The growing demand for equitable and efficient transportation solutions has positioned autonomous vehicles (AVs) as a transformative technology with significant potential for rural areas. This literature review examines the challenges and opportunities associated with AV deployment in rural environments, characterized by sparse infrastructure, diverse road conditions, and aging populations. Using a systematic analysis of field tests, simulation-based studies, and survey research, key obstacles are identified, including limited lane markings, unpaved roads, digital connectivity gaps, and user acceptance issues. The results highlight the critical role of advancements in sensor technology, localization methods, and edge computing in addressing these barriers. Additionally, strategic infrastructure modifications, such as enhanced road signage and reliable communication systems, are essential for AV integration. This paper emphasizes the need for tailored AV solutions to meet the specific requirements of rural settings, including adaptability to adverse weather conditions and mixed traffic environments. Insights into public perception reveal the importance of trust-building initiatives and community engagement to foster widespread acceptance. The findings provide actionable recommendations for policymakers, industry leaders, and infrastructure operators, focusing on scalable deployment strategies, policy adaptations, and sustainable solutions. By addressing these challenges, AVs enhance mobility, safety, and accessibility, transforming rural transportation networks into more equitable and efficient systems. This review serves as a foundational reference for future research, charting pathways for the integration of AVs in rural contexts.

Understanding automated vehicle (AV) behavior in complex road environments and user attitudes in such contexts is critical for their safe and effective integration into smart cities. Despite growing deployment, limited public data exist on AV performance in construction zones; highly dynamic settings marked by irregular lane markings, shifting detours, and unpredictable human presence. This study investigates AV behavior in these conditions through qualitative, video-based analysis of user-documented experiences on YouTube, focusing on Tesla’s supervised Full Self-Driving (FSD) and Waymo systems. Spoken narration, captions, and subtitles were examined to evaluate AV perception, decision-making, control, and interaction with humans. Findings reveal that while AVs excel in structured tasks such as obstacle detection, lane tracking, and cautious speed control, they face challenges in interpreting temporary infrastructure, responding to unpredictable human actions, and navigating low-visibility environments. These limitations not only impact performance but also influence user trust and acceptance. The study underscores the need for continued technological refinement, improved infrastructure design, and user-informed deployment strategies. By addressing current shortcomings, this research offers critical insights into AV readiness for real-world conditions and contributes to safer, more adaptive urban mobility systems.

Power outages pose considerable risks to the reliability of electric grids, affecting both consumers and utilities through service disruptions and potential economic losses. This study analyzes a historical outage dataset from a Regional Transmission Organization (RTO) to reveal key patterns and trends that suggest outage management strategies. By integrating exploratory data analysis, predictive modeling, and a Large Language Model (LLM)-based interface integration, as well as data visualization techniques, we identify and present critical drivers of outage duration and frequency. A random forest regressor trained on features including planned duration, facility name, outage owner, priority, season, and equipment type proved highly effective for predicting outage duration with high accuracy. This predictive framework underscores the practical value of incorporating planning information and seasonal context in anticipating outage timelines. The findings of this study not only deepen the understanding of temporal and spatial outage dynamics but also provide valuable insights for utility companies and researchers. Utility companies can use these results to better predict outage durations, allocate resources more effectively, and improve service restoration time. Researchers can leverage this analysis to enhance future models and methodologies for studying outage patterns, ensuring that artificial intelligence (AI)-driven methods can contribute to improving management strategies. The broader impact of this study is to ensure that the insights gained can be applied to strengthen the reliability and resilience of power grids or energy systems in general.