The aim of this paper is to investigate possible workflows for OOD pattern recognition in AI-based spectrogram analysis, applied in industrial manufacturing environment. First, we attempt to identify and articulate the challenges associated with OOD recognition in the context of spectrogram analysis, where the acoustic sources are subtle and often complex signals. These deserve particular attention, since the effectivity of OOD detection algorithms are acceptable in case of significant deviations, however, it is questionable for fine anomalies. In addition, it is also discussed here, how OOD records can affect the accuracy and reliability of AI models in terms of equipment failure identification and process inefficiencies. Last, methodes are proposed for OOD-pattern recognition. The integrability of these methods into existing manufacturing workflows in terms of practicality, adaptability and effectiveness are also investigated.

The development of autonomous transportation systems represents a critical step toward achieving intelligent and reliable mobility. Ensuring accurate, real-time environmental perception and the robust detection of unexpected or rare events remains a major challenge for autonomous vehicles operating in complex and dynamic environments. To address this, we propose a novel processing pipeline that constructs Bird’s Eye View (BEV) representations from raw 3D LiDAR point clouds using both intensity and height map channels, thereby retaining essential geometric and reflective features. On top of these BEV representations, an optimized YOLOv11-based deep learning model is applied for high-precision object detection. A key contribution of our work is the integration of a real-time Out-of-Distribution (OOD) detection module, which employs lightweight statistical techniques in conjunction with learned feature representations to ensure minimal computational overhead while maintaining operational robustness. The proposed architecture enables the reliable identification of standard traffic objects as well as the detection of atypical or previously unseen events, such as overturned vehicles or unknown obstacles. Experimental evaluation on representative driving scenarios demonstrates that our method achieves approximately 95% detection accuracy, outperforming conventional baselines in both speed and reliability. Overall, the results highlight the potential of combining state-of-the-art deep neural detection frameworks with efficient, statistically grounded OOD analysis for enhancing the safety and trustworthiness of autonomous vehicle perception systems.