The flourishing realm of advanced driver-assistance systems (ADAS) as well as autonomous vehicles (AVs) presents exceptional opportunities to enhance safe driving. An essential aspect of this transformation involves monitoring driver behavior through observable physiological indicators, including the driver’s facial expressions, hand placement on the wheels, and the driver’s body postures. An artificial intelligence (AI) system under consideration alerts drivers about potentially unsafe behaviors using real-time voice notifications. This paper offers an all-embracing survey of neural network-based methodologies for studying these driver bio-metrics, presenting an exhaustive examination of their advantages and drawbacks. The evaluation includes two relevant datasets, separately categorizing ten different in-cabinet behaviors, providing a systematic classification for driver behaviors detection. The ultimate aim is to inform the development of driver behavior monitoring systems. This survey is a valuable guide for those dedicated to enhancing vehicle safety and preventing accidents caused by careless driving. The paper’s structure encompasses sections on autonomous vehicles, neural networks, driver behavior analysis methods, dataset utilization, and final findings and future suggestions, ensuring accessibility for audiences with diverse levels of understanding regarding the subject matter.

Traffic crashes are a leading cause of death and injury worldwide, with far-reaching societal and economic consequences. To effectively address this global health crisis, researchers and practitioners rely on the analysis of crash data to identify risk factors, evaluate countermeasures, and inform road safety policies. This systematic review synthesizes the state of the art in road crash data analysis methodologies, focusing on the application of statistical and machine learning techniques to extract insights from crash databases. We systematically searched for peer-reviewed studies on quantitative crash data analysis methods and synthesized findings by using narrative synthesis due to methodological diversity. Our review included studies spanning traditional statistical approaches, Bayesian methods, and machine learning techniques, as well as emerging AI applications. We review traditional and emerging crash data sources, discuss the evolution of analysis methodologies, and highlight key methodological issues specific to crash data, such as unobserved heterogeneity, endogeneity, and spatial–temporal correlations. Key findings demonstrate the superiority of random-parameter models over fixed-parameter approaches in handling unobserved heterogeneity, the effectiveness of Bayesian hierarchical models for spatial–temporal analysis, and promising results from machine learning approaches for real-time crash prediction. This survey also explores emerging research frontiers, including the use of big data analytics, deep learning, and real-time crash prediction, and their potential to revolutionize road safety management. Limitations include methodological heterogeneity across studies and geographic bias toward high-income countries. By providing a taxonomy of crash data analysis methodologies and discussing their strengths, limitations, and practical implications, this paper serves as a comprehensive reference for researchers and practitioners seeking to leverage crash data to advance road safety.

This comprehensive study provides an in-depth examination of the Internet of Things (IoT), which refers to the interconnection of multiple devices through various wireless protocols that facilitate data transfer and improve operational intelligence. IoT is widely used in numerous fields, including urban infrastructure, domestic settings, transportation systems, military operations, healthcare, and agriculture. However, with its growing prevalence comes a significant increase in security risks across multiple layers, such as hardware, software, cloud infrastructure, and networks. This study categorizes these vulnerabilities and explores how adversaries can exploit weaknesses to compromise IoT systems. In doing so, it highlights the risks associated with unauthorized access, data breaches, and system manipulation, all of which pose a direct threat to confidentiality, integrity, and availability. To address these concerns, this paper examines various mitigation strategies that aim to enhance IoT security by reducing attack surfaces, improving authentication methods, and securing communication protocols. By systematically analyzing existing vulnerabilities and countermeasures, this research contributes to the ongoing effort to fortify IoT devices and infrastructure against current and emerging threats. Through this study, we seek to advance the discussion on securing IoT environments while emphasizing the importance of proactive security measures in this rapidly evolving landscape.

This paper introduces the Automated Zero Trust Risk Management with DevSecOps Integration (AZTRM-D) framework, a novel approach that embeds security throughout the entire Secure Software and System Development Life Cycle (S-SDLC). AZTRM-D strategically unifies three established methodologies: DevSecOps practices, the NIST Risk Management Framework (RMF), and the Zero Trust (ZT) model. It then significantly augments their capabilities through the pervasive application of Artificial Intelligence (AI). This integration shifts traditional, often fragmented, security paradigms towards a proactive, automated, and continuously adaptive security posture. AI serves as the foundational enabler, providing real-time threat intelligence, automating critical security controls, facilitating continuous vulnerability detection, and enabling dynamic policy enforcement from initial code development through operational deployment. By automating key security functions and providing continuous oversight, AZTRM-D enhances risk mitigation, reduces vulnerabilities, streamlines compliance, and significantly strengthens the overall security posture of software systems, thereby addressing the complexities of modern cyber threats and accelerating the delivery of secure software.