Autonomous vehicles rely on complex sensing systems to perceive their environment and ensure safe operation. This review analyses the main sensor technologies used in self-driving vehicles, including cameras, LiDAR, radar, ultrasonic sensors and GNSS/IMU-based localisation systems. A core set of 40 primary research articles was systematically analysed to compare the capabilities, limitations and integration challenges of sensing technologies used in autonomous vehicles. In addition to these primary studies, further references were included to provide background information and describe emerging developments in autonomous sensing systems. The review shows that no single sensor technology can provide reliable perception under all environmental conditions. Camera systems offer rich visual information but are sensitive to lighting and weather conditions, while LiDAR provides highly accurate three-dimensional geometry but suffers from signal attenuation in rain and fog. Radar sensors demonstrate superior robustness in adverse weather and enable direct velocity measurement, although their spatial resolution remains limited compared to optical sensors. As a result, modern autonomous vehicles rely on multi-sensor fusion architectures that combine complementary sensing modalities to improve reliability and safety. The analysis also identifies several key research gaps in the current literature. In particular, there is a lack of systematic evaluation of trade-offs between sensor performance, computational requirements and vehicle energy consumption. Furthermore, the safety certification of artificial intelligence-based perception systems and the integration of emerging technologies such as FMCW LiDAR and terahertz radar remain open research challenges. Overall, the results suggest that the future of autonomous vehicle perception will depend not only on improvements in individual sensors but also on robust sensor fusion architectures, safety-certified AI models and energy-efficient sensor processing platforms. These findings provide guidance for researchers and engineers developing next-generation sensing systems for autonomous driving.

The rapid progress of sensing technologies, artificial intelligence, and embedded computing has significantly accelerated the development of autonomous vehicles. Among the core challenges of higher-level driving automation, reliable environmental perception remains one of the most critical. This review presents a systematic PRISMA-based analysis of multimodal sensor technologies and fusion architectures applied in autonomous driving, based on 66 peer-reviewed studies published between 2014 and 2025. The study examines the operational characteristics, advantages, and limitations of major sensing modalities, including cameras, LiDAR, radar, ultrasonic sensors, and GNSS/IMU-based localization systems. Particular attention is given to multimodal fusion strategies, covering early, mid-level, high-level, and transformer-based architectures that combine complementary sensor information to improve perception robustness and decision reliability. The review further synthesizes current evidence on performance under adverse environmental conditions, benchmark validation practices, real-time computational constraints, and the growing role of functional safety frameworks such as ISO 26262 and SOTIF. Emerging research directions, including 4D radar, self-supervised long-range fusion, foundation models, and cooperative V2X perception, are also discussed. The findings indicate that multimodal sensor fusion is a highly effective architectural strategy for improving scalability, fail-operational robustness, and certifiable safety in autonomous driving systems, particularly in higher-level automation scenarios. Future research should focus on uncertainty-aware fusion, explainable cross-modal reasoning, large-scale real-world validation, and efficient hardware–software co-design to support robust Level 4–5 vehicle autonomy.

This paper explores the ever-changing relationship between China and the United States, the world's two largest economies, with a particular focus on trade imbalances and their far-reaching implications. Providing a comprehensive historical context, the study sheds light on the significance of the CN-US economic relationship and its profound influence on the global economy. Tracing the trajectory of CN-US trade from its early beginnings of direct exchange to recent trade frictions and tariff disputes, the analysis examines the role of statistical errors in reported trade imbalances and dissects the intricate factors contributing to the trade deficit. Emphasizing the urgency of a balanced CN-US relationship, the study advocates for a harmonious blend of cooperation and confrontation, ensuring equality, mutual benefit, and peaceful coexistence. Furthermore, the paper delves into China's impressive economic growth, its potential to surpass the US economy, and the possibility of the Chinese yuan replacing the US dollar as the global reserve currency. The paper concludes by underlining the critical importance of maintaining a stable and cooperative relationship between the two nations to steer global development in a positive direction. By addressing trade imbalances and fostering a collaborative approach, China and the United States can pave the way for a more equitable and prosperous global economic landscape. As the world watches the unfolding of this pivotal economic relationship, understanding its complexities is essential for policymakers and stakeholders alike.

Human decision-making plays a crucial role in the learning process, affecting engagement, motivation, information processing, and social interactions. Learners have the autonomy to select materials, study times, and strategies that suit their preferences and strengths. When faced with complex concepts, learners make cognitive choices that impact their understanding and knowledge application. Additionally, in collaborative settings, learners actively decide when and how to seek help and engage with peers.Understanding the significance of human choices in learning has important implications for educators and instructional designers. By fostering a supportive environment that encourages autonomy, active decision-making, and collaboration, educators can empower learners to take charge of their learning journey and improve outcomes.In conclusion, human choices shape the learning process, influencing engagement, motivation, information processing, and social interactions. Acknowledging and harnessing the power of decision-making can enhance educational practices and empower learners to become active agents in their learning journey.

A Global Perspective on the US-China Trade War: Causes, Consequences, and Implications. The US-China trade war, ignited by the protectionist rhetoric of former President Donald Trump, has had profound implications for global economic relations. This paper seeks to examine the chronological development of the trade war, its underlying causes, and the far-reaching consequences it has had on the relationship between the two superpowers. Additionally, it explores the potential ramifications for the Indo-Pacific region and the dynamics of the US-Russia relationship.The trade war has emerged as a complex issue, with both the US and China resorting to retaliatory measures, resulting in a vicious cycle of tariffs and sanctions. Through a thorough investigation, this paper aims to shed light on the impact of these measures on the competitive advantage of American companies compared to their Chinese counterparts. Furthermore, it delves into China's countermeasures and their effects on the country's economy and industrial development.To ensure a comprehensive analysis, the paper draws on a thorough literature review, referencing various studies that explore the root causes and economic impact of the trade war. It places the trade war within its historical context, drawing parallels with past conflicts such as the US-Japan trade war, and examines the political motivations behind the current tension between the US and China.The analysis incorporates relevant statistical data, including trade deficit figures and the composition of imports and exports between the two nations. By doing so, it aims to paint a clear picture of the economic ramifications of the trade war on both countries and the global economy.In conclusion, the paper underscores the necessity of adopting a pragmatic and rational approach to address the US-China trade war. It advocates for both nations to seek common ground while setting aside their differences to achieve mutually beneficial cooperation. Moreover, it emphasizes the significance of innovation, industrial upgrading, and international collaborations for China's economic development amid the ongoing trade war.By presenting a comprehensive examination of the US-China trade war and its implications, this paper contributes to a deeper understanding of the global economic landscape and the critical need for constructive engagement between major economies.

The rapid technological advancement of autonomous vehicles (AVs)—as exemplified by recent research such as Enhancing High-Speed Cruising Performance of Autonomous Vehicles Through Integrated Deep Reinforcement Learning Framework (IEEE Transactions on Intelligent Transportation Systems, 2025)—raises pressing societal questions about their acceptance. Although numerous studies have examined attitudes toward autonomous vehicles in recent years, most research is limited to a single country, age group, or small sample. As a result, there is still a lack of empirical analyses comparing multiple generations and conducted on large samples that would provide a comprehensive picture of the age-determining factors of social acceptance of AVs. The present study aims to fill this gap with a questionnaire survey of 8663 people, which examines the trust, technology acceptance, and willingness to use autonomous vehicles among the Baby Boomer, X, Y, Z, and Alpha generations using Likert scale items. We used the Kruskal–Wallis test to explore the differences between generations. Our results show significant differences: while Generations Z and Alpha show remarkable openness and trust in AV technology, the Baby Boomer generation shows marked skepticism. The novelty of the research is that it provides large-scale, multi-generational comparative data, thus shedding light from a new perspective on the cultural, experiential, and trust patterns that determine the social acceptance of autonomous vehicles, beyond technological factors. The limitations of the study include self-reported data collection, geographical limitations (Hungary), and the lack of actual driving experience among the Alpha generation, which may affect the validity of the responses. However, these factors point the way for future international and qualitative research. The results can provide practical guidance for decision-makers, developers, and urban planners in developing inclusive, generation-sensitive transportation policies and communication strategies.