WZPlanner：施工区自动驾驶的安全端到端路径规划
WZPlanner: Safe End-to-End Path Planning for Autonomous Driving in Work Zones
- Carnegie Mellon University（卡内基梅隆大学）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对施工区地图缺失与数据稀缺问题，提出WorkZonePlan数据集和BF++模型，联合预测车道/施工区边界与轨迹，以更小模型实现更高驾驶得分，提升施工区自动驾驶安全性。
AI中文摘要：
施工区通过临时交通管制和封闭改变车道几何形状，这些变化可能未包含在车载地图中，对自动驾驶汽车（AV）的感知和规划构成挑战。由于缺乏具有结构化几何监督的公开数据集，泛化能力也受到限制。我们提出了WorkZonePlan数据集，包含149K+合成和5K+真实世界的多模态样本，具有车道边界、施工区边界和驾驶轨迹选项的3D标注。它还提供了76个闭环CARLA场景，在三种天气条件下重放，产生228条Bench2Drive格式的评估路线。我们引入了WAVE（虚拟和真实环境中面向施工区的AV数据生成），这是一个用于创建数据集的半自动流水线，以及BoundaryFormer（BF），一种基于Transformer的模型，联合预测车道和施工区边界多项式以及驾驶轨迹。BF使用槽注意力进行边界预测。消融研究表明，使用边界槽特征的独立轨迹解码器显著优于仅使用槽注意力的方法。基于这一发现，BF++提供了Camera和Camera+LiDAR变体，具有度量地面平面编码、类型化边界/轨迹查询、长距离点锚、图像空间曲线细化和保守门控LiDAR融合。在评估冻结时所有四种模型共有的211条路线上，BF++-Camera和BF++-Camera+LiDAR分别实现了63.0和64.4的驾驶得分，而SimLingo为59.3，TransFuser++（TF++）为26.1。BF++比SimLingo小40倍，比TF++小10倍以上，同时实现了更高的驾驶得分。这些结果支持联合预测车道边界、施工区边界和驾驶轨迹，作为在施工区实现更安全AV运行的有前景方向。代码和数据集：此HTTPS URL。
英文摘要：
Work zones alter lane geometry through temporary traffic controls and closures that may be absent from on-board maps, challenging autonomous vehicle (AV) perception and planning. Generalization is also limited by scarce public datasets with structured geometric supervision. We present WorkZonePlan, a dataset comprising 149K+ synthetic and 5K+ real-world multimodal samples with 3D annotations for lane boundaries, work zone boundaries, and driving trajectory options. It also provides 76 closed-loop CARLA scenarios replayed under three weather conditions, yielding 228 Bench2Drive-format evaluation routes. We introduce WAVE (Work-zone-focused AV data generation in Virtual and rEal Environments), a semi-automated pipeline for creating the dataset, and BoundaryFormer (BF), a transformer-based model that jointly predicts lane and work zone boundary polynomials and driving trajectories. BF uses slot attention for boundary prediction. Ablations show that a separate trajectory decoder using boundary slot features substantially improves trajectory prediction over a slot-attention-only approach. Building on this finding, BF++ offers Camera and Camera+LiDAR variants with metric ground-plane encoding, typed boundary/trajectory queries, long-range point anchors, image-space curve refinement, and conservative gated LiDAR fusion. On the 211 routes common to all four models at the evaluation freeze, BF++-Camera and BF++-Camera+LiDAR achieve Driving Scores of 63.0 and 64.4, respectively, compared with 59.3 for SimLingo and 26.1 for TransFuser++ (TF++). BF++ is 40 times smaller than SimLingo and more than 10 times smaller than TF++, while achieving higher Driving Scores. These results support jointly predicting lane boundaries, work zone boundaries, and driving trajectories as a promising direction toward safer AV operation in work zones. Code and dataset: https://github.com/Nishad-Sahu/WZPlanner.
