Download or read book Deep Long Short term Memory Network Embedded Connected Automated Car following Model Predictive Control Strategy written by Zhen Zhang and published by . This book was released on 2019 with total page 111 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent years, autonomous vehicle (AV) technology, which is expected to solve critical issues, such as traffic efficiency, capacity, and safety, has been put a lot of efforts and making considerable progress. It utilizes data from various sensors for sensing, prediction, and control tasks. Another related technology that also has significant impacts on transportation is connected vehicle (CV). With the assistance of dedicated short-range communication devices, CV communicates with other vehicles in the system or roadside infrastructure to get valuable information about surroundings. Combining these technologies together, connected and automated vehicle (CAV) can further enhance the AV benefits in various ways, such as safety and efficiency. Towards to fully automation, one of most important areas is the advanced driver-assistance systems, especially the longitudinal control. Since the manual vehicles will still dominate the road for a long time, how to perform the longitudinal control for a CAV is a critical problem to be solved for mixed traffic consisting of CAVs and manual vehicles. Model Predictive Control (MPC) is a modern control framework that has been extensively studied across various fields. There is also plenty of research applying MPC to control the vehicle in full CAV environments. However, due to the lack of communication with the preceding manual vehicle, CAV is not able to attain the planning of the leading vehicle's control actions, which is critically needed by MPC controller. The emerging deep learning techniques have demonstrated promising capability in various domains, including traffic prediction. This research focuses on developing a novel car-following control strategy for a platoon of CAVs and manual vehicles. Specifically, it controls those CAVs following another manual vehicle in this platoon and enhance the stability. The proposed longitudinal control strategy is designed in MPC manner, embedded with deep-learning enhanced prediction. This dissertation first conducts a comprehensive review on car-following models and MPC theories and applications on vehicle control. Then a novel control strategy is developed to enhance the efficiency and stability of controlling CAVs in mixed traffic. There are two major parts in this strategy. One is trajectory prediction model, and the other is MPC controller. Two different deep long-short-term-memory (LSTM) based models are designed and evaluated for two potential control scenarios, taking advantages of new deep learning technology. Embedded with deep learning models, MPC controller is formulated with consideration of safety, efficiency, and driving comfort. Several experiments are carried out to analyze the performance of trajectory prediction models and proposed control strategy and results show promising potential.

Download or read book Predictive Control Strategy for Automated Driving Systems Under Mixed Traffic Lane Change Conditions written by Kunsong Shi and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the recent development of technologies, automated vehicles and connectedautomated vehicles (CAVs) have been researched and developed. However, mass deployment of fully automated vehicles is very difficult to achieve in the near future because of the high cost of high level autonomous vehicles. Automated driving system (ADS) like the Connected and automated vehicle highway (CAVH) system that can utilize roadside infrastructure is one of the best approaches for large scale deployment for CAVs because the system can reduce the workload and cost of a single vehicle. However, mass deployment of ADS will still take some time. Therefore, in the near future, mixed traffic conditions containing CAVs and human driven vehicles will be the predominant condition. Safe and efficient control for autonomous vehicles under mixed is still a very challenging task for the automated driving system. In this research, we present a predictive control strategy for automated driving systems under mixed traffic lane change conditions. To achieve this goal, we first proposed a deep learning based lane change prediction module that considers a new lane change prediction scenario that is more realistic by considering more surrounding vehicles. Then we developed a deep learning based integrated two dimensional vehicle trajectory prediction module. This integrated model can predict combined behaviors of car-following and lane change. Then we created a predictive deep reinforcement learning based CAV controller that can utilize the predicted information to generate safe and efficient longitudinal control for CAVs under mixed traffic lane change conditions. Several experiments are conducted using the trajectory data Next Generation Simulation (NGSIM) dataset to evaluate the effectiveness of the proposed modules. The experiment result shows that our lane change prediction module can accurately predict human lane change behavior under the defined lane change condition. Moreover, the experiment result demonstrates that the proposed integrated two dimensional trajectory prediction model can accurately predict both lane change trajectories and car-following trajectories. In addition, experiments for the deep reinforcement learning-based CAV controller showed that the proposed controller can improve traffic safety and efficiency of CAVs under mixed traffic lane change conditions.

Download or read book Mixed Platoon Control Strategy of Connected and Automated Vehicles Based on Physics informed Deep Reinforcement Learning written by Haotian Shi (Ph.D.) and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation presents a distributed platoon control strategy of connected and automated vehicles (CAVs) based on physics-informed Deep Reinforcement Learning (DRL) for mixed traffic of CAVs and human-driven vehicles (HDVs). The dissertation will mainly consist of three parts: (i) generic DRL-based CAV control framework for the mixed traffic flow; (ii) DRL-based CAV distributed control under communication failure for the fully connected automated environment; (iii) distributed CAVs control for the mixed traffic flow, under real-time aggregated macroscopic car-following behavior estimation based on DRL. For the first part, we first discussed the current challenges for CAV control in mixed traffic flow. For distributed CAV control, we categorize the local downstream environment into two broad traffic scenarios based on the composition of CAVs and HDVs to accommodate any possible CAV-HDV platoon configuration: (i) a fully connected automated environment, where all local downstream vehicles are CAVs, forming a CAV-CAVs topology; and (ii) a mixed local downstream environment, comprising the closest downstream CAV followed by one or more HDVs, creating a CAV-HDVs-CAV topology. This generic control framework effectively accommodates any CAV-HDV platoon topology that may emerge within the mixed traffic platoon. This part is discussed in Section 3. For the second part, this study proposes a deep reinforcement learning (DRL) based distributed longitudinal control strategy for connected and automated vehicles (CAVs) under communication failure to stabilize traffic oscillations. Specifically, the Signal-Interference-plus-Noise Ratio (SINR) based vehicle-to-vehicle (V2V) communication is incorporated into the DRL training environment to reproduce the realistic communication and time-space varying information flow topologies (IFTs). A dynamic information fusion mechanism is designed to smooth the high-jerk control signal caused by the dynamic IFTs. Based on that, each CAV controlled by the DRL-based agent was developed to receive the real-time downstream CAVs' state information and take longitudinal actions to achieve the equilibrium consensus in the multi-agent system. Simulated experiments are conducted to tune the communication adjustment mechanism and further validate the control performance, oscillation dampening performance and generalization capability of our proposed algorithm. This part is discussed in Section 4. The third part proposes an innovative distributed longitudinal control strategy for connected automated vehicles (CAVs) in the mixed traffic environment of CAV and human-driven vehicles (HDVs), incorporating high-dimensional platoon information. For mixed traffic, the traditional CAV control method focuses on microscopic trajectory information, which may not be efficient in handling the HDV stochasticity (e.g., long reaction time; various driving styles) and mixed traffic heterogeneities. Different from traditional methods, our method, for the first time, characterizes consecutive HDVs as a whole (i.e., AHDV) to reduce the HDV stochasticity and utilize its macroscopic features to control the following CAVs. The new control strategy takes advantage of platoon information to anticipate the disturbances and traffic features induced downstream under mixed traffic scenarios and greatly outperforms the traditional methods. In particular, the control algorithm is based on deep reinforcement learning (DRL) to fulfill car-following control efficiency and further address the stochasticity for the aggregated car following behavior by embedding it in the training environment. To better utilize the macroscopic traffic features, a general platoon of mixed traffic is categorized as a CAV-HDVs-CAV pattern and described by corresponding DRL states. The macroscopic traffic flow properties are built upon the Newell car-following model to capture the characteristics of aggregated HDVs' joint behaviors. Simulated experiments are conducted to validate our proposed strategy. The results demonstrate that the proposed control method has outstanding performances in terms of oscillation dampening, eco-driving, and generalization capability. This part is discussed in Section 5.

Download or read book Practical Design and Application of Model Predictive Control written by Nassim Khaled and published by Butterworth-Heinemann. This book was released on 2018-05-04 with total page 264 pages. Available in PDF, EPUB and Kindle. Book excerpt: Practical Design and Application of Model Predictive Control is a self-learning resource on how to design, tune and deploy an MPC using MATLAB® and Simulink®. This reference is one of the most detailed publications on how to design and tune MPC controllers. Examples presented range from double-Mass spring system, ship heading and speed control, robustness analysis through Monte-Carlo simulations, photovoltaic optimal control, and energy management of power-split and air-handling control. Readers will also learn how to embed the designed MPC controller in a real-time platform such as Arduino®. The selected problems are nonlinear and challenging, and thus serve as an excellent experimental, dynamic system to show the reader the capability of MPC. The step-by-step solutions of the problems are thoroughly documented to allow the reader to easily replicate the results. Furthermore, the MATLAB® and Simulink® codes for the solutions are available for free download. Readers can connect with the authors through the dedicated website which includes additional free resources at www.practicalmpc.com. Illustrates how to design, tune and deploy MPC for projects in a quick manner Demonstrates a variety of applications that are solved using MATLAB® and Simulink® Bridges the gap in providing a number of realistic problems with very hands-on training Provides MATLAB® and Simulink® code solutions. This includes nonlinear plant models that the reader can use for other projects and research work Presents application problems with solutions to help reinforce the information learned

Download or read book Nonlinear Model Predictive Control written by Frank Allgöwer and published by Birkhäuser. This book was released on 2012-12-06 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Traffic Flow Dynamics written by Martin Treiber and published by Springer Science & Business Media. This book was released on 2012-10-11 with total page 505 pages. Available in PDF, EPUB and Kindle. Book excerpt: This textbook provides a comprehensive and instructive coverage of vehicular traffic flow dynamics and modeling. It makes this fascinating interdisciplinary topic, which to date was only documented in parts by specialized monographs, accessible to a broad readership. Numerous figures and problems with solutions help the reader to quickly understand and practice the presented concepts. This book is targeted at students of physics and traffic engineering and, more generally, also at students and professionals in computer science, mathematics, and interdisciplinary topics. It also offers material for project work in programming and simulation at college and university level. The main part, after presenting different categories of traffic data, is devoted to a mathematical description of the dynamics of traffic flow, covering macroscopic models which describe traffic in terms of density, as well as microscopic many-particle models in which each particle corresponds to a vehicle and its driver. Focus chapters on traffic instabilities and model calibration/validation present these topics in a novel and systematic way. Finally, the theoretical framework is shown at work in selected applications such as traffic-state and travel-time estimation, intelligent transportation systems, traffic operations management, and a detailed physics-based model for fuel consumption and emissions.

Download or read book TinyML written by Pete Warden and published by O'Reilly Media. This book was released on 2019-12-16 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: Deep learning networks are getting smaller. Much smaller. The Google Assistant team can detect words with a model just 14 kilobytes in size—small enough to run on a microcontroller. With this practical book you’ll enter the field of TinyML, where deep learning and embedded systems combine to make astounding things possible with tiny devices. Pete Warden and Daniel Situnayake explain how you can train models small enough to fit into any environment. Ideal for software and hardware developers who want to build embedded systems using machine learning, this guide walks you through creating a series of TinyML projects, step-by-step. No machine learning or microcontroller experience is necessary. Build a speech recognizer, a camera that detects people, and a magic wand that responds to gestures Work with Arduino and ultra-low-power microcontrollers Learn the essentials of ML and how to train your own models Train models to understand audio, image, and accelerometer data Explore TensorFlow Lite for Microcontrollers, Google’s toolkit for TinyML Debug applications and provide safeguards for privacy and security Optimize latency, energy usage, and model and binary size

Download or read book Model Based Predictive Control written by J.A. Rossiter and published by CRC Press. This book was released on 2017-07-12 with total page 323 pages. Available in PDF, EPUB and Kindle. Book excerpt: Model Predictive Control (MPC) has become a widely used methodology across all engineering disciplines, yet there are few books which study this approach. Until now, no book has addressed in detail all key issues in the field including apriori stability and robust stability results. Engineers and MPC researchers now have a volume that provides a complete overview of the theory and practice of MPC as it relates to process and control engineering. Model-Based Predictive Control, A Practical Approach, analyzes predictive control from its base mathematical foundation, but delivers the subject matter in a readable, intuitive style. The author writes in layman's terms, avoiding jargon and using a style that relies upon personal insight into practical applications. This detailed introduction to predictive control introduces basic MPC concepts and demonstrates how they are applied in the design and control of systems, experiments, and industrial processes. The text outlines how to model, provide robustness, handle constraints, ensure feasibility, and guarantee stability. It also details options in regard to algorithms, models, and complexity vs. performance issues.

Download or read book Creating Autonomous Vehicle Systems written by Shaoshan Liu and published by Morgan & Claypool Publishers. This book was released on 2017-10-25 with total page 285 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the first technical overview of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences of creating autonomous vehicle systems. These systems are complex, consisting of three major subsystems: (1) algorithms for localization, perception, and planning and control; (2) client systems, such as the robotics operating system and hardware platform; and (3) the cloud platform, which includes data storage, simulation, high-definition (HD) mapping, and deep learning model training. The algorithm subsystem extracts meaningful information from sensor raw data to understand its environment and make decisions about its actions. The client subsystem integrates these algorithms to meet real-time and reliability requirements. The cloud platform provides offline computing and storage capabilities for autonomous vehicles. Using the cloud platform, we are able to test new algorithms and update the HD map—plus, train better recognition, tracking, and decision models. This book consists of nine chapters. Chapter 1 provides an overview of autonomous vehicle systems; Chapter 2 focuses on localization technologies; Chapter 3 discusses traditional techniques used for perception; Chapter 4 discusses deep learning based techniques for perception; Chapter 5 introduces the planning and control sub-system, especially prediction and routing technologies; Chapter 6 focuses on motion planning and feedback control of the planning and control subsystem; Chapter 7 introduces reinforcement learning-based planning and control; Chapter 8 delves into the details of client systems design; and Chapter 9 provides the details of cloud platforms for autonomous driving. This book should be useful to students, researchers, and practitioners alike. Whether you are an undergraduate or a graduate student interested in autonomous driving, you will find herein a comprehensive overview of the whole autonomous vehicle technology stack. If you are an autonomous driving practitioner, the many practical techniques introduced in this book will be of interest to you. Researchers will also find plenty of references for an effective, deeper exploration of the various technologies.

Download or read book Theory of Ground Vehicles written by J. Y. Wong and published by John Wiley & Sons. This book was released on 2001-03-20 with total page 586 pages. Available in PDF, EPUB and Kindle. Book excerpt: An updated edition of the classic reference on the dynamics of road and off-road vehicles As we enter a new millennium, the vehicle industry faces greater challenges than ever before as it strives to meet the increasing demand for safer, environmentally friendlier, more energy efficient, and lower emissions products. Theory of Ground Vehicles, Third Edition gives aspiring and practicing engineers a fundamental understanding of the critical factors affecting the performance, handling, and ride essential to the development and design of ground vehicles that meet these requirements. As in previous editions, this book focuses on applying engineering principles to the analysis of vehicle behavior. A large number of practical examples and problems are included throughout to help readers bridge the gap between theory and practice. Covering a wide range of topics concerning the dynamics of road and off-road vehicles, this Third Edition is filled with up-to-date information, including: * The Magic Formula for characterizing pneumatic tire behavior from test data for vehicle handling simulations * Computer-aided methods for performance and design evaluation of off-road vehicles, based on the author's own research * Updated data on road vehicle transmissions and operating fuel economy * Fundamentals of road vehicle stability control * Optimization of the performance of four-wheel-drive off-road vehicles and experimental substantiation, based on the author's own investigations * A new theory on skid-steering of tracked vehicles, developed by the author.

Download or read book Road Vehicle Automation 3 written by Gereon Meyer and published by Springer. This book was released on 2016-07-01 with total page 292 pages. Available in PDF, EPUB and Kindle. Book excerpt: This edited book comprises papers about the impacts, benefits and challenges of connected and automated cars. It is the third volume of the LNMOB series dealing with Road Vehicle Automation. The book comprises contributions from researchers, industry practitioners and policy makers, covering perspectives from the U.S., Europe and Japan. It is based on the Automated Vehicles Symposium 2015 which was jointly organized by the Association of Unmanned Vehicle Systems International (AUVSI) and the Transportation Research Board (TRB) in Ann Arbor, Michigan, in July 2015. The topical spectrum includes, but is not limited to, public sector activities, human factors, ethical and business aspects, energy and technological perspectives, vehicle systems and transportation infrastructure. This book is an indispensable source of information for academic researchers, industrial engineers and policy makers interested in the topic of road vehicle automation.

Download or read book Artificial Intelligence in Healthcare written by Adam Bohr and published by Academic Press. This book was released on 2020-06-21 with total page 385 pages. Available in PDF, EPUB and Kindle. Book excerpt: Artificial Intelligence (AI) in Healthcare is more than a comprehensive introduction to artificial intelligence as a tool in the generation and analysis of healthcare data. The book is split into two sections where the first section describes the current healthcare challenges and the rise of AI in this arena. The ten following chapters are written by specialists in each area, covering the whole healthcare ecosystem. First, the AI applications in drug design and drug development are presented followed by its applications in the field of cancer diagnostics, treatment and medical imaging. Subsequently, the application of AI in medical devices and surgery are covered as well as remote patient monitoring. Finally, the book dives into the topics of security, privacy, information sharing, health insurances and legal aspects of AI in healthcare. Highlights different data techniques in healthcare data analysis, including machine learning and data mining Illustrates different applications and challenges across the design, implementation and management of intelligent systems and healthcare data networks Includes applications and case studies across all areas of AI in healthcare data

Download or read book Handbook of Model Predictive Control written by Saša V. Raković and published by Springer. This book was released on 2018-09-01 with total page 693 pages. Available in PDF, EPUB and Kindle. Book excerpt: Recent developments in model-predictive control promise remarkable opportunities for designing multi-input, multi-output control systems and improving the control of single-input, single-output systems. This volume provides a definitive survey of the latest model-predictive control methods available to engineers and scientists today. The initial set of chapters present various methods for managing uncertainty in systems, including stochastic model-predictive control. With the advent of affordable and fast computation, control engineers now need to think about using “computationally intensive controls,” so the second part of this book addresses the solution of optimization problems in “real” time for model-predictive control. The theory and applications of control theory often influence each other, so the last section of Handbook of Model Predictive Control rounds out the book with representative applications to automobiles, healthcare, robotics, and finance. The chapters in this volume will be useful to working engineers, scientists, and mathematicians, as well as students and faculty interested in the progression of control theory. Future developments in MPC will no doubt build from concepts demonstrated in this book and anyone with an interest in MPC will find fruitful information and suggestions for additional reading.

Download or read book Automated Machine Learning written by Frank Hutter and published by Springer. This book was released on 2019-05-17 with total page 223 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.

Download or read book Vehicle Propulsion Systems written by Lino Guzzella and published by Springer Science & Business Media. This book was released on 2007-09-21 with total page 345 pages. Available in PDF, EPUB and Kindle. Book excerpt: The authors of this text have written a comprehensive introduction to the modeling and optimization problems encountered when designing new propulsion systems for passenger cars. It is intended for persons interested in the analysis and optimization of vehicle propulsion systems. Its focus is on the control-oriented mathematical description of the physical processes and on the model-based optimization of the system structure and of the supervisory control algorithms.

Download or read book Real Time Collision Detection written by Christer Ericson and published by CRC Press. This book was released on 2004-12-22 with total page 634 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written by an expert in the game industry, Christer Ericson's new book is a comprehensive guide to the components of efficient real-time collision detection systems. The book provides the tools and know-how needed to implement industrial-strength collision detection for the highly detailed dynamic environments of applications such as 3D games, virt

Download or read book Representation Learning for Natural Language Processing written by Zhiyuan Liu and published by Springer Nature. This book was released on 2020-07-03 with total page 319 pages. Available in PDF, EPUB and Kindle. Book excerpt: This open access book provides an overview of the recent advances in representation learning theory, algorithms and applications for natural language processing (NLP). It is divided into three parts. Part I presents the representation learning techniques for multiple language entries, including words, phrases, sentences and documents. Part II then introduces the representation techniques for those objects that are closely related to NLP, including entity-based world knowledge, sememe-based linguistic knowledge, networks, and cross-modal entries. Lastly, Part III provides open resource tools for representation learning techniques, and discusses the remaining challenges and future research directions. The theories and algorithms of representation learning presented can also benefit other related domains such as machine learning, social network analysis, semantic Web, information retrieval, data mining and computational biology. This book is intended for advanced undergraduate and graduate students, post-doctoral fellows, researchers, lecturers, and industrial engineers, as well as anyone interested in representation learning and natural language processing.