<p>Traffic sign detection systems help intelligent vehicles to perceive and adhere to traffic regulations, which is crucial for ensuring driving safety. Recent studies reveal that attackers can generate adversarial examples by attaching carefully designed patches to traffic signs, which can mislead detection results and produce potential collision risks. In order to enhance the success rate of adversarial attacks and fully reveal the threat of adversarial examples, researchers simulate the changes of traffic signs in driving scenarios by transforming the size and position of adversarial patches when generating them. However, when performing such transformations, most studies did not take into account the long-tailed distribution of traffic sign sizes in driving scenarios. Additionally, optimization mechanisms overly rely on semantic information and ignore geometric information, which limits the applicability of adversarial examples. To address these issues, this paper proposes a progressive sampling strategy-guided image transformation that adjusts the size distribution of traffic signs based on the number of attack rounds, thereby fitting the long-tailed distribution through an “easy-to-hard" progressive attack. Additionally, an optimization metric is proposed to adjust the semantic confidence based on the intersection over union between the ground truth and predicted boxes, thereby enabling a focus on boxes that exhibit both high geometric and semantic consistency. Experiments conducted on the TT100KD and COCO datasets, as well as on a dataset collected by a test vehicle, demonstrate that the proposed method achieves at least a 3.5% improvement in attack success rates compared to existing methods.</p>