SceneDemand: Intent-Conditioned Recursive Vector Scene Generation for Autonomous Driving

Generating interactive corner cases helps expose weaknesses in autonomous-driving planners and supports simulation-based training. However, existing scene-centric approaches do not explicitly align extension with the planner’s intended trajectory and planning horizon. Scenes sampled without planner feedback may also lack informative interactions with the ego. Furthermore, increased difficulty alone does not guarantee training value. We propose SceneDemand, an intent-conditioned framework for on-demand recursive vector scene generation. Building on the Scenario Dreamer prior, we design a joint lane–agent diffusion generator that completes regions along the selected ego trajectory while preserving previously committed content. A temporal-coverage mechanism triggers extension when the existing map cannot support the full planning horizon, enabling scene growth to adapt to evolving ego intent. We further introduce Counterfactual Direct Planner Guidance (CDPG), which compares frozen-critic values with and without new vehicles at multiple static intent-aligned viewpoints to steer their generation toward planner-specific challenges. On Waymo, intent conditioning increases the recursive extension completion rate from 48% to 76%. Under matched training and evaluation across three policy seeds, planners trained on SceneDemand-CDPG scenes achieve 77.92% mean success on held-out Waymo Log Replay scenes, compared with 75.75% for Scenario Dreamer.