LEO：基于图注意力网络的混合多传感器扩展物体融合与跟踪用于自动驾驶应用
LEO: Graph Attention Network based Hybrid Multi Sensor Extended Object Fusion and Tracking for Autonomous Driving Applications
AI总结：
LEO结合图注意力网络融合多模态传感器数据，实现动态物体形状和轨迹的准确估计，具备自适应融合权重和跨传感器类型的一致性跟踪能力。
摘要：
准确估计动态物体的形状和轨迹对可靠自动驾驶至关重要。经典贝叶斯扩展物体模型在理论上稳健且高效，但依赖先验和更新似然函数的完整性，而深度学习方法虽然具有适应性，但需要密集标注和高计算成本。LEO（Learned Extension of Objects）是一种时空图注意力网络，通过融合多模态生产级传感器轨迹学习自适应融合权重，确保时间一致性，并表示多尺度形状。使用特定任务的平行四边形真实值公式，LEO可以建模复杂几何（如铰接卡车和拖车）并在不同传感器类型、配置、物体类别和区域间泛化，对具有挑战性和远距离目标保持稳健。在梅赛德斯-奔驰DRIVE PILOT SAE L3数据集上的评估显示，LEO具有实时计算效率，适合生产系统；在公共数据集如德尔夫特视图（VoD）上的额外验证进一步证实了跨数据集的泛化能力。
英文摘要：
Accurate shape and trajectory estimation of dynamic objects is essential for reliable automated driving. Classical Bayesian extended-object models offer theoretical robustness and efficiency but depend on completeness of a-priori and update-likelihood functions, while deep learning methods bring adaptability at the cost of dense annotations and high compute. We bridge these strengths with LEO (Learned Extension of Objects), a spatio-temporal Graph Attention Network that fuses multi-modal production-grade sensor tracks to learn adaptive fusion weights, ensure temporal consistency, and represent multi-scale shapes. Using a task-specific parallelogram ground-truth formulation, LEO models complex geometries (e.g. articulated trucks and trailers) and generalizes across sensor types, configurations, object classes, and regions, remaining robust for challenging and long-range targets. Evaluations on the Mercedes-Benz DRIVE PILOT SAE L3 dataset demonstrate real-time computational efficiency suitable for production systems; additional validation on public datasets such as View of Delft (VoD) further confirms cross-dataset generalization.
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