  Adversarial attacks in the physical world can harm the robustness of
detection models. Evaluating the robustness of detection models in the physical
world can be challenging due to the time-consuming and labor-intensive nature
of many experiments. Thus, virtual simulation experiments can provide a
solution to this challenge. However, there is no unified detection benchmark
based on virtual simulation environment. To address this challenge, we proposed
an instant-level data generation pipeline based on the CARLA simulator. Using
this pipeline, we generated the DCI dataset and conducted extensive experiments
on three detection models and three physical adversarial attacks. The dataset
covers 7 continuous and 1 discrete scenes, with over 40 angles, 20 distances,
and 20,000 positions. The results indicate that Yolo v6 had strongest
resistance, with only a 6.59% average AP drop, and ASA was the most effective
attack algorithm with a 14.51% average AP reduction, twice that of other
algorithms. Static scenes had higher recognition AP, and results under
different weather conditions were similar. Adversarial attack algorithm
improvement may be approaching its 'limitation'.
