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Advanced Driver Assistance Systems (ADAS) have made significant strides,capitalizing on computer vision to enhance perception and decision-makingcapabilities. Nonetheless, the adaptation of these systems to diverse trafficscenarios poses challenges due to shifts in data distribution stemming fromfactors such as location, weather, and road infrastructure. To tackle this, weintroduce a weakly-supervised label unification pipeline that amalgamatespseudo labels from a multitude of object detection models trained onheterogeneous datasets. Our pipeline engenders a unified label space throughthe amalgamation of labels from disparate datasets, rectifying bias andenhancing generalization. We fine-tune multiple object detection models onindividual datasets, subsequently crafting a unified dataset featuring pseudolabels, meticulously validated for precision. Following this, we retrain asolitary object detection model using the merged label space, culminating in aresilient model proficient in dynamic traffic scenarios. We put forth acomprehensive evaluation of our approach, employing diverse datasetsoriginating from varied Asian countries, effectively demonstrating its efficacyin challenging road conditions. Notably, our method yields substantialenhancements in object detection performance, culminating in a model withheightened resistance against domain shifts.

Fusing Pseudo Labels with Weak Supervision for Dynamic Traffic Scenarios