  Adversarial attacks can compromise the robustness of real-world detection
models. However, evaluating these models under real-world conditions poses
challenges due to resource-intensive experiments. Virtual simulations offer an
alternative, but the absence of standardized benchmarks hampers progress.
Addressing this, we propose an innovative instant-level data generation
pipeline using the CARLA simulator. Through this pipeline, we establish the
Discrete and Continuous Instant-level (DCI) dataset, enabling comprehensive
experiments involving three detection models and three physical adversarial
attacks. Our findings highlight diverse model performances under adversarial
conditions. Yolo v6 demonstrates remarkable resilience, experiencing just a
marginal 6.59% average drop in average precision (AP). In contrast, the ASA
attack yields a substantial 14.51% average AP reduction, twice the effect of
other algorithms. We also note that static scenes yield higher recognition AP
values, and outcomes remain relatively consistent across varying weather
conditions. Intriguingly, our study suggests that advancements in adversarial
attack algorithms may be approaching its ``limitation''.In summary, our work
underscores the significance of adversarial attacks in real-world contexts and
introduces the DCI dataset as a versatile benchmark. Our findings provide
valuable insights for enhancing the robustness of detection models and offer
guidance for future research endeavors in the realm of adversarial attacks.
