用Transformer改进高阶目标融合，让自动驾驶感知更准更省算力。
HiLO: High-Level Object Fusion for Autonomous Driving using Transformers
- 基于Transformer设计新型高阶目标融合方法HiLO，替代传统滤波器。
- 在真实数据集上提升F1分数25.9个百分点，平均IoU提高6.1个百分点。
- 适用于城市与高速跨场景部署，适合近量产级车载系统使用。
传感器数据融合对自动驾驶环境感知的鲁棒性至关重要。学习型融合方法多采用特征级融合以实现高性能，但其复杂度和硬件需求限制了在近量产车辆中的应用。高阶融合方法则在较低计算开销下具备更强鲁棒性，传统方法如卡尔曼滤波占据主导地位。本文改进自适应卡尔曼滤波（AKF），提出一种基于Transformer的新型高阶目标融合方法HiLO。实验表明，该方法在F1分数上提升25.9个百分点，在平均交并比（mean IoU）上提升6.1个百分点。在新构建的大规模真实世界数据集上的评估验证了方法的有效性，跨场景（城市与高速公路）测试进一步证明其泛化能力。代码、数据及模型已开源：https://github.com/rst-tu-dortmund/HiLO。
原文摘要 · Abstract (English)
The fusion of sensor data is essential for a robust perception of the environment in autonomous driving. Learning-based fusion approaches mainly use feature-level fusion to achieve high performance, but their complexity and hardware requirements limit their applicability in near-production vehicles. High-level fusion methods offer robustness with lower computational requirements. Traditional methods, such as the Kalman filter, dominate this area. This paper modifies the Adapted Kalman Filter (AKF) and proposes a novel transformer-based high-level object fusion method called HiLO. Experimental results demonstrate improvements of $25.9$ percentage points in $\textrm{F}_1$ score and $6.1$ percentage points in mean IoU. Evaluation on a new large-scale real-world dataset demonstrates the effectiveness of the proposed approaches. Their generalizability is further validated by cross-domain evaluation between urban and highway scenarios. Code, data, and models are available at https://github.com/rst-tu-dortmund/HiLO .
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