风险感知的世界建模与流引导占用演化用于自动驾驶中的选择性轨迹规划
Risk-Aware World Modeling with Flow-Guided Occupancy Evolution for Selective Trajectory Planning in Automated Driving
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中文总结 AI 辅助
RiskWorld通过流引导占用演化融合风险场与视觉特征，实现共享占用预测和选择性轨迹替换，在3秒视界上取得最低碰撞率，并以11.5 FPS高效运行。
中文摘要 AI 辅助
自动驾驶中的安全运动规划需要预测不断演变的交通风险，并决定何时修订当前规划的轨迹。我们引入了RiskWorld，一个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。空间风险场和时间演员上下文与视觉鸟瞰特征融合。流引导演化传输占用和场景特征，而符号残差在传输后校正占用。每个规划步骤生成一次预测，并在候选轨迹之间重用。每个候选轨迹与当前状态持久性参考进行比较，产生非负碰撞分数修正。由当前世界评估选择的轨迹作为规划锚点，仅当额外预测风险触发干预且替代轨迹满足预测风险和轨迹误差的逐分量约束时，才进行替换。候选轨迹的几何形状保持不变。我们在nuScenes上使用相机特征、注释派生的当前和历史演员状态以及数据集提供的地图上下文评估RiskWorld用于开环规划。RiskWorld在3秒的长评估视界上实现了最低碰撞率，并在各种最先进基线中取得了第二好的平均L2误差，同时在单个NVIDIA RTX 4090上以11.5 FPS运行，参数为90.81百万。在设置内的消融实验表明，RiskWorld实现了比当前状态重新评分基线更低的碰撞率，而预测重用使得额外候选轨迹能够以较低的计算边际成本进行评估。
英文摘要
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.
发表机构
- RWTH Aachen University（亚琛工业大学）
- Monash University（莫纳什大学）
- Technical University of Munich (TUM)（慕尼黑工业大学）
机构由 AI 辅助整理，请以论文原文为准。
