To explore the traffic violation characteristics of Autonomous Vehicles (AV) in real-world traffic environments, the adaptability of existing traffic regulations, and the differences compared to Human-Driven Vehicles (HV), this study utilizes the SinD artificial driving dataset, highD artificial driving dataset, and Argoverse2 autonomous driving dataset. 6 types of violation cases are analyzed, including speed limit violations, safe following distance violations, lane changes, running red lights, left-turn blocking of straight-moving vehicles, and continuous lane changes. The study employs Pearson correlation coefficients and linear regression models to analyze speed limit violations, evaluates the safety of following distance and lane changes using headway time as an indicator, and uses data proportion analysis to assess red light violations and left-turn blocking violations that impede straight-moving traffic. Additionally, continuous lane change characteristics are studied by examining lane change frequency, stability, and speed variability. The results show that under high-speed conditions, AVs are more prone to persistent speed limit violations but exhibit more stable speeds. The risk of safe following distance violations is higher for AVs compared to HVs, while the risk of lane change violations is lower for AVs. Red light violations are more significantly influenced by weather conditions for AVs, and once left-turn blocking violations occur, they present more severe risks. In short-distance, low-speed scenarios, AVs tend to change lanes more frequently with poorer stability. The study confirms that AVs have not yet fully complied with traffic regulations, with higher rates of speed limit and safe following distance violations. Significant differences exist between AVs and HVs across all six types of violations. The applicability of current traffic regulations to AVs has limitations. Based on this, future research can further explore 4 areas: the construction of specialized AV violation datasets, the improvement of specific traffic regulations for AVs, upgrades to autonomous driving algorithms, and the establishment of compliance evaluation mechanisms. The findings provide theoretical support and practical reference for the compliant integration of AVs into the existing traffic system.