Semantic-Level Conflict Traffic Scenario Generation Via Spatiotemporal Polygon Anchors

Abstract

Autonomous Driving Systems (ADS) require rigorous and complex testing under diverse conditions to fulfill various demands and purposes of testing tasks, such as occlusion-triggered events, necessitating semantic-level control in scenario generation. Existing methods, reliant on low-level state controls, struggle to represent high-level semantic intents for task-oriented testing. We propose SPATSG, a novel framework for event-driven, semantically aligned traffic scenario generation, leveraging Spatiotemporal Polygon Anchors (SPA) to bridge high-level test requirements and low-level diffusion guidance. SPAs encapsulate critical geometric and temporal patterns of traffic agents, derived from a set of targeted scenarios. During diffusion denoising, SPATSG integrates SPAs via an auxiliary loss to steer sampling toward desired semantics. A dynamic resampling strategy further intensifies guidance and prioritizes promising trajectory candidates progressively to balance exploration and refinement. We evaluate SPATSG on SinD, a Chinese intersection benchmark featuring complex interactions and diverse conflicts. Experiments on occlusion-triggered scenario generation show that SPATSG demonstrates superior semantic controllability, effectively reveals risk events across ADS, and maintains diversity and realism compared to baselines. This work offers a principled, interpretable approach for semantically controllable ADS testing and evaluation.