## 2 Related Work
* **Mitigating Label Noise in Machine Learning and Object Detection in Infrared Images**: The study [21] explores the use of a structured transition matrix network (STMN) with an adversarial learning process to mitigate label noise. This method leverages human cognition to improve the estimation accuracy of the label transition matrix, converting noisy labels into true labels. The approach demonstrates superiority over state-of-the-art methods in both synthetic and real-world datasets. Research [22] introduces a novel backbone network, Deep-IRTarget, for object detection in infrared images. This method combines frequency and spatial feature extraction using a Hypercomplex Infrared Fourier Transform and convolutional neural network. The dual-domain feature resource allocation model enhances detection accuracy significantly across various infrared imagery databases. Few-shot learning approaches human-like fast learning with limited samples, and the paper [23] introduces a Part-Aware Correlation Network (PACNet) that uses Partial Representation (PR) and Semantic Covariance Matrix (SCM) to enhance understanding the label correlations, outperforming state-of-the-art methods with significant performance gains on benchmark datasets. The studies summarized above demonstrate diverse methodologies and approaches to understanding and improving driver interactions with smart car systems. While some focus on physiological metrics such as pupil diameter and EEG data [5], others utilize machine learning frameworks to classify cognitive and perceptual loads [6; 7]. The integration of multiple data streams, as seen in the ARecNet model [7], shows promise for enhancing real-time workload recognition, but the complexity and computational demands of such models pose challenges for practical implementation. Research on stress detection and drowsiness [8; 10] highlights the importance of robust detection methods in maintaining driver safety. However, these studies often rely on controlled simulations which may not fully capture the nuances of real-world driving conditions. The use of shared control algorithms [13] and decision-making models based on fuzzy logic [14] offers potential solutions for improving road safety and driver assistance, yet these approaches require accurate and comprehensive data to be effective. Reviews on fuzzy logic [15] and HVI for situational awareness [16] provide valuable insights into the evolution and challenges of these technologies. They underscore the need for adaptive and multimodal interfaces that can respond to varying driver states and environmental conditions. The exploration of AR interfaces [18] further contributes to this understanding by demonstrating how smart user interfaces can optimize situational awareness in complex driving scenarios. Overall, while significant progress has been made in developing methods for assessing and enhancing driver cognitive workload and situational awareness, there remain limitations related to the generalizability of findings from simulated environments to real-world applications. Future research should focus on validating these models in diverse driving conditions and exploring more adaptive and integrated interface solutions to support driver safety and performance. Additionally, these solutions should be capable of functioning in real-time, whereas currently, this is often not the case.