Intelligent Sensing And Control For Autonomous Vehicle Following

This edited volume includes thoroughly collected on sensing and control for autonomous vehicles. Guidance, navigation and motion control systems for autonomous vehicles are increasingly important in land-based, marine and aerial operations. Autonomous underwater vehicles may be used for pipeline inspection, light intervention work, underwater survey and collection of oceanographic/biological data. Autonomous unmanned aerial systems can be used in a large number of applications such as inspection, monitoring, data collection, surveillance, etc. At present, vehicles operate with limited autonomy and a minimum of intelligence. There is a growing interest for cooperative and coordinated multi-vehicle systems, real-time re-planning, robust autonomous navigation systems and robust autonomous control of vehicles. Unmanned vehicles with high levels of autonomy may be used for safe and efficient collection of environmental data, for assimilation of climate and environmental models and to complement global satellite systems. The target audience primarily comprises research experts in the field of control theory, but the book may also be beneficial for graduate students.

Advances in Intelligent Vehicles presents recent advances in intelligent vehicle technologies that enhance the safety, reliability, and performance of vehicles and vehicular networks and systems. This book provides readers with up-to-date research results and cutting-edge technologies in the area of intelligent vehicles and transportation systems. Topics covered include virtual and staged testing scenarios, collision avoidance, human factors, and modeling techniques. The Series in Intelligent Systems publishes titles that cover state-of-the-art knowledge and the latest advances in research and development in intelligent systems. Its scope includes theoretical studies, design methods, and real-world implementations and applications. Provides researchers and engineers with up-to-date research results and state-of-the art technologies in the area of intelligent vehicles and transportation systems Covers hot topics, including driver assistance systems; cooperative vehicle-highway systems; collision avoidance; pedestrian protection; image, radar and lidar signal processing; and V2V and V2I communications

This important text/reference presents state-of-the-art research on intelligent vehicles, covering not only topics of object/obstacle detection and recognition, but also aspects of vehicle motion control. With an emphasis on both high-level concepts, and practical detail, the text links theory, algorithms, and issues of hardware and software implementation in intelligent vehicle research. Topics and features: presents a thorough introduction to the development and latest progress in intelligent vehicle research, and proposes a basic framework; provides detection and tracking algorithms for structured and unstructured roads, as well as on-road vehicle detection and tracking algorithms using boosted Gabor features; discusses an approach for multiple sensor-based multiple-object tracking, in addition to an integrated DGPS/IMU positioning approach; examines a vehicle navigation approach using global views; introduces algorithms for lateral and longitudinal vehicle motion control.

This book provides the latest information in intelligent vehicle control and intelligent transportation. Detailed discussions of vehicle dynamics and ground-vehicle interactions are provided for the modeling, simulation and control of vehicles. It includes an extensive review of past and current research achievements in the intelligent vehicle motion control and sensory field, and the book provides a careful assessment of future developments.

This book captures multidisciplinary research encompassing various facets of autonomous vehicle systems (AVS) research and developments. The AVS field is rapidly moving towards realization with numerous advances continually reported. The contributions to this field come from widely varying branches of knowledge, making it a truly multidisciplinary area of research and development. The topics covered in the book include: AI and deep learning for AVS Autonomous steering through deep neural networks Adversarial attacks and defenses on autonomous vehicles Gesture recognition for vehicle control Multi-sensor fusion in autonomous vehicles Teleoperation technologies for AVS Simulation and game theoretic decision making for AVS Path following control system design for AVS Hybrid cloud and edge solutions for AVS Ethics of AVS

This book presents the results of the successful Sensors Special Issue on Intelligent Vehicles that received submissions between March 2019 and May 2020. The Guest Editors of this Special Issue are Dr. David Fernández-Llorca, Dr. Ignacio Parra-Alonso, Dr. Iván García-Daza and Dr. Noelia Parra-Alonso, all from the Computer Engineering Department at the University of Alcalá (Madrid, Spain). A total of 32 manuscripts were finally accepted between 2019 and 2020, presented by top researchers from all over the world. The reader will find a well-representative set of current research and developments related to sensors and sensing for intelligent vehicles. The topics of the published manuscripts can be grouped into seven main categories: (1) assistance systems and automatic vehicle operation, (2) vehicle positioning and localization, (3) fault diagnosis and fail-x systems, (4) perception and scene understanding, (5) smart regenerative braking systems for electric vehicles, (6) driver behavior modeling and (7) intelligent sensing. We, the Guest Editors, hope that the readers will find this book to contain interesting papers for their research, papers that they will enjoy reading as much as we have enjoyed organizing this Special Issue

The intelligent vehicle will play a crucial and essential role in the development of the future intelligent transportation system, which is developing toward the connected driving environment, ultimate driving safety, and comforts, as well as green efficiency. While the decision making, planning, and control are extremely vital components of the intelligent vehicle, these modules act as a bridge, connecting the subsystem of the environmental perception and the bottom-level control execution of the vehicle as well. This short book covers various strategies of designing the decision making, trajectory planning, and tracking control, as well as share driving, of the human-automation to adapt to different levels of the automated driving system. More specifically, we introduce an end-to-end decision-making module based on the deep Q-learning, and improved path-planning methods based on artificial potentials and elastic bands which are designed for obstacle avoidance. Then, the optimal method based on the convex optimization and the natural cubic spline is presented. As for the speed planning, planning methods based on the multi-object optimization and high-order polynomials, and a method with convex optimization and natural cubic splines, are proposed for the non-vehicle-following scenario (e.g., free driving, lane change, obstacle avoidance), while the planning method based on vehicle-following kinematics and the model predictive control (MPC) is adopted for the car-following scenario. We introduce two robust tracking methods for the trajectory following. The first one, based on nonlinear vehicle longitudinal or path-preview dynamic systems, utilizes the adaptive sliding mode control (SMC) law which can compensate for uncertainties to follow the speed or path profiles. The second one is based on the five-degrees-of-freedom nonlinear vehicle dynamical system that utilizes the linearized time-varying MPC to track the speed and path profile simultaneously. Toward human-automation cooperative driving systems, we introduce two control strategies to address the control authority and conflict management problems between the human driver and the automated driving systems. Driving safety field and game theory are utilized to propose a game-based strategy, which is used to deal with path conflicts during obstacle avoidance. Driver's driving intention, situation assessment, and performance index are employed for the development of the fuzzy-based strategy. Multiple case studies and demos are included in each chapter to show the effectiveness of the proposed approach. We sincerely hope the contents of this short book provide certain theoretical guidance and technical supports for the development of intelligent vehicle technology.

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