Advances in Intelligent Vehicle Control

Advanced intelligent vehicle control systems have evolved in the last few decades thanks to the use of artificial-intelligence-based techniques, the appearance of new sensors, and the development of technology necessary for their implementation. Therefore, a substantial improvement in vehicle safety, comfort, and performance has been achieved. The appearance of new vehicles with new technologies incorporated in them requires new control strategies that will continue to increase handling, stability, and energy efficiency. In recent years, intelligent vehicle control has been widely investigated from different points of view. Many researchers have studied active safety systems, advanced driver assistance systems, autonomous driver systems, etc., through strategies incorporating aspects of artificial intelligence, making them adapt and learn from situations never explored before. To achieve this, it has been necessary to develop increasingly precise dynamic vehicle models and incorporate new intelligent sensors and sensor fusion techniques to learn the vehicle’s state accurately. However, it is important to observe not only the state of the vehicle where these systems are incorporated but also those of vehicles around it that can influence the vehicle’s behavior. This requires communication between vehicles and developing architectures that enable smart transportation. On the other hand, the incorporation of electric vehicles (EVs) in recent years has enabled a new way of focusing on vehicle control systems, fundamentally due to the incorporation of new systems that must be studied differently. (...)Partial funding for open access charge: Universidad de Málag

Artificial Intelligence and the Ethics of Self-learning Robots

The convergence of robotics technology with the science of artificial intelligence ( or AI) is rapidly enabling the development of robots that emulate a wide range of intelligent human behaviors.1 Recent advances in machine learning techniques have produced significant gains in the ability of artificial agents to perform or even excel in activities formerly thought to be the exclusive province of human intelligence, including abstract problem-solving, perceptual recognition, social interaction, and natural language use. These developments raise a host of new ethical concerns about the responsible design, manufacture, and use of robots enabled with artificial intelligence-particularly those equipped with self-learning capacities. The potential public benefits of self-learning robots are immense. Driverless cars promise to vastly reduce human fatalities on the road while boosting transportation efficiency and reducing energy use. Robot medics with access to a virtual ocean of medical case data might one day be able to diagnose patients with far greater speed and reliability than even the best-trained human counterparts. Robots tasked with crowd control could predict the actions of a dangerous mob well before the signs are recognizable to law enforcement officers. Such applications, and many more that will emerge, have the potential to serve vital moral interests in protecting human life, health, and well-being. Yet as this chapter will show, the ethical risks posed by AI-enabled robots are equally serious-especially since self-learning systems behave in ways that cannot always be anticipated or folly understood, even by their programmers. Some warn of a future where Al escapes our control, or even turns against humanity (Standage 2016); but other, far less cinematic dangers are much nearer to hand and are virtually certain to cause great harms if not promptly addressed by technologists, lawmakers, and ocher stakeholders. The task of ensuring the ethical design, manufacture, use, and governance of AI-enabled robots and other artificial agents is thus as critically important as it is vast

Automotive Intelligence Embedded in Electric Connected Autonomous and Shared Vehicles Technology for Sustainable Green Mobility

The automotive sector digitalization accelerates the technology convergence of perception, computing processing, connectivity, propulsion, and data fusion for electric connected autonomous and shared (ECAS) vehicles. This brings cutting-edge computing paradigms with embedded cognitive capabilities into vehicle domains and data infrastructure to provide holistic intrinsic and extrinsic intelligence for new mobility applications. Digital technologies are a significant enabler in achieving the sustainability goals of the green transformation of the mobility and transportation sectors. Innovation occurs predominantly in ECAS vehicles’ architecture, operations, intelligent functions, and automotive digital infrastructure. The traditional ownership model is moving toward multimodal and shared mobility services. The ECAS vehicle’s technology allows for the development of virtual automotive functions that run on shared hardware platforms with data unlocking value, and for introducing new, shared computing-based automotive features. Facilitating vehicle automation, vehicle electrification, vehicle-to-everything (V2X) communication is accomplished by the convergence of artificial intelligence (AI), cellular/wireless connectivity, edge computing, the Internet of things (IoT), the Internet of intelligent things (IoIT), digital twins (DTs), virtual/augmented reality (VR/AR) and distributed ledger technologies (DLTs). Vehicles become more intelligent, connected, functioning as edge micro servers on wheels, powered by sensors/actuators, hardware (HW), software (SW) and smart virtual functions that are integrated into the digital infrastructure. Electrification, automation, connectivity, digitalization, decarbonization, decentralization, and standardization are the main drivers that unlock intelligent vehicles' potential for sustainable green mobility applications. ECAS vehicles act as autonomous agents using swarm intelligence to communicate and exchange information, either directly or indirectly, with each other and the infrastructure, accessing independent services such as energy, high-definition maps, routes, infrastructure information, traffic lights, tolls, parking (micropayments), and finding emergent/intelligent solutions. The article gives an overview of the advances in AI technologies and applications to realize intelligent functions and optimize vehicle performance, control, and decision-making for future ECAS vehicles to support the acceleration of deployment in various mobility scenarios. ECAS vehicles, systems, sub-systems, and components are subjected to stringent regulatory frameworks, which set rigorous requirements for autonomous vehicles. An in-depth assessment of existing standards, regulations, and laws, including a thorough gap analysis, is required. Global guidelines must be provided on how to fulfill the requirements. ECAS vehicle technology trustworthiness, including AI-based HW/SW and algorithms, is necessary for developing ECAS systems across the entire automotive ecosystem. The safety and transparency of AI-based technology and the explainability of the purpose, use, benefits, and limitations of AI systems are critical for fulfilling trustworthiness requirements. The article presents ECAS vehicles’ evolution toward domain controller, zonal vehicle, and federated vehicle/edge/cloud-centric based on distributed intelligence in the vehicle and infrastructure level architectures and the role of AI techniques and methods to implement the different autonomous driving and optimization functions for sustainable green mobility.publishedVersio

Big data analytics:Computational intelligence techniques and application areas

Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment

AI and OR in management of operations: history and trends

The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested