Download or read book Predictive Energy Optimization in Connected and Automated Vehicles Using Approximate Dynamic Programming written by Shreshta Rajakumar Deshpande and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Global CO2 emissions regulations, in conjunction with increasing customer demands are requiring significant improvements in vehicle energy (or fuel) efficiency. In this drive to reduce fuel consumption, improvements in the powertrain (or propulsion system) continue to be a major area of focus, particularly shifting to higher levels of electrification. A next step in the evolution of improving fuel efficiency is to have the propulsion system controller make use of vehicle-level information. In this context, Connected and Automated Vehicle (CAV) technologies offer the potential for enhancing the vehicle fuel efficiency as well as improving vehicle safety and comfort by leveraging information from advanced mapping and location, and Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication. The focus of this thesis is to develop Dynamic Programming (DP) and Approximate Dynamic Programming (ADP) based approaches that combine the energy-saving potentials of powertrain electrification and CAV technologies, and further compound them. In this work, an ADP-based scheme is used to jointly optimize the vehicle velocity and energy management strategy of an electrified CAV over real-world driving routes. This predictive controls framework uses preview information from the route and environment to achieve significant fuel efficiency improvements even in the presence of variabilities (such as driver aggressiveness and varying traffic signal information). The controller was then implemented and tested in a demonstration vehicle at a proving ground facility over reconstructed route scenarios. Further, this thesis explores approaches to reducing the computational complexity of optimization methods based on Dynamic Programming, which can restrict its use in many real-time applications. To this end, two sub-optimal methodologies are proposed. One of them, the integrated DP-ECMS (Dynamic Programming-Equivalent Consumption Minimization Strategy) method embeds a heuristic strategy within the DP framework. In doing so, the resulting implementation is only marginally sub-optimal compared to the (original) DP, while mitigating the curse of dimensionality. The second method proposed to reduce computation time is the WASP (Warm Start Dynamic Programming) algorithm. Specifically, the solution to a perturbed receding horizon optimal control problem was computed in an approximately optimal manner, by making use of the value function and other properties of the original (unperturbed) DP solution. Its efficacy is demonstrated through application in simplified dynamic optimization problems.

Download or read book Route optimized Energy Management of Connected and Automated Multi mode Plug in Hybrid Electric Vehicle Using Reduced order Powertrain Modeling and Dynamic Programming written by and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : This thesis details the development of a methodology to blend charge-depleting (CD) and charge-sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV) to minimize energy consumption when the planned drive route cannot be completely executed in all-electric mode. This methodology enables efficient utilization of onboard energy resources by using increased awareness of driving conditions facilitated by Vehicle-to-Vehicle (V2V), Vehicle-to-Infrastructure (V2I), Vehicle-to-Everything (V2X) connectivity, and onboard perception technologies of Connected and Automated Vehicles (CAVs). With such application demanding a real-time update of optimal mode scheme to dynamic traffic conditions, the emphasis of this study is to develop a quick and computationally inexpensive blended mode optimizer by reduced-order modeling of Chevrolet Volt. On-road validation of the developed optimizer on a fleet of 4 instrumented vehicles revealed energy savings in the range of 2 to 12% and an initial optimization time less than 7 seconds for a 24-mile drive cycle.

Download or read book Energy Efficient Driving of Road Vehicles written by Antonio Sciarretta and published by Springer. This book was released on 2019-08-01 with total page 294 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book elaborates the science and engineering basis for energy-efficient driving in conventional and autonomous cars. After covering the physics of energy-efficient motion in conventional, hybrid, and electric powertrains, the book chiefly focuses on the energy-saving potential of connected and automated vehicles. It reveals how being connected to other vehicles and the infrastructure enables the anticipation of upcoming driving-relevant factors, e.g. hills, curves, slow traffic, state of traffic signals, and movements of nearby vehicles. In turn, automation allows vehicles to adjust their motion more precisely in anticipation of upcoming events, and to save energy. Lastly, the energy-efficient motion of connected and automated vehicles could have a harmonizing effect on mixed traffic, leading to additional energy savings for neighboring vehicles. Building on classical methods of powertrain modeling, optimization, and optimal control, the book further develops the theory of energy-efficient driving. In addition, it presents numerous theoretical and applied case studies that highlight the real-world implications of the theory developed. The book is chiefly intended for undergraduate and graduate engineering students and industry practitioners with a background in mechanical, electrical, or automotive engineering, computer science or robotics.

Download or read book Look ahead Optimization of a Connected and Automated 48V Mild hybrid Electric Vehicle written by Shobhit Gupta and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Increasing cost of fuel and global regulatory targets are driving the automotive industry towards fuel efficient vehicles. Hybrid electric vehicles (HEVs) can significantly improve the fuel economy by the application of an efficient control strategy. Additionally, the look-ahead information available from advanced driver assistance systems and cloud applications in a connected and automated vehicle can make the powertrain more predictive in nature. This would enable the implementation of a global optimization algorithm such as Dynamic Programming (DP). In this thesis, DP is implemented to co-optimize the vehicle velocity and energy management of a 48V mild-HEV over real world driving scenarios. Velocity optimization is performed by considering the look-ahead route characteristics such as the speed limit constraints along with the position of traffic lights and stop signs. To enable close to real-time implementation of DP, efforts have been put to alleviate the well-known "Curse of Dimensionality." A variable step size strategy is adopted instead of a constant step size. Furthermore, this thesis aims at building the Rollout Algorithm using Approximate Dynamic Programming for the 48V optimal control problem. This algorithm yields a look-ahead suboptimal control policy and under certain conditions, the sub-optimality can be minimized which is shown in this thesis. To compare the benefits obtained from the rollout, an experimentally validated driver model is developed which serves as the baseline for this project.

Download or read book Neuroevolution and Machine Learning Research Applied to Connected Automated Vehicle and Powertrain Control written by and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : This dissertation focuses on advancing Predictive Energy Management (PrEM) functions applied to modern connected and automated vehicles (CAV) cohorts. PrEM aims to utilize connectivity and ADAS functions to adaptively minimize vehicle energy consumption in a wide array of operations, extending the original control designed around a reduced set of test cycle procedures to adapt to real-world stochastic operating conditions. This research document is built upon three journal publications covering two PrEM schemes; the global cohort and local vehicle optimization paths. Both optimal control solutions are generated using various Neuroevolution centric processes. Chapter 1 discusses the methods and reasoning behind the need to increase the development speed of readily implementable optimal control functions for both complex and system-of-systems (SoS) applications. Neuroevolution allows for fast development time, optimal design space exploration, high-fidelity modeling usage, and seamless integration with data science processes. It additionally enables real-time implementation without modification and requires a low compute footprint. This provides a new paradigm for future automotive product development where conventional adaptive and optimal techniques deployment is still lagging due to their complexity and shortcomings. At the global level, vehicle energy consumption is minimized by optimally controlling vehicle speed in diverse environments. Chapters 2 and 3 relate to connected traffic lights and uncontrolled intersection operations respectively. In the first study, the CAV cohort optimizes its velocity based on connected traffic light information. Thanks to the Traffic Technology Services (TTS) network, this information is shared via cellular communication. Energy consumption reduction of up to 22\% is reported using simulation and during closed-loop track testing. In the second study, no such timing information exists, and the cohorts must collaborate to enable safe operation at uncontrolled intersections. Here, the cohorts share states' information to minimize deceleration and acceleration events for comfort and energy savings, primarily focusing on safety. Simulation demonstrates that effective collaboration can be achieved with cohorts' lengths of up to 100 meters in congested environments. At the local PrEM level, additional energy savings can be achieved for each specific cohort's vehicle based on its powertrain architecture. One of the more complex and relevant architectures to apply localized PrEM to are hybrid electric vehicles (HEV), where two sources of energy can be blended optimally based on a vehicle's predicted speed profile, which is directly controlled by the global PrEM optimization function. In Chapter 4, Neuroevolution and vehicle speed profile classification is applied to a P3 HEV in demonstrating significant additional energy consumption improvements.

Download or read book Comparison of Opitimal Energy Management Strategies Using Dynamic Programming Model Predictive Control and Constant Velocity Pattern written by Amol Arvind Patil and published by . This book was released on 2020 with total page 67 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to the recent advancements in autonomous vehicle technology, future vehicle velocity predictions are becoming more robust which allows fuel economy (FE) improvements in hybrid electric vehicles through optimal energy management strategies (EMS). A real-world highway drive cycle (DC) and a controls-oriented 2017 Toyota Prius Prime model are used to study potential FE improvements. We proposed three important metrics for comparison: (1) perfect full drive cycle prediction using dynamic programming, (2) 10-second prediction horizon model predictive control (MPC), and (3) 10-second constant velocity prediction. These different velocity predictions are put into an optimal EMS derivation algorithm to derive optimal engine torque and engine speed. The results show that the constant velocity prediction algorithm outperformed the baseline control strategy but underperformed the MPC strategy with an average 1.58% and 2.45% of FE improvement with highway and city-highway DC. Also, using a 10-second prediction window MPC strategy provided FE improvement results close to the full drive cycle prediction case. MPC has the potential to achieve 60%-65% and 70% - 80% of global FE improvement over highway and city-highway DC respectively

Download or read book Reinforcement Learning in Eco driving for Connected and Automated Vehicles written by Zhaoxuan Zhu and published by . This book was released on 2021 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Connected and Automated Vehicles (CAVs) can significantly improve transportation efficiency by taking advantage of advanced connectivity technologies. Meanwhile, the combination of CAVs and powertrain electrification, such as Hybrid Electric Vehicles (HEVs) and Plug-in Hybrid Electric Vehicles (PHEVs), offers greater potential to improve fuel economy due to the extra control flexibility compared to vehicles with a single power source. In this context, the eco-driving control optimization problem seeks to design the optimal speed and powertrain components usage profiles based upon the information received by advanced mapping or Vehicle-to-Everything (V2X) communications to minimize the energy consumed by the vehicle over a given itinerary. To overcome the real-time computational complexity and embrace the stochastic nature of the driving task, the application and extension of state-of-the-art (SOTA) Deep Reinforcement Learning (Deep RL, DRL) algorithms to the eco-driving problem for a mild-HEV is studied in this dissertation. For better training and a more comprehensive evaluation, an RL environment, consisting of a mild HEV powertrain and vehicle dynamics model and a large-scale microscopic traffic simulator, is developed. To benchmark the performance of the developed strategies, two causal controllers, namely a baseline strategy representing human drivers and a deterministic optimal-control-based strategy, and the non-causal wait-and-see solution are implemented. In the first RL application, the eco-driving problem is formulated as a Partially Observable Markov Decision Process, and a SOTA model-free DRL (MFDRL) algorithm, Proximal Policy Optimization with Long Short-term Memory as function approximator, is used. Evaluated over 100 trips randomly generated in the city of Columbus, OH, the MFDRL agent shows a 17% fuel economy improvement against the baseline strategy while keeping the average travel time comparable. While showing performance comparable to the optimal-control-based strategy, the actor of the MFDRL agent offers an explicit control policy that significantly reduces the onboard computation. Subsequently, a model-based DRL (MBDRL) algorithm, Safe Model-based Off-policy Reinforcement Learning (SMORL) is proposed. The algorithm addresses the following issues emerged from the MFDRL development: a) the cumbersome process necessary to design the rewarding mechanism, b) the lack of the constraint satisfaction and feasibility guarantee and c) the low sample efficiency. Specifically, SMORL consists of three key components, a massively parallelizable dynamic programming trajectory optimizer, a value function learned in an off-policy fashion and a learned safe set as a generative model. Evaluated under the same conditions, the SMORL agent shows a 21% reduction on the fuel consumption over the baseline and the dominant performance over the MFDRL agent and the deterministic optimal-control-based controller.

Download or read book REAL TIME PREDICTIVE CONTROL OF CONNECTED VEHICLE POWERTRAINS FOR IMPROVED ENERGY EFFICIENCY written by and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Abstract : The continued push for the reduction of energy consumption across the automotive vehicle fleet has led to widespread adoption of hybrid and plug-in hybrid electric vehicles (PHEV) by auto manufacturers. In addition, connected and automated vehicle (CAV) technologies have seen rapid development in recent years and bring with them the potential to significantly impact vehicle energy consumption. This dissertation studies predictive control methods for PHEV powertrains that are enabled by CAV technologies with the goal of reducing vehicle energy consumption. First, a real-time predictive powertrain controller for PHEV energy management is developed. This controller utilizes predictions of future vehicle velocity and power demand in order to optimize powersplit decisions of the vehicle. This predictive powertrain controller utilizes nonlinear model predictive control (NMPC) to perform this optimization while being cognizant of future vehicle behavior. Second, the developed NMPC powertrain controller is thoroughly evaluated both in simulation and real-time testing. The controller is assessed over a large number of standardized and real-world drive cycles in simulation in order to properly quantify the energy savings benefits of the controller. In addition, the NMPC powertrain controller is deployed onto a real-time rapid prototyping embedded controller installed in a test vehicle. Using this real-time testing setup, the developed NMPC powertrain controller is evaluated using on-road testing for both energy savings performance and real-time performance. Third, a real-time integrated predictive powertrain controller (IPPC) for a multi-mode PHEV is presented. Utilizing predictions of future vehicle behavior, an optimal mode path plan is computed in order to determine a mode command best suited to the future conditions. In addition, this optimal mode path planning controller is integrated with the NMPC powertrain controller to create a real-time integrated predictive powertrain controller that is capable of full supervisory control for a multi-mode PHEV. Fourth, the IPPC is evaluated in simulation testing across a range of standard and real-world drive cycles in order to quantify the energy savings of the controller. This analysis is comprised of the combined benefit of the NMPC powertrain controller and the optimal mode path planning controller. The IPPC is deployed onto a rapid prototyping embedded controller for real-time evaluation. Using the real-time implementation of the IPPC, on-road testing was performed to assess both energy benefits and real-time performance of the IPPC. Finally, as the controllers developed in this research were evaluated for a single vehicle platform, the applicability of these controllers to other platforms is discussed. Multiple cases are discussed on how both the NMPC powertrain controller and the optimal mode path planning controller can be applied to other vehicle platforms in order to broaden the scope of this research.

Download or read book Approximate Dynamic Programming for Dynamic Vehicle Routing written by Marlin Wolf Ulmer and published by Springer. This book was released on 2017-04-19 with total page 209 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book provides a straightforward overview for every researcher interested in stochastic dynamic vehicle routing problems (SDVRPs). The book is written for both the applied researcher looking for suitable solution approaches for particular problems as well as for the theoretical researcher looking for effective and efficient methods of stochastic dynamic optimization and approximate dynamic programming (ADP). To this end, the book contains two parts. In the first part, the general methodology required for modeling and approaching SDVRPs is presented. It presents adapted and new, general anticipatory methods of ADP tailored to the needs of dynamic vehicle routing. Since stochastic dynamic optimization is often complex and may not always be intuitive on first glance, the author accompanies the ADP-methodology with illustrative examples from the field of SDVRPs. The second part of this book then depicts the application of the theory to a specific SDVRP. The process starts from the real-world application. The author describes a SDVRP with stochastic customer requests often addressed in the literature, and then shows in detail how this problem can be modeled as a Markov decision process and presents several anticipatory solution approaches based on ADP. In an extensive computational study, he shows the advantages of the presented approaches compared to conventional heuristics. To allow deep insights in the functionality of ADP, he presents a comprehensive analysis of the ADP approaches.

Download or read book Vehicle Power Management written by Xi Zhang and published by Springer Science & Business Media. This book was released on 2011-08-12 with total page 353 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vehicle Power Management addresses the challenge of improving vehicle fuel economy and reducing emissions without sacrificing vehicle performance, reliability and durability. It opens with the definition, objectives, and current research issues of vehicle power management, before moving on to a detailed introduction to the modeling of vehicle devices and components involved in the vehicle power management system, which has been proven to be the most cost-effective and efficient method for initial-phase vehicle research and design. Specific vehicle power management algorithms and strategies, including the analytical approach, optimal control, intelligent system approaches and wavelet technology, are derived and analyzed for realistic applications. Vehicle Power Management also gives a detailed description of several key technologies in the design phases of hybrid electric vehicles containing battery management systems, component optimization, hardware-in-the-loop and software-in-the-loop. Vehicle Power Management provides graduate and upper level undergraduate students, engineers, and researchers in both academia and the automotive industry, with a clear understanding of the concepts, methodologies, and prospects of vehicle power management.

Download or read book Approximate Dynamic Programming written by Warren B. Powell and published by John Wiley & Sons. This book was released on 2007-10-05 with total page 487 pages. Available in PDF, EPUB and Kindle. Book excerpt: A complete and accessible introduction to the real-world applications of approximate dynamic programming With the growing levels of sophistication in modern-day operations, it is vital for practitioners to understand how to approach, model, and solve complex industrial problems. Approximate Dynamic Programming is a result of the author's decades of experience working in large industrial settings to develop practical and high-quality solutions to problems that involve making decisions in the presence of uncertainty. This groundbreaking book uniquely integrates four distinct disciplines—Markov design processes, mathematical programming, simulation, and statistics—to demonstrate how to successfully model and solve a wide range of real-life problems using the techniques of approximate dynamic programming (ADP). The reader is introduced to the three curses of dimensionality that impact complex problems and is also shown how the post-decision state variable allows for the use of classical algorithmic strategies from operations research to treat complex stochastic optimization problems. Designed as an introduction and assuming no prior training in dynamic programming of any form, Approximate Dynamic Programming contains dozens of algorithms that are intended to serve as a starting point in the design of practical solutions for real problems. The book provides detailed coverage of implementation challenges including: modeling complex sequential decision processes under uncertainty, identifying robust policies, designing and estimating value function approximations, choosing effective stepsize rules, and resolving convergence issues. With a focus on modeling and algorithms in conjunction with the language of mainstream operations research, artificial intelligence, and control theory, Approximate Dynamic Programming: Models complex, high-dimensional problems in a natural and practical way, which draws on years of industrial projects Introduces and emphasizes the power of estimating a value function around the post-decision state, allowing solution algorithms to be broken down into three fundamental steps: classical simulation, classical optimization, and classical statistics Presents a thorough discussion of recursive estimation, including fundamental theory and a number of issues that arise in the development of practical algorithms Offers a variety of methods for approximating dynamic programs that have appeared in previous literature, but that have never been presented in the coherent format of a book Motivated by examples from modern-day operations research, Approximate Dynamic Programming is an accessible introduction to dynamic modeling and is also a valuable guide for the development of high-quality solutions to problems that exist in operations research and engineering. The clear and precise presentation of the material makes this an appropriate text for advanced undergraduate and beginning graduate courses, while also serving as a reference for researchers and practitioners. A companion Web site is available for readers, which includes additional exercises, solutions to exercises, and data sets to reinforce the book's main concepts.

Download or read book Electric Systems for Transportation written by Maria Carmen Falvo and published by MDPI. This book was released on 2021-09-02 with total page 690 pages. Available in PDF, EPUB and Kindle. Book excerpt: Transportation systems play a major role in the reduction of energy consumptions and environmental impact all over the world. The significant amount of energy of transport systems forces the adoption of new solutions to ensure their performance with energy-saving and reduced environmental impact. In this context, technologies and materials, devices and systems, design methods, and management techniques, related to the electrical power systems for transportation are continuously improving thanks to research activities. The main common challenge in all the applications concerns the adoption of innovative solutions that can improve existing transportation systems in terms of efficiency and sustainability.

Download or read book Energy Management in Hybrid Electric Vehicles written by Siba Prasada Panigrahi and published by Butterworth-Heinemann. This book was released on 2016-09-01 with total page 544 pages. Available in PDF, EPUB and Kindle. Book excerpt: Energy Management in Hybrid Electric Vehicles provides the basics of energy management, powertrain configuration, and optimization in hybrid electric vehicles (HEVs), beginning with an introduction to industry challenges and the state-of-the-art in electric, hybrid, and fuel cell vehicles. It then considers, in detail, critical topics such as HEV architecture, battery technology, and regenerative braking, also providing guidance on different control and simulation models alongside the latest advances in rule-based and optimization-based approaches to energy management. Users will find a rare, practical overview of the knowledge needed to work in this fast-moving area. Provides an overview of the theory and practical examples needed for engineers to confidently analyze hybrid configurations and control strategies Ideal reference for those interested in energy management, hybrid electric vehicles, powertrain configuration, fuel cell vehicles, HEV architecture, battery technology, and regenerative braking Brings together, in a single resource, cutting-edge knowledge from the different fields involved in the development of hybrid electric vehicle technology Offers guidance on different control, simulation, and optimization approaches, enabling the selection of appropriate energy management solutions for particular applications

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 Hybrid Electric Vehicles written by Simona Onori and published by Springer. This book was released on 2015-12-16 with total page 121 pages. Available in PDF, EPUB and Kindle. Book excerpt: This SpringerBrief deals with the control and optimization problem in hybrid electric vehicles. Given that there are two (or more) energy sources (i.e., battery and fuel) in hybrid vehicles, it shows the reader how to implement an energy-management strategy that decides how much of the vehicle’s power is provided by each source instant by instant. Hybrid Electric Vehicles: •introduces methods for modeling energy flow in hybrid electric vehicles; •presents a standard mathematical formulation of the optimal control problem; •discusses different optimization and control strategies for energy management, integrating the most recent research results; and •carries out an overall comparison of the different control strategies presented. Chapter by chapter, a case study is thoroughly developed, providing illustrative numerical examples that show the basic principles applied to real-world situations. The brief is intended as a straightforward tool for learning quickly about state-of-the-art energy-management strategies. It is particularly well-suited to the needs of graduate students and engineers already familiar with the basics of hybrid vehicles but who wish to learn more about their control strategies.

Download or read book Autonomous Vehicles and Future Mobility written by Pierluigi Coppola and published by Elsevier. This book was released on 2019-06-15 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous Vehicles and Future Mobility presents novel methods for examining the long term effects on individuals, society, and on the environment on a wide range of forthcoming transport scenarios such self-driving vehicles, workplace mobility plans, demand responsive transport analysis, mobility as a service, multi-source transport data provision, and door-to-door mobility. With the development and realization of new mobility options comes change in long term travel behavior and transport policy. Autonomous Vehicles and Future Mobility addresses these impacts, considering such key areas as attitude of users towards new services, the consequences of introducing of new mobility forms, the impacts of changing work related trips, the access to information about mobility options and the changing strategies of relevant stakeholders in transportation. By examining and contextualizing innovative transport solutions in this rapidly evolving field, Autonomous Vehicles and Future Mobility provides insights into current implementation of these potentially sustainable solutions, serving as general guidelines and best practices for researchers, professionals, and policy makers. Covers hot topics including travel behavior change, autonomous vehicle impacts, intelligent solutions, mobility planning, mobility as a service, sustainable solutions, and more Examines up to date models and applications using novel technologies Contributions from leading scholars around the globe Case studies with latest research results

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.