自动驾驶中的安全运动规划需要预判不断变化的交通风险，并决定何时修正当前规划的轨迹。我们提出了 RiskWorld，一种用于共享占用预测和选择性轨迹替换的风险感知世界建模框架。该框架将空间风险场和时间行为者上下文与视觉鸟瞰图特征进行融合。通过流引导演化传输占用和场景特征，并利用带符号残差在传输后校正占用状态。每个规划步骤生成一次预测结果，并在所有候选方案中复用。通过将每个候选方案与当前状态持久性参考进行比较，得出非负的碰撞评分修正值。由当前世界评估选定的轨迹作为规划锚点，仅当额外预测的风险触发干预且替代方案满足关于预测风险和轨迹误差的分量约束时，才对其进行替换。候选几何形状保持不变。我们在 nuScenes 数据集上利用相机特征、标注衍生的当前和历史行为者状态以及数据集提供的地图上下文，对 RiskWorld 进行了开环规划评估。RiskWorld 在 3 秒的长评估视界下实现了最低的碰撞率，并在多种最先进基线中取得了第二好的平均 L2 误差，同时在单张 NVIDIA RTX 4090 显卡上以 11.5 FPS 运行，参数量为 90.81 M。设置内消融实验表明，RiskWorld 的碰撞率低于当前状态重评分基线，而预测结果的复用使得能够以较低的边际计算成本评估额外的候选方案。

Safe motion planning in automated driving requires anticipating evolving traffic risks and deciding when to revise the current planned trajectory. We introduce RiskWorld, a risk-aware world modeling framework for shared occupancy forecasting and selective trajectory replacement. Spatial risk fields and temporal actor context are fused with visual bird's-eye-view features. Flow-guided evolution transports occupancy and scene features, while signed residuals correct occupancy after transport. One forecast is generated per planning step and reused across candidates. Each candidate is compared with a current-state persistence reference, yielding a nonnegative collision-score correction. The trajectory selected by current-world evaluation serves as the planning anchor and is replaced only when additional predicted risk triggers intervention and an alternative satisfies component-wise constraints on predicted risk and trajectory error. Candidate geometries remain unchanged. We evaluate RiskWorld for open-loop planning on nuScenes using camera features, annotation-derived current and historical actor states, and dataset-provided map context. RiskWorld achieves the lowest collision rate at a long evaluation horizon of 3 s, and the second-best average L2 error among various state-of-the-art baselines, while running at 11.5 FPS on a single NVIDIA RTX 4090 with 90.81 M parameters. Within-setting ablations show that RiskWorld achieves lower collision rates than the current-state rescoring baseline, while forecast reuse enables additional candidates to be evaluated at low marginal computational cost.