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Traffic congestion is a serious problem in the USA that affects safety, economy, environments, and human lives. Autonomous vehicles (AVs) equipped with vehicle-to-everything (V2X) communication technology is emerging as a viable solution to mitigate traffic congestion. In this dissertation, we propose an advanced traffic control system for autonomous vehicles, utilizing machine learning techniques, to alleviate traffic congestion, and enhance traffic efficiency and safety. The proposed system consists of two key components: an intelligent adaptive cruise control system (ACC) and a cooperative lane-change system. To address the limitations of existing static model-based approaches, we introduce a novel AI-based ACC system that dynamically adjusts the ACC settings based on real-time traffic conditions. By adapting to changing situations, this system significantly improves traffic efficiency. However, we recognize that current intelligent ACC systems primarily focus on traffic flow enhancement, disregarding the influence of adaptive inter-vehicle gap adjustment on driving safety and comfort. To bridge this gap, we develop a Safety-Aware Intelligent ACC system, which effectively assesses driving safety by dynamically updating safety model parameters according to varying traffic conditions. This innovative approach ensures that driving safety and comfort are prioritized alongside traffic efficiency. Furthermore, we present a novel multi-agent reinforcement learning (MARL)-based intelligent lane-change system for autonomous vehicles. This system optimizes both local and global performance by incorporating a road-side unit (RSU) responsible for managing a specific road segment, as well as vehicle-to-everything (V2X) capabilities for the agents. This density-aware cooperative multi-agent framework enables efficient and safe lane changes, considering the overall traffic conditions and maximizing the benefits for all vehicles involved. Finally, we present a use case scenario of our proposed next-generation traffic control system by designing an intelligent adaptive motion control system for electric vehicles (EVs) which facilitates an EV to control its motion to align with the position where the electromagnetic strength is expected to be maximal to receive maximum charging efficiency. By combining the AI-based ACC system and the MARL-based intelligent lane-change system, our next-generation traffic control system for autonomous vehicles aims to revolutionize traffic management, offering improved efficiency and safety for autonomous vehicles on the roads of the future.
The next generation of autonomous vehicles will provide major improvements in traffic flow, fuel efficiency, and vehicle safety. Several challenges currently prevent the deployment of autonomous vehicles, one aspect of which is robust and adaptable vehicle control. Designing a controller for autonomous vehicles capable of providing adequate performance in all driving scenarios is challenging due to the highly complex environment and inability to test the system in the wide variety of scenarios which it may encounter after deployment. However, deep learning methods have shown great promise in not only providing excellent performance for complex and non-linear control problems, but also in generalizing previously learned rules to new scenarios. For these reasons, the use of deep neural networks for vehicle control has gained significant interest. In this book, we introduce relevant deep learning techniques, discuss recent algorithms applied to autonomous vehicle control, identify strengths and limitations of available methods, discuss research challenges in the field, and provide insights into the future trends in this rapidly evolving field.
The book provides a broad overview of the challenges and recent developments in the field of smart mobility and transportation, including technical, algorithmic and social aspects of smart mobility and transportation. It reviews new ideas for services and platforms for future mobility. New concepts of artificial intelligence and the implementation in new hardware architecture are discussed. In the context of artificial intelligence, new challenges of machine learning for autonomous vehicles and fleets are investigated. The book also investigates human factors and social questions of future mobility concepts. The goal of this book is to provide a holistic approach towards smart transportation. The book reviews new technologies such as the cloud, machine learning and communication for fully atomatized transport, catering to the needs of citizens. This will lead to complete change of concepts in transportion.
This book covers the start-of-the-art research and development for the emerging area of autonomous and intelligent systems. In particular, the authors emphasize design and validation methodologies to address the grand challenges related to safety. This book offers a holistic view of a broad range of technical aspects (including perception, localization and navigation, motion control, etc.) and application domains (including automobile, aerospace, etc.), presents major challenges and discusses possible solutions.
Approximately 3700 people die in traffic accidents each day. The most frequent cause of accidents is human error. Autonomous driving can significantly reduce the number of traffic accidents. To prepare autonomous vehicles for road traffic, the software and system components must be thoroughly validated and tested. However, due to their criticality, there is only a limited amount of data for safety-critical driving scenarios. Such driving scenarios can be represented in the form of time series. These represent the corresponding kinematic vehicle movements by including vectors of time, position coordinates, velocities, and accelerations. There are several ways to provide such data. For example, this can be done in the form of a kinematic model. Alternatively, methods of artificial intelligence or machine learning can be used. These are already being widely used in the development of autonomous vehicles. For example, generative algorithms can be used to generate safety-critical driving data. A novel taxonomy for the generation of time series and suitable generative algorithms will be described in this paper. In addition, a generative algorithm will be recommended and used to demonstrate the generation of time series associated with a typical example of a driving-critical scenario.
During the past decade model predictive control (MPC), also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems. This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC). The main topic areas that appear to be of central importance for NMPC are covered, namely receding horizon control theory, modeling for NMPC, computational aspects of on-line optimization and application issues. The book consists of selected papers presented at the International Symposium on Nonlinear Model Predictive Control – Assessment and Future Directions, which took place from June 3 to 5, 1998, in Ascona, Switzerland. The book is geared towards researchers and practitioners in the area of control engineering and control theory. It is also suited for postgraduate students as the book contains several overview articles that give a tutorial introduction into the various aspects of nonlinear model predictive control, including systems theory, computations, modeling and applications.
Elon Musk thought that his company Tesla will have fully autonomous cars ready by the end of 2020. "There are no fundamental challenges left," he said recently. "There are a number of minor issues. And then there's a struggle to solve all these little problems and bring the whole thing together." Although the technology to allow a car to complete a journey without human interference (what the industry calls "level 5 autonomy") can move quickly, the development of a vehicle that can do so safely and legally is another matter. The novelty of autonomous technology is intended to turn our legal and social ties into daily transport. Importantly, without a driver behind the wheel, autonomous vehicles raise concerns about the liability and responsibility for the conduct of the lane. Therefore, this book is structured to answer many questions about autonomous vehicles and make you not only understand all the aspects of this emerging technology, but master the discussions and debates about the following topics: Chapter One: The rise of autonomous vehicles Autonomous vehicles become reality History of Autonomous vehicles Road Items Weights Society of Automotive Engineers (SAE International) Chapter Two: Tesla Autopilot AutoPilot AI Advanced Sensor Coverage Wide, Main and Narrow Forward Cameras Wide Main Narrow Forward Looking Side Cameras Rearward Looking Side Cameras Rear View Camera Radar Ultrasonic Sensors Processing Power Increased 40x Tesla Vision Autopilot Navigate on Autopilot Autosteer+ Smart Summon Full Self-Driving Capability From Home To your Destination Chapter Three: A level-by-level explainer of autonomous vehicles Classification System For The Development Of Innovations The J3016 Guidelines Six SAE level Criticism of SAE classification Level 0: No automation Level 1: Driver assistance Level 2: Partial automation Level 3: Conditional automation Level 4: High automation Level 5: Full automation Chapter Four: Main Connectivity Specifications Of Autonomous Vehicles Vehicle-To-Everything Architectures must be both redundant and real-time. The demand for high-speed data would increase only Security and other applications Include external connectivity Autonomous driving efficiency and reliability are non-negotiable More and more electrified cars would need a new approach to safety Next generation Car Design Would Need Miniaturized Solutions Co-creation of the future of mobility Chapter Five: Building Passenger Trust Is Key Technology for self-driving cars is accelerating fast, but our driverless future isn't going anywhere if people don't trust it. rules of the road implicit laws are more challenging The math-based AV safety model What is Sensitive Protection Responsibility? RSS is compatible with other AV systems How are AVs safely sharing the road with human drivers? 01 Safe distance: Don't hit the car in front of you 02 Cutting in: Don't cut it in recklessly 03 Right of Way: Right of way is given, not taken 04 Limited Visibility: Be cautious in areas with limited visibility 05 Avoid Crashes: If you can avoid a crash without causing another one, you must Moving past the miles-driven Improving road safety with RSS today RSS to gain support Baidu Valeo China ITS Alliance RAND Corp. The Arizona Institute for Automated Mobility Joint Research Institutes Chapter Six: The reasons Autonomous vehicles still aren’t on our roads The Gap Between the Invention and The Application Sensors Machine Learning The Open Road Regulations Social Acceptability Chapter Seven: Legal frameworks and other national initiatives The United States European Union Membership United Arab Emirates Japan Australia Chapter Eight: Liability, ethics and human rights implications The novelty of autonomous vehicles The critical debate Autonomy Threats Chapter Nine: Leading opinions on an ethical rollout for autonomous vehicles The Three Laws of Robotics The Ethical Dilemmas of Autonomy The Worst-Case Scenario The Trolley Issue Chapter Ten: Social and economic implications Roads Safety Vehicles Ownership and Vehicles Insurance Jobs Chapter Eleven: Ongoing research and impediments to autonomous vehicle development Research and Development The Social Acceptance of Autonomous Vehicles Chapter Twelve: The Sensor Types Drive Autonomous Vehicles Multiple Redundant Sensor Systems Overview of the study SAE Levels Short DESCRIPTIONS No car manufacturer has reached level 3 or higher Which sensors are needed? Camera and LIDAR Systems Cameras Back and 360° cameras Front-Facing Camera Systems RADAR Sensor LiDAR Summary and insight
With the rapid development of artificial intelligence and the emergence of various new sensors, autonomous driving has grown in popularity in recent years. The implementation of autonomous driving requires new sources of sensory data, such as cameras, radars, and lidars, and the algorithm processing requires a high degree of parallel computing. In this regard, traditional CPUs have insufficient computing power, while DSPs are good at image processing but lack sufficient performance for deep learning. Although GPUs are good at training, they are too “power-hungry,” which can affect vehicle performance. Therefore, this book looks to the future, arguing that custom ASICs are bound to become mainstream. With the goal of ICs design for autonomous driving, this book discusses the theory and engineering practice of designing future-oriented autonomous driving SoC chips. The content is divided into thirteen chapters, the first chapter mainly introduces readers to the current challenges and research directions in autonomous driving. Chapters 2–6 focus on algorithm design for perception and planning control. Chapters 7–10 address the optimization of deep learning models and the design of deep learning chips, while Chapters 11-12 cover automatic driving software architecture design. Chapter 13 discusses the 5G application on autonomous drving. This book is suitable for all undergraduates, graduate students, and engineering technicians who are interested in autonomous driving.
In recent years the applications of advanced information technologies in the field of transportation have affected both road infrastructures and vehicle technologies. The development of advanced transport telematics systems and the implementation of a new generation of technological options in the transport environment have had a significant impact on improved traffic management, efficiency and safety. This volume contains contributions from scientific and academic centres which have been active in this field of research and provides an overview of applications of AI technology in the field of traffic control and management. The topics covered are: -- current status of AI in transport -- AI applications in traffic engineering -- in-vehicle AI
Control of large-scale distributed energy systems over communication networks is an important topic with many application domains. The book presents novel concepts of distributed control for networked and cyber-physical systems (CPS), such as smart industrial production lines, smart energy grids, and autonomous vehicular systems. It focuses on new solutions in managing data and connectivity to support connected and automated vehicles (CAV). The book compiles original research papers presented at the conference “Networked Control Systems for Connected and Automated Vehicles” (Russia). The latest connected and automated vehicle technologies for next generation autonomous vehicles are presented. The book sets new goals for the standardization of the scientific results obtained and the advancement to the level of full autonomy and full self-driving (FSD). The book presents the latest research in artificial intelligence, assessing virtual environments, deep learning systems, and sensor fusion for automated vehicles. Particular attention is paid to new safety standards, safety and security systems, and control of epidemic spreading over networks. The issues of building modern transport infrastructure facilities are also discussed in the articles presented in this book. The book is of considerable interest to scientists, researchers, and graduate students in the field of transport systems, as well as for managers and employees of companies using or producing equipment for these systems.