VCSEL chip structure and packaging optimization design for high-power VCSEL light source modules

The introduction to autonomous vehicles and intelligent transportation systems, as well as related work on autonomous vehicles and intelligent transportation systems, are described in this chapter. Aside from that, it has a chapter on autonomous intelligent vehicles and autonomous vehicles. Issues and challenges in autonomous intelligent vehicles, as well as the framework of an intelligent transportation system. In addition, the chapter discusses research opportunities in autonomous intelligent vehicles, IPv4 and IPv6 issues, and the future of IPv9 for vehicles, as well as a survey on autonomous vehicles in highway scenarios. During the last century, the automobile industry has excelled at producing cars that are both safe and affordable. Because of significant advancements in the Internet of things (IoT), the programmable logic controller (PLC), and other aspects of computing, autonomous vehicles (AVs) are becoming a reality. Cars, trucks, and other vehicles that don’t need to be plugged in to get power are referred to as self-sufficient vehicles. The multi-agent-based transportation system, which demonstrates how vehicles, drivers, roads, infrastructure, and vehicles all work together, is one of the most important intelligent transportation systems because it demonstrates how all of these things work together. To solve and manage traffic problems, an intelligent transportation systems (ITS) system employs a variety of technologies. Communication and control, vehicle sensing, and electronics are among these technologies. ITS has been used in the developed world for the past two decades. It is still a novel concept in the developing world. Sensors and computers work together in the Internet of things to store and process data, and data analytics is used to make the traffic system run more smoothly.KeywordsAutonomous vehicle (AV)Intelligent transportation system (ITS)Autonomous intelligent vehicle (AIV)Advanced driver assistance system (ADAS)Advanced driving system (ADS)

Gigaphoton Inc. develops a high-power laser produced plasma extreme ultraviolet (LPP EUV) light source for high volume manufacturing which enables sub-10nm critical layer patterning for semiconductor device fabrication. A technology update of key components of a 100 W LPP-EUV light source is given in this paper. The key components efficiently produce a stable plasma and evacuate the tin debris from the EUV vessel with a magnetic debris mitigation system. The chosen technology guarantees therefore a high-power and long-life EUV light source system. Each component is described with updated data. The latest system performance results are also presented. They were obtained from our proto LPP-EUV light systems which support 100 W output power.

Autonomous vehicles represent a paradigm shift in transportation and are taking center stage, development lies in the indispensable role of artificial intelligence (AI). The integration of AI algorithms in these vehicles incorporate a range of sophisticated technologies: machines, computer vision, sensor fusion and deep neural networks that together enable to navigate, perceive and make decisions in real time. AI is for cognition Behind these vehicles are intellectual capabilities that process immense amounts of sensor data in order to interpret it and respond to dynamic environments. Predictive modelling capabilities allow you to predict reactions to possible dangers and thus guarantee higher safety standards. However, the influence of AI in autonomous vehicles goes beyond efficiency and safety, it contributes to the renovation of urban landscapes that impact infrastructure development and accessibility while improving ethics. Concerns. Addressing the privacy, liability and ethical implications of AI-driven decision-making remains critical and highlights the need for robust regulatory frameworks. Despite it, Ongoing research and cross-industry collaboration efforts are further refining the role of AI in these vehicles, with the aim of maximizing their social benefit and further improving safety, reliability and Integration into everyday life.

This paper proposes that Artificial Intelligence (AI) progresses through several overlapping generations: AI 1.0 (Information AI), AI 2.0 (Agentic AI), AI 3.0 (Physical AI), and a speculative AI 4.0 (Conscious AI). Each AI generation is driven by shifting priorities among algorithms, computing power, and data. AI 1.0 accompanied breakthroughs in pattern recognition and information processing, fueling advances in computer vision, natural language processing, and recommendation systems. AI 2.0 is built on these foundations through real-time decision-making in digital environments, leveraging reinforcement learning and adaptive planning for agentic AI applications. AI 3.0 extended intelligence into physical contexts, integrating robotics, autonomous vehicles, and sensor-fused control systems to act in uncertain real-world settings. Building on these developments, the proposed AI 4.0 puts forward the bold vision of self-directed AI capable of setting its own goals, orchestrating complex training regimens, and possibly exhibiting elements of machine consciousness. This paper traces the historical foundations of AI across roughly 70 years, mapping how changes in technological bottlenecks from algorithmic innovation to high-performance computing to specialized data have stimulated each generational leap. It further highlights the ongoing synergies among AI 1.0, 2.0, 3.0, and 4.0, and explores the ethical, regulatory, and philosophical challenges that arise when artificial systems approach (or aspire to) human-like autonomy. Ultimately, understanding these evolutions and their interdependencies is pivotal for guiding future research, crafting responsible governance, and ensuring that AI’s transformative potential benefits society.

Advanced Driving Assistant Systems, intelligent and autonomous vehicles are promising solutions to enhance road safety, traffic issues and passengers' comfort. Such applications require advanced computer vision algorithms that demand powerful computers with high-speed processing capabilities. Keeping intelligent vehicles on the road until its destination, in some cases, remains a great challenge, particularly when driving at high speeds. The first principle task is robust navigation, which is often based on system vision to acquire RGB images of the road for more advanced processing. The second task is the vehicle's dynamic controller according to its position, speed and direction. This paper presents an accurate and efficient road boundaries and painted lines' detection algorithm for intelligent and autonomous vehicle. It combines Hough Transform to initialize the algorithm at each time needed, and Canny edges' detector, least-square method and Kalman filter to minimize the adaptive region of interest, predict the future road boundaries' location and lines parameters. The scenarios are simulated on the Pro-SiVIC simulator provided by Civitec, which is a realistic simulator of vehicles' dynamics, road infrastructures, and sensors behaviors, and OPAL-RT product dedicated for real time processing and parallel computing.

Recent developments in emerging technology, such as artificial intelligence, robotics, and autonomous vehicles, have generated inquiries about the incorporation of these technologies into the law, as well as the philosophical and ethical questions that accompany their use. This paper considers existing legal doctrine and regulatory frameworks as applied to the niche area of Level 3 autonomous vehicles. Specifically, this paper considers tort liability in the form of negligence and products liability, privacy risks, and regulatory considerations in relation to Level 3 vehicles. Finally, societal risks and potential solutions are considered to provide insight into potential areas of additional work.

Artificial intelligence (AI) used in autonomous vehicles (AV) to check for driver alertness is a critical piece of technology that makes the decision to hand over control to the human if there is a disengagement of the autonomous capability. It is important that this AI be inclusive without bias, because treating drivers differently will have an impact on the safety of humans not only in the vehicle but also on the road. This paper evaluates the AI that powers driver attention systems in the car to check if the AI treats all humans inclusively the same way beyond their ethnicity, gender and age, and whether it follows AI ethics principles of trust, transparency and fairness. Driver attention is built using two different AI models. One uses camera data to recognise humans and the other evaluates whether the human is alert. We found that both these AI models are biased and not inclusive of all people in all situations. We also found that there are unethical practices in how humans are tracked to check for alertness by using infrared sensors that track their retina movements without any concept of consent or privacy of people being tracked in the vehicles. The paper builds upon prior research on face detection outside the car and research that shows that car cognition AI does not recognise all humans on the road equally. We present research results about how the car is biased against some humans in its face identification and how the assertion of alertness of humans to hand over control during an emergency is fundamentally flawed in its definition of alertness. We recommend mitigation techniques and call for further research to build upon our work to make the AV inclusive with bias mitigation of bias in all forms of AI in AVs.

The paper is concerned with the visions and threats of Artificial Intelligence. It characterizes and describes those that, according to experts and scientists , will have a huge impact on the shape, nature and functioning of societies in the next two to three decades. Undoubtedly, one of the significant challenges is the development and application of deep learning. Scientific research in Artificial Intelligence will be accompanied by intensive development of computer vision, convolutional networks and virtual, augmented and mixed reality. In the future, everything digital will be able to fall prey to counterfeiters. The essence and functioning of related Deepfake tools are explained and described in the paper. The development of Artificial Intelligence will result in a wide and versatile application of Biometrics. The paper describes the prospective areas of its use and draws attention to the problems associated with the construction of the oversized databases necessary for this purpose. In the era of a society characteristic of the development of the fourth industrial revolution, theway and means of automobile transportation and the necessary infrastructure will be radically transformed - autonomous vehicles will appear. The paper describes all levels of reference relating to the scope of control of an autonomous vehicle. Effective use of Artificial Intelligence solutions will not be possible without computers with enormous computing power. Theemergence of quantum computers will certainly solve this problem. The essence of quantum computer functioning is described in the next chapter. Artificial Intelligence can successfully perform many tasks better than humans. This will make Artificial Intelligence take on a huge economic value. Positions will be lost for both physical and mental workers. The question then becomes, where are we? Which professions can take over Artificial Intelligence and which ones will not move? What is the future of work done by humans? These issues are addressed in Chapter 9. The prospect of widespread and comprehensive use of Artificial Intelligence solutions will raise the following question, with respect to both humans and society. Can Artificial Intelligence optimize our happiness? The answer to such a question is not an easy one. The paper attempts to answer it. In the age of Robotics and Artificial Intelligence, we will be accompanied by, revolutionary changes in the way humans of entire societies function. There will be an urgent need to define a neweconomic model. Issues clarifying the nature and importance of the essential components of the said model are addressed in this work. Solutions and use of Artificial Intelligence are often accompanied by various kinds of failures. These can have many causes. The paper describes and explains, proposed by Yampolsky Roman, a kind of dysfunction pattern. The results of scientific research andpractical experience that the list of Artificial Intelligence dangers is very extensive. A representative list of risks from Artificial, along with the characteristics of each of them, is presented in the last part of the paper.

The integration of autonomous vehicles (AVs) into public transportation systems has the potential to transform urban mobility by enhancing efficiency, accessibility, and sustainability. As cities continue to expand, traditional transit systems face challenges such as traffic congestion, high operational costs, and limited coverage. AVs, with their ability to optimize routes, reduce human error, and operate continuously, offer a promising solution to improve public transit networks. The adoption of AV technology in public transportation can redefine mobility patterns, particularly by bridging first- and last-mile connectivity gaps, enhancing commuter convenience, and improving mobility for elderly and disabled passengers. Unlike conventional transit modes, autonomous buses, shuttles, and taxis can dynamically adjust routes based on real-time data, reducing travel time and increasing overall system efficiency. Additionally, AVs can function seamlessly with multi-modal transport networks, integrating with metro, rail, and bus services to create a more cohesive and accessible urban transit system. Beyond improving passenger experience, AVs offer significant economic and environmental benefits. Their reduced dependence on human drivers leads to lower labor costs, while advanced energy-efficient systems contribute to lower fuel consumption and carbon emissions. Autonomous public transit solutions also help alleviate traffic congestion by minimizing unnecessary stops and optimizing roadway usage through vehicle-to-infrastructure (V2I) and vehicle-to vehicle (V2V) communication. However, the transition to a fully autonomous public transport system is not without challenges. Cities and governments must address issues such as infrastructure readiness, regulatory frameworks, liability concerns, cyber security risks, and public acceptance to ensure a smooth and secure transition. By analyzing real-world case studies and pilot projects, this chapter provides insights into how different cities worldwide are integrating AVs into their transit systems. Several pilot programs have demonstrated the feasibility of self driving shuttle services, particularly in urban areas with high commuter demand and limited transit accessibility. Furthermore, technological advancements in artificial intelligence (AI), machine learning, and sensor-based navigation continue to refine AV capabilities, making them increasingly viable for large-scale deployment in public transportation. This chapter also explores future trends, policy recommendations, and best practices that can accelerate AV adoption in public transport. Governments, urban planners, and transportation authorities must collaborate to design regulatory policies that encourage innovation while ensuring passenger safety and data security. Investment in smart infrastructure, including intelligent traffic management systems and dedicated AV lanes, will be crucial for optimizing AV operations in public transit. Ultimately, the successful integration of AVs into public transportation can lead to a more efficient, inclusive, and sustainable urban mobility ecosystem. With the right combination of technological innovation, policy support, and public engagement, AVs have the potential to redefine how people commute in the cities of the future, offering safe, affordable, and environmentally friendly solutions.

We are only at the beginning of a rapid period of transformation of our economy and society due to the convergence of many digital technologies. Artificial Intelligence (AI) is central to this change and offers major opportunities to improve our lives. The recent developments in AI are the result of increased processing power, improvements in algorithms and the exponential growth in the volume and variety of digital data. Many applications of AI have started entering into our every-day lives, from machine translations, to image recognition, and music generation, and are increasingly deployed in industry, government, and commerce. Connected and autonomous vehicles, and AI-supported medical diagnostics are areas of application that will soon be commonplace. There is strong global competition on AI among the US, China, and Europe. The US leads for now but China is catching up fast and aims to lead by 2030. For the EU, it is not so much a question of winning or losing a race but of finding the way of embracing the opportunities offered by AI in a way that is human-centred, ethical, secure, and true to our core values. The EU Member States and the European Commission are developing coordinated national and European strategies, recognising that only together we can succeed. We can build on our areas of strength including excellent research, leadership in some industrial sectors like automotive and robotics, a solid legal and regulatory framework, and very rich cultural diversity also at regional and sub-regional levels. It is generally recognised that AI can flourish only if supported by a robust computing infrastructure and good quality data: • With respect to computing, we identified a window of opportunity for Europe to invest in the emerging new paradigm of computing distributed towards the edges of the network, in addition to centralised facilities. This will support also the future deployment of 5G and the Internet of Things. • With respect to data, we argue in favour of learning from successful Internet companies, opening access to data and developing interactivity with the users rather than just broadcasting data. In this way, we can develop ecosystems of public administrations, firms, and civil society enriching the data to make it fit for AI applications responding to European needs. We should embrace the opportunities afforded by AI but not uncritically. The black box characteristics of most leading AI techniques make them opaque even to specialists. AI systems are currently limited to narrow and well-defined tasks, and their technologies inherit imperfections from their human creators, such as the well-recognised bias effect present in data. We should challenge the shortcomings of AI and work towards strong evaluation strategies, transparent and reliable systems, and good human-AI interactions. Ethical and secure-by-design algorithms are crucial to build trust in this disruptive technology, but we also need a broader engagement of civil society on the values to be embedded in AI and the directions for future development. This social engagement should be part of the effort to strengthen our resilience at all levels from local, to national and European, across institutions, industry and civil society. Developing local ecosystems of skills, computing, data, and applications can foster the engagement of local communities, respond to their needs, harness local creativity and knowledge, and build a human-centred, diverse, and socially driven AI. We still know very little about how AI will impact the way we think, make decisions, relate to each other, and how it will affect our jobs. This uncertainty can be a source of concern but is also a sign of opportunity. The future is not yet written. We can shape it based on our collective vision of what future we would like to have. But we need to act together and act fast.

As artificial intelligence (AI) systems become increasingly integrated into various aspects of daily life, ethical considerations surrounding their development and deployment are paramount. This paper aims to explore the intersection of AI technology and ethical theory, examining how principles such as fairness, accountability, and transparency can be integrated into AI design and deployment. Through an examination of case studies in sectors like autonomous vehicles, healthcare, and social media, we underscore the possible impacts of deploying AI unethically. Additionally, this paper emphasizes the need for ethical guidelines and regulatory frameworks to promote responsible AI usage. By integrating existing literature and case studies, this research seeks to enhance understanding of the ethical landscape in AI and offer recommendations for stakeholders to support a fair technological future. KEYWORDS Artificial Intelligence (AI), Ethics, Fairness, Accountability, Case Studies, Autonomous Vehicles, deployment.

iCab Use Case for ROS-based Architecture

New approaches to 3D vision are enabling new advances in artificial intelligence and autonomous vehicles, a better understanding of how animals navigate the 3D world, and new insights into human perception in virtual and augmented reality. Whilst traditional approaches to 3D vision in computer vision (SLAM: simultaneous localization and mapping), animal navigation (cognitive maps), and human vision (optimal cue integration) start from the assumption that the aim of 3D vision is to provide an accurate 3D model of the world, the new approaches to 3D vision explored in this issue challenge this assumption. Instead, they investigate the possibility that computer vision, animal navigation, and human vision can rely on partial or distorted models or no model at all. This issue also highlights the implications for artificial intelligence, autonomous vehicles, human perception in virtual and augmented reality, and the treatment of visual disorders, all of which are explored by individual articles. This article is part of a discussion meeting issue 'New approaches to 3D vision'.

The rapid advancements in deep learning (DL) and artificial intelligence (AI) have led to transformative applications across various domains. Edge devices, including mobile and embedded systems, are crucial for the widespread adoption of machine intelligence. However, training and deploying deep neural networks (DNNs) require substantial computational resources, resulting in high energy consumption. This energy often stems from non-renewable sources, leading to significant carbon dioxide emissions and environmental damage. With the emergence of large-scale models for AI-generated content (AIGC), such as large language models and diffusion models, the energy consumption issue has intensified, creating an urgent demand for sustainable AI solutions. These challenges motivate my research on energy-efficient deep learning toward sustainable AI. To build sustainable AI systems, this thesis works on promoting AI sustainability and using AI for sustainability purposes. To promote AI sustainability, a hardware-software co-design approach is adopted, integrating both bottom-up and top-down strategies for practical, energy-efficient deep learning deployments.The first part of the thesis introduces the bottom-up approach, emphasizing AI algorithm-aware efficient system design. Specifically, edge devices utilize dynamic power management strategies, such as dynamic voltage and frequency scaling (DVFS), to manage energy consumption by adjusting voltage-frequency (VF) levels. System-level configurations, including CPU cores, CPU VF levels, and GPU VF levels, significantly impact both latency and power consumption during DNN inference. Developing effective strategies for these configurations enables system-level optimizations, providing a path to deal with the efficiency challenge. The second part of the thesis presents the top-down approach, which focuses on designing hardware-driven efficient AI algorithms. Given the hardware constraints in AI application environments, this approach explores efficient compression techniques, such as weight pruning and knowledge distillation, to create lightweight models with reduced computational demands, thereby enhancing efficiency. The third part of the thesis examines the efficiency challenges posed by the evolution of large-scale models, which have greatly enhanced the capabilities of AIGC for text and image generation. While these models offer substantial generative potential, they also exacerbate the efficiency dilemma. This section explores strategies to optimize the efficiency of these large-scale models based on their unique characteristics. Beyond efficiency, the deployment of AI in real-world applications, such as autonomous vehicles, underscores the importance of prioritizing safety. The fourth part of the thesis addresses issues of AI fairness, trustworthiness, and security to ensure sustainable AI. This section investigates the potential to reverse-engineer adversarial attacks to infer the adversary's underlying information. In conclusion, this thesis works on building energy-efficient DL for sustainable AI. The objective of the work is not only to enhance the efficiency of AI systems but also to address broader concerns of fairness, trust, and security, paving the way for responsible and sustainable AI deployment in real-world scenarios.--Author's abstract

The rapid evolution of Artificial Intelligence (AI) promises transformative impacts across diverse sectors. This article explores key futuristic trends in AI, including the pursuit of General Artificial Intelligence (AGI) with human-like cognitive abilities. Addressing transparency concerns, Explainable AI (XAI) aims to elucidate decision- making processes. In healthcare, AI drives drug discovery and personalized medicine, while in climate change mitigation, it analyzes environmental data for sustainable practices. Autonomous vehicles, AI in education, and the fusion of AI with creativity showcase AI's broadening influence. Quantum AI, AI ethics, and Brain-Computer Interfaces (BCIs) represent cutting-edge developments. Emphasizing ethical frameworks and collaboration, the article envisions a future where AI, guided by responsibility, collaborates with humanity to address global challenges and promote inclusivity.