Generating safety-critical driving scenarios requires understanding why dangerous interactions arise, rather than merely forcing collisions. However, existing methods rely on heuristic adversarial agent selection and unstructured perturbations, lacking explicit modeling of interaction dependencies and thus exhibiting a realism--adversarial trade-off. We present CounterScene, a framework that endows closed-loop generative BEV world models with structured counterfactual reasoning for safety-critical scenario generation. Given a safe scene, CounterScene asks: what if the causally critical agent had behaved differently? To answer this, we introduce causal adversarial agent identification to identify the critical agent and classify conflict types, and develop a conflict-aware interactive world model in which a causal interaction graph is used to explicitly model dynamic inter-agent dependencies. Building on this structure, stage-adaptive counterfactual guidance performs minimal interventions on the identified agent, removing its spatial and temporal safety margins while allowing risk to emerge through natural interaction propagation. Extensive experiments on nuScenes demonstrate that CounterScene achieves the strongest adversarial effectiveness while maintaining superior trajectory realism across all horizons, improving long-horizon collision rate from 12.3% to 22.7% over the strongest baseline with better realism (ADE 1.88 vs.2.09). Notably, this advantage further widens over longer rollouts, and CounterScene generalizes zero-shot to nuPlan with state-of-the-art realism.

翻译：生成安全关键的驾驶场景需要理解危险交互的成因，而非仅强制发生碰撞。然而现有方法依赖启发式对抗智能体选择与无结构扰动，缺乏对交互依赖关系的显式建模，因而存在真实性与对抗性的权衡。我们提出CounterScene框架，赋予闭环生成式BEV世界模型结构化反事实推理能力，用于安全关键场景生成。给定安全场景时，CounterScene追问：若因果关键智能体行为发生改变会怎样？为此，我们引入因果对抗智能体识别方法定位关键智能体并分类冲突类型，进而构建冲突感知交互式世界模型——其中采用因果交互图显式建模智能体间动态依赖关系。基于该结构，阶段自适应反事实引导对已识别智能体施加最小干预，消除其空间与时间安全裕度，同时使风险通过自然交互传播涌现。在nuScenes上的大量实验表明，CounterScene在保持最优轨迹真实性的前提下实现了最强对抗有效性，其长时域碰撞率较最强基线从12.3%提升至22.7%，且真实性更优（ADE 1.88对比2.09）。值得注意的是，该优势在更长展开时序中进一步扩大，且CounterScene能以最先进的真实性零样本泛化至nuPlan。