  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.
