Advanced Driver Assistance Systems (ADAS) have made significant strides, capitalizing on computer vision to enhance perception and decision-making capabilities. Nonetheless, the adaptation of these systems to diverse traffic scenarios poses challenges due to shifts in data distribution stemming from factors such as location, weather, and road infrastructure. To tackle this, we introduce a weakly-supervised label unification pipeline that amalgamates pseudo labels from a multitude of object detection models trained on heterogeneous datasets. Our pipeline engenders a unified label space through the amalgamation of labels from disparate datasets, rectifying bias and enhancing generalization. We fine-tune multiple object detection models on individual datasets, subsequently crafting a unified dataset featuring pseudo labels, meticulously validated for precision. Following this, we retrain a solitary object detection model using the merged label space, culminating in a resilient model proficient in dynamic traffic scenarios. We put forth a comprehensive evaluation of our approach, employing diverse datasets originating from varied Asian countries, effectively demonstrating its efficacy in challenging road conditions. Notably, our method yields substantial enhancements in object detection performance, culminating in a model with heightened resistance against domain shifts.

翻译：先进驾驶辅助系统基于计算机视觉显著提升了感知与决策能力，但受地点、天气、道路基础设施等因素导致的数据分布偏移影响，此类系统在适应多样化交通场景时面临挑战。针对这一问题，我们提出一种弱监督标签统一流水线，通过融合在异构数据集上训练的多个目标检测模型生成的伪标签，构建统一标签空间，从而矫正偏差并增强泛化能力。具体而言，我们首先在独立数据集上微调多个目标检测模型，随后构建包含经过精度验证的伪标签的统一数据集，最终利用合并后的标签空间重新训练单一目标检测模型，获得适用于动态交通场景的鲁棒模型。通过采用来自多个亚洲国家的多样化数据集进行全面评估，我们证实了该方法在复杂道路条件下的有效性。值得注意的是，本方法显著提升了目标检测性能，使模型具备更强的域偏移抵抗能力。