In Autonomous Vehicle (AV) technology, the robustness of Traffic Sign Classification (TSC) systems is crucial for ensuring safe navigation. Currently, TSC systems lack dedicated adversarial recovery methods, making them susceptible to adversarial attacks. This study embarks on an innovative path by adapting six established adversarial recovery methods from general image classification (IC) to TSC in AVs. Prompted by the limited availability of TSC-specific adversarial recovery solutions, our research undertakes a comparative analysis of these six methods to evaluate their applicability and effectiveness in the TSC domain. Each method is meticulously adapted and integrated, considering the unique challenges and requirements of TSC in AV environments. Our findings reveal insights into their performance, with the Purifying Variational Autoencoder (PuVAE) method outperforming others, achieving recovery rates of 96.15%, 79.24%, and 60.18% for Chinese, Belgium, and Germany traffic sign datasets, respectively. Additionally, it demonstrated Structural Similarity Index Measure (SSIM) values of 0.58, 0.53, and 0.66, along with recovery times of 0.0009 seconds, 0.0008 seconds, and 0.0008 seconds for the respective datasets. By highlighting effective recovery methods and addressing the unique challenges of adversarial attacks in TSC, this study contributes to improving the safety and resilience of autonomous driving systems. This research not only fills a significant gap in the safety mechanisms of AVs but also paves the way for future exploration and development of more robust and secure TSC systems, capable of effectively countering a wide range of adversarial attacks.

This paper explores the integration of Artificial Intelligence Generated Content (AIGC) with human-machine intelligence (HMI) to enhance the functionality of Intelligent Transportation Systems (ITS). Adaptive decision-making mechanisms are crucial as transportation networks become increasingly complex, generating vast real-time data from vehicles, infrastructure, and users. AIGC plays a transformative role in optimizing traffic flow through dynamic routing and real-time traffic management. At the same time, human intelligence ensures that these systems remain responsive to ever-changing real-world conditions. In terms of safety, AIGC is employed to simulate complex driving scenarios for autonomous vehicle training and detect traffic anomalies, with human oversight providing contextual decisionmaking in unexpected or ambiguous situations. For sustainability, AIGC develops data-driven strategies to reduce emissions and energy consumption, while human expertise ensures alignment with ethical and environmental goals. This synergy between AIGC and human intelligence is vital for refining real-time decision-making, ensuring both accuracy and adaptability across diverse ITS scenarios. This paper offers a comprehensive literature review on core and supporting AIGC technologies and their applications in key ITS domains. Case studies and initiatives from industry leaders demonstrate practical implementations of AIGC-driven HMI collaboration in ITS. We also address challenges such as compatibility with legacy systems, data privacy, model bias, and scalability. The paper concludes by outlining future research directions, emphasizing the need for scalable, interpretable, and ethically compliant AIGC models. By prioritizing humanmachine intelligence, this work advances the safe, efficient, and sustainable deployment of AIGC-driven HMI solutions in modern transportation networks.