what matters for driving and the corresponding geometric information. We therefore propose AffordDrive3D, an affordance- and geometry-aware world-action model that jointly learns future action-relevant regions and spatial structure. In order to capture the scene semantics and driving context needed for driving affordance prediction, we build AffordDrive3D on a VLM backbone to forecast drivable areas and collision-critical regions that directly affect ego motion, while predicting future geometry from RGB world-model latents. Experiments on NAVSIM show that AffordDrive3D achieves strong performance and demonstrate the effectiveness of jointly modeling future affordance and geometry.