## 1 Introduction  
As a fundamental perception task in the traffic environment, by predicting their categories and 2D bounding boxes, 2D vehicle detection identifies vehicles of interest in the surrounding environment. Cameras are a crucial sensor type for 2D vehicle detection,capturing rich semantic information of the scene in the form of color images. Given the high safety requirement in traffic scenarios, assessing the robustness of 2D vehicle detectors under different conditions is crucial before deployment. Despite recent significant advancements in 2D vehicle detection, such as Faster R-CNN [1], YOLO [2], YOLOv3 [3], SSD [4], CornerNet [5], CenterNet [6], Cascade R-CNN [7], RepPoints [8], DETR [9]. Advantages and disadvantages of different detection models are shown in Appendix A.1. However, conducting a comprehensive evaluation of algorithm robustness in deployment environments and optimizing robustness remain challenging tasks. In addition, since most of the annotated datasets used for training detectors were primarily collected under standard conditions, the models trained on clean data may experience performance degradation in adverse weather conditions (such as rain, snow, or fog) and under certain unfavorable conditions (such as strong or weak light, noise, blur, occlusion, etc.). In other words, deep learning models based on data often exhibit poor generalization when faced with data corruption, posing substantial obstacles to safety and reliability. Some researches synthesized common data corruption on clean datasets to evaluate the robustness of object detection, but they only considered a few simple data corruptions, which may be inadequate and unrealistic for vehicle detection. Moreover, due to the high cost associated with data collection and the rarity of extreme scenarios and adverse weather conditions, these datasets tend to be small. In light of this, comprehensively analyzing possible corruption types and accurately evaluating the corruption robustness when considering diverse driving scenarios remain challenging. According to the challenge introduced above, in this study, as shown in Figure 1, after comprehensively analyzing possible corruption types, we focus on traffic scenes in real and complex environments. Through experimental analysis of a large number of influence factors, we have removed some factors that have insignificant effects on vehicle detection or rarely occur, such as various adversarial corruption, shadow interference, hail, lens distortion, glare, etc. Finally, according to the entire chain of vehicle detection, we have classified the fundamental factors that have the greatest impact and are most likely to occur on vehicle detection in reality into four categories and then carried out detailed evaluation and analysis. These mainly include external adverse weather and occlusion, noise introduced by sensors, changes in color and brightness, blur caused by the high-speed movement of the vehicle, and transformation during image transmission and compression, effectively covering a wide range of real-world interference scenarios. Furthermore, each type of interference has been generated at three different severity levels and then applied to dense traffic flow images to create various corrupted images. To perform robustness evaluation, on this basis, we constructed datasets that include extreme weather conditions and common image corruption. These large-scale interference datasets can serve as comprehensive and widely applicable benchmark datasets for assessing the robustness of vehicle detection models in complex traffic scenarios, thus facilitating future research. Considering the importance of real-time performance in traffic scenarios, we selected representative real-time object detection algorithms, including multiple versions of YOLOv5 and YOLOv8, SSD, CenterNet, Faster R-CNN, Mask R-CNN, RetinaNet, and Cascade R-CNN, to assess the robustness of 2D vehicle detectors under various transportation scenarios.