Download or read book Integrated Motion Planning and Control for Automated Vehicles Up to the Limits of Handling written by Vincent Andreas Laurense and published by . This book was released on 2019 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to keep self-driving cars safe, it is important that these vehicles can plan safe trajectories through their environment and have the ability to robustly use their full tire-force potential. Racing at the limits of handling provides a purposefully challenging scenario for the development of reliable vehicle-motion planning and control techniques, as race cars are constantly pushed to their physical limits. With a common trajectory-tracking architecture for automated vehicle control, steering provides path-tracking control, and the throttle and brakes are used to track a desired speed profile. For the specific application of racing, this speed profile can be designed to fully use the tire-force potential. Experimental data show that a preexisting control framework based on this approach can match the lap time of an amateur race-car driver, but a professional race-car driver proves to be slightly faster. It is demonstrated with both experimental results and an analytical method that with this decoupled path-tracking and speed-tracking controller, an automated vehicle is prone to either under-utilize the tires or lose control over the path-tracking dynamics when unintentionally operating beyond the limit. Furthermore, a professional race-car driver successfully operates the vehicle with a control strategy that seems fundamentally different from trajectory tracking. Namely, he shows significant lap-to-lap variations in both speed and path, but he is consistently faster than automated trajectory-tracking control. This inspires new strategies for automated vehicle control. In this context, two novel feedback-control strategies are presented, which employ slip-angle control to robustly use the vehicle's full tire-force potential, while speed control provides the path-tracking functionality. Subsequently, in order to have the ability to also adjust the vehicle's path, a Nonlinear Model Predictive Control (NMPC) framework is presented which can trade-off longitudinal and lateral control inputs. Experimental results demonstrate that this controller successfully coordinates the inputs at the limits of handling. However, the computational burden of this NMPC framework limits the length of the planning horizon for real-time control, which in turn inhibits its ability to adjust the vehicle's path and speed. To address this issue, a new NMPC framework is developed, which serially cascades vehicle models of different levels of complexity in the planning horizon. Experimental results on an automated race car demonstrate the benefits of this new concept, with a high quality of control provided by a high-fidelity vehicle model in the near-term planning horizon, and significant extension of the planning horizon with a low-fidelity model.

Download or read book An Architecture for Integrated Decision making Motion Planning and Control of Automated Vehicles written by Vivian Zhang Patterson and published by . This book was released on 2022 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Automated vehicles have immense potential to improve the safety of our roadways. In order to handle the complex task of driving, they need the ability to make decisions regarding other road users, plan a trajectory, and control the vehicle, responding online to an evolving environment. For model-based control, it is important to use models that capture the full range of the vehicle's dynamics. We develop a tire model that is computationally tractable and useful in scenarios ranging from a stop-and-go maneuver to driving at the limits of road-tire friction. A model-based steering controller successfully demonstrates the efficacy of this tire model even when the vehicle is sliding on low-friction surfaces. In addition to being able to control the vehicle, an AV architecture must also be able to make discrete decisions regarding obstacles in the environment. Ultimately, these decisions are carried out by a controller commanding steering and longitudinal inputs, which motivates building a system that makes decisions based on the capabilities of the underlying controller. Our novel architecture partitions the drivable space into discrete options, solves a nonlinear optimization in each option in parallel, and then picks the solution that best satisfies high-level objectives such as safety and efficiency. Finally, frameworks for automated vehicles need to be designed with human values in mind. Safety is a top priority and is codified in legal texts as duty of due care. By leveraging the architecture described above to realize these human values, the vehicle drives safely and comfortably in an overtaking maneuver with oncoming traffic.

Download or read book Motion planning and feedback control techniques with applications to long tractor trailer vehicles written by Oskar Ljungqvist and published by Linköping University Electronic Press. This book was released on 2020-04-20 with total page 119 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, improved sensor and hardware technologies as well as new methods and algorithms have made self-driving vehicles a realistic possibility in the near future. At the same time, there has been a growing demand within the transportation sector to increase efficiency and to reduce the environmental impact related to transportation of people and goods. Therefore, many leading automotive and technology companies have turned their attention towards developing advanced driver assistance systems and self-driving vehicles. Autonomous vehicles are expected to have their first big impact in closed environments, such as mines, harbors, loading and offloading sites. In such areas, the legal requirements are less restrictive and the surrounding environment is more controlled and predictable compared to urban areas. Expected positive outcomes include increased productivity and safety, reduced emissions and the possibility to relieve the human from performing complex or dangerous tasks. Within these sites, tractor-trailer vehicles are frequently used for transportation. These vehicles are composed of several interconnected vehicle segments, and are therefore large, complex and unstable while reversing. This thesis addresses the problem of designing efficient motion planning and feedback control techniques for such systems. The contributions of this thesis are within the area of motion planning and feedback control for long tractor-trailer combinations operating at low-speeds in closed and unstructured environments. It includes development of motion planning and feedback control frameworks, structured design tools for guaranteeing closed-loop stability and experimental validation of the proposed solutions through simulations, lab and field experiments. Even though the primary application in this work is tractor-trailer vehicles, many of the proposed approaches can with some adjustments also be used for other systems, such as drones and ships. The developed sampling-based motion planning algorithms are based upon the probabilistic closed-loop rapidly exploring random tree (CL-RRT) algorithm and the deterministic lattice-based motion planning algorithm. It is also proposed to use numerical optimal control offline for precomputing libraries of optimized maneuvers as well as during online planning in the form of a warm-started optimization step. To follow the motion plan, several predictive path-following control approaches are proposed with different computational complexity and performance. Common for these approaches are that they use a path-following error model of the vehicle for future predictions and are tailored to operate in series with a motion planner that computes feasible paths. The design strategies for the path-following approaches include linear quadratic (LQ) control and several advanced model predictive control (MPC) techniques to account for physical and sensing limitations. To strengthen the practical value of the developed techniques, several of the proposed approaches have been implemented and successfully demonstrated in field experiments on a full-scale test platform. To estimate the vehicle states needed for control, a novel nonlinear observer is evaluated on the full-scale test vehicle. It is designed to only utilize information from sensors that are mounted on the tractor, making the system independent of any sensor mounted on the trailer. Under de senaste årtiondena har utvecklingen av sensor- och hårdvaruteknik gått i en snabb takt, samtidigt som nya metoder och algoritmer har introducerats. Samtidigt ställs det stora krav på transportsektorn att öka effektiviteten och minska miljöpåverkan vid transporter av både människor och varor. Som en följd av detta har många ledande fordonstillverkare och teknikföretag börjat satsat på att utveckla avancerade förarstödsystem och självkörande fordon. Även forskningen inom autonoma fordon har under de senaste årtiondena kraftig ökat då en rad tekniska problem återstår att lösas. Förarlösa fordon förväntas få sitt första stora genombrott i slutna miljöer, såsom gruvor, hamnar, lastnings- och lossningsplatser. I sådana områden är lagstiftningen mindre hård jämfört med stadsområden och omgivningen är mer kontrollerad och förutsägbar. Några av de förväntade positiva effekterna är ökad produktivitet och säkerhet, minskade utsläpp och möjligheten att avlasta människor från att utföra svåra eller farliga uppgifter. Inom dessa platser används ofta lastbilar med olika släpvagnskombinationer för att transportera material. En sådan fordonskombination är uppbyggd av flera ihopkopplade moduler och är således utmanande att backa då systemet är instabilt. Detta gör det svårt att utforma ramverk för att styra sådana system vid exempelvis autonom backning. Självkörande fordon är mycket komplexa system som består av en rad olika komponenter vilka är designade för att lösa separata delproblem. Två viktiga komponenter i ett självkörande fordon är dels rörelseplaneraren som har i uppgift att planera hur fordonet ska röra sig för att på ett säkert sätt nå ett överordnat mål, och dels den banföljande regulatorn vars uppgift är att se till att den planerade manövern faktiskt utförs i praktiken trots störningar och modellfel. I denna avhandling presenteras flera olika algoritmer för att planera och utföra komplexa manövrar för lastbilar med olika typer av släpvagnskombinationer. De presenterade algoritmerna är avsedda att användas som avancerade förarstödsystem eller som komponenter i ett helt autonomt system. Även om den primära applikationen i denna avhandling är lastbilar med släp, kan många av de förslagna algoritmerna även användas för en rad andra system, så som drönare och båtar. Experimentell validering är viktigt för att motivera att en föreslagen algoritm är användbar i praktiken. I denna avhandling har flera av de föreslagna planerings- och reglerstrategierna implementerats på en småskalig testplattform och utvärderats i en kontrollerad labbmiljö. Utöver detta har även flera av de föreslagna ramverken implementerats och utvärderats i fältexperiment på en fullskalig test-plattform som har utvecklats i samarbete med Scania CV. Här utvärderas även en ny metod för att skatta släpvagnens beteende genom att endast utnyttja information från sensorer monterade på lastbilen, vilket gör det föreslagna ramverket oberoende av sensorer monterade på släpvagnen.

Download or read book Passivity Based Model Predictive Control for Mobile Vehicle Motion Planning written by Adnan Tahirovic and published by Springer Science & Business Media. This book was released on 2013-04-18 with total page 64 pages. Available in PDF, EPUB and Kindle. Book excerpt: Passivity-based Model Predictive Control for Mobile Vehicle Navigation represents a complete theoretical approach to the adoption of passivity-based model predictive control (MPC) for autonomous vehicle navigation in both indoor and outdoor environments. The brief also introduces analysis of the worst-case scenario that might occur during the task execution. Some of the questions answered in the text include: • how to use an MPC optimization framework for the mobile vehicle navigation approach; • how to guarantee safe task completion even in complex environments including obstacle avoidance and sideslip and rollover avoidance; and • what to expect in the worst-case scenario in which the roughness of the terrain leads the algorithm to generate the longest possible path to the goal. The passivity-based MPC approach provides a framework in which a wide range of complex vehicles can be accommodated to obtain a safer and more realizable tool during the path-planning stage. During task execution, the optimization step is continuously repeated to take into account new local sensor measurements. These ongoing changes make the path generated rather robust in comparison with techniques that fix the entire path prior to task execution. In addition to researchers working in MPC, engineers interested in vehicle path planning for a number of purposes: rescued mission in hazardous environments; humanitarian demining; agriculture; and even planetary exploration, will find this SpringerBrief to be instructive and helpful.

Download or read book On motion planning and control for truck and trailer systems written by Oskar Ljungqvist and published by Linköping University Electronic Press. This book was released on 2019-01-22 with total page 78 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the last decades, improved sensor and hardware technologies as well as new methods and algorithms have made self-driving vehicles a realistic possibility in the near future. Thanks to this technology enhancement, many leading automotive and technology companies have turned their attention towards developing advanced driver assistance systems (ADAS) and self-driving vehicles. Autonomous vehicles are expected to have their first big impact in closed areas, such as mines, harbors and loading/offloading sites. In such areas, the legal requirements are less restrictive and the surrounding environment is more controlled and predictable compared to urban areas. Expected positive outcomes include increased productivity and safety, reduced emissions and the possibility to relieve the human from performing complex or dangerous tasks. Within these sites, different truck and trailer systems are used to transport materials. These systems are composed of several interconnected modules, and are thus large and highly unstable while reversing. This thesis addresses the problem of designing efficient motion planning and feedback control frameworks for such systems. First, a cascade controller for a reversing truck with a dolly-steered trailer is presented. The unstable modes of the system is stabilized around circular equilibrium configurations using a gain-scheduled linear quadratic (LQ) controller together with a higher-level pure pursuit controller to enable path following of piecewise linear reference paths. The cascade controller is then used within a rapidly-exploring random tree (RRT) framework and the complete motion planning and control framework is demonstrated on a small-scale test vehicle. Second, a path following controller for a reversing truck with a dolly-steered trailer is proposed for the case when the obtained motion plan is kinematically feasible. The control errors of the system are modeled in terms of their deviation from the nominal path and a stabilizing LQ controller with feedforward action is designed based on the linearization of the control error model. Stability of the closed-loop system is proven by combining global optimization, theory from linear differential inclusions and linear matrix inequality techniques. Third, a systematic framework is presented for analyzing stability of the closed-loop system consisting of a controlled vehicle and a feedback controller, executing a motion plan computed by a lattice planner. When this motion planner is considered, it is shown that the closed-loop system can be modeled as a nonlinear hybrid system. Based on this, a novel method is presented for analyzing the behavior of the tracking error, how to design the feedback controller and how to potentially impose constraints on the motion planner in order to guarantee that the tracking error is bounded and decays towards zero. Fourth, a complete motion planning and control solution for a truck with a dolly-steered trailer is presented. A lattice-based motion planner is proposed, where a novel parametrization of the vehicle’s state-space is proposed to improve online planning time. A time-symmetry result is established that enhance the numerical stability of the numerical optimal control solver used for generating the motion primitives. Moreover, a nonlinear observer for state estimation is developed which only utilizes information from sensors that are mounted on the truck, making the system independent of additional trailer sensors. The proposed framework is implemented on a full-scale truck with a dolly-steered trailer and results from a series of field experiments are presented.

Download or read book Motion Planning and Control of Automated Vehicles in Critical Situations written by Lars Svensson and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Probabilistic Motion Planning for Automated Vehicles written by Naumann, Maximilian and published by KIT Scientific Publishing. This book was released on 2021-02-25 with total page 192 pages. Available in PDF, EPUB and Kindle. Book excerpt: In motion planning for automated vehicles, a thorough uncertainty consideration is crucial to facilitate safe and convenient driving behavior. This work presents three motion planning approaches which are targeted towards the predominant uncertainties in different scenarios, along with an extended safety verification framework. The approaches consider uncertainties from imperfect perception, occlusions and limited sensor range, and also those in the behavior of other traffic participants.

Download or read book Recent Advances in Motion Planning and Control of Autonomous Vehicles written by Bai Li and published by Mdpi AG. This book was released on 2023-12-19 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles are increasingly prevalent, navigating both structured urban roads and challenging offroad scenes. At the core of these vehicles lie the planning and control modules, which are crucial for demonstrating the intelligence inherent in an autonomous driving system. The planning module is responsible for devising an open-loop trajectory, taking into account a variety of environmental restrictions, task-related demands, and vehicle-kinematics-related constraints, while the control module ensures adherence to this trajectory in a closed-loop manner. This adherence is vital in a range of conditions, including diverse weather scenarios, different driving situations, and in response to potential disturbances such as mechanical failures or cyber threats. In certain contexts, these modules are collectively referred to as 'control', with the planning component considered an open-loop controller. This Special Issue focuses on the latest research trends in planning and control methods for autonomous driving. It comprises 11 papers that cover a broad spectrum of applications, including occlusion-aware motion planning in warehouses, control strategies for articulated vehicles, cooperative trajectory planning for autonomous forklifts, and tracking control for underwater vehicles in the face of disturbances and uncertainties. These contributions collectively underscore the diverse and evolving nature of autonomous vehicle technology.

Download or read book Robust MPC Based Motion Planning and Control of Autonomous Ground Vehicles written by Vivek Bithar and published by . This book was released on 2020 with total page 133 pages. Available in PDF, EPUB and Kindle. Book excerpt: The motion planning layer of an autonomous software stack is responsible for the planning of an obstacle avoidance path in all possible scenarios. Emergency scenarios where maneuvers that must be planned are at the limits of vehicle handling are the most challenging path planning problems due to the presence of inherent uncertainties in the modeling, localization/state estimation, and the environment perception.

Download or read book Path Planning and Robust Control of Autonomous Vehicles written by Sheng Zhu (Mechanical engineer) and published by . This book was released on 2020 with total page 198 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous driving is gaining popularity in research interest and industry investment over the last decade, due to its potential to increase driving safety to avoid driver errors which account for over 90% of all motor vehicle crashes. It could also help to improve public mobility especially for the disabled, and to boost the productivity due to enlarged traffic capacity and accelerated traffic flows. The path planning and following control, as the two essential modules for autonomous driving, still face critical challenges in implementations in a dynamically changing driving environment. For the local path/trajectory planning, multifold requirements need to be satisfied including reactivity to avoid collision with other objects, smooth curvature variation for passenger comfort, feasibility in terms of vehicle control, and the computation efficiency for real-time implementations. The feedback control is required afterward to accurately follow the planned path or trajectory by deciding appropriate actuator inputs, and favors smooth control variations to avoid sudden jerks. The control may also subject to instability or performance deterioration due to continuously changing operating conditions along with the model uncertainties. The dissertation contributes by raising the framework of path planning and control to address these challenges. Local on-road path planning methods from two-dimensional (2D) geometric path to the model-based state trajectory is explored. The latter one is emphasized due to its advantages in considering the vehicle model, state and control constraints to ensure dynamic feasibility. The real-time simulation is made possible with the adoption of control parameterization and lookup tables to reduce computation cost, with scenarios showing its smooth planning and the reactivity in collision avoidance with other traffic agents. The dissertation also explores both robust gain-scheduling law and model predictive control (MPC) for path following. The parameter-space approach is introduced in the former with validated robust performance under the uncertainty of vehicle load, speed and tire saturation parameter through hardware-in-the-loop and vehicle experiments. The focus is also put on improving the safety of the intended functionality (SOTIF) to account for the potential risks caused by lack of situational awareness in the absence of a system failure. Such safety hazards include the functional inability to comprehend the situation and the insufficient robustness to diverse conditions. The dissertation enhanced the SOTIF with parameter estimation through sensor fusion to increase the vehicle situational awareness of its internal and external conditions, such as the road friction coefficient. The estimated road friction coefficient helps in planning a dynamically feasible trajectory under adverse road condition. The integration of vehicle stability control with autonomous driving functions is also explored in the case that the road friction coefficient estimation is not responsive due to insufficiency in time and excitations.

Download or read book Planning Universal On Road Driving Strategies for Automated Vehicles written by Steffen Heinrich and published by Springer. This book was released on 2018-04-19 with total page 141 pages. Available in PDF, EPUB and Kindle. Book excerpt: Steffen Heinrich describes a motion planning system for automated vehicles. The planning method is universally applicable to on-road scenarios and does not depend on a high-level maneuver selection automation for driving strategy guidance. The author presents a planning framework using graphics processing units (GPUs) for task parallelization. A method is introduced that solely uses a small set of rules and heuristics to generate driving strategies. It was possible to show that GPUs serve as an excellent enabler for real-time applications of trajectory planning methods. Like humans, computer-controlled vehicles have to be fully aware of their surroundings. Therefore, a contribution that maximizes scene knowledge through smart vehicle positioning is evaluated. A post-processing method for stochastic trajectory validation supports the search for longer-term trajectories which take ego-motion uncertainty into account. About the Author Steffen Heinrich has a strong background in robotics and artificial intelligence. Since 2009 he has been developing algorithms and software components for self-driving systems in research facilities and for automakers in Germany and the US.

Download or read book Autonomous Vehicles written by Adam Levitt and published by . This book was released on 2006 with total page 244 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Vehicle Dynamics and Control written by Shahram Azadi and published by Elsevier. This book was released on 2021-03-25 with total page 510 pages. Available in PDF, EPUB and Kindle. Book excerpt: Vehicle Dynamics and Control: Advanced Methodologies features the latest information on advanced dynamics and vehicle motion control, including a comprehensive overview of passenger cars and articulated vehicles, fundamentals, and emerging developments. This book provides a unified, balanced treatment of advanced approaches to vehicle dynamics and control. It proceeds to cover advanced vehicle control strategies, such as identification and estimation, adaptive nonlinear control, new robust control techniques, and soft computing. Other topics, such as the integrated control of passenger cars and articulated heavy vehicles, are also discussed with a significant amount of material on engineering methodology, simulation, modeling, and mathematical verification of the systems. This book discusses and solves new challenges in vehicle dynamics and control problems and helps graduate students in the field of automotive engineering as well as researchers and engineers seeking theoretical/practical design procedures in automotive control systems. Provides a vast spectrum of advanced vehicle dynamics and control systems topics and current research trends Provides an extensive discussion in some advanced topics on commercial vehicles, such as dynamics and control of semitrailer carrying liquid, integrated control system design, path planning and tracking control in the autonomous articulated vehicle

Download or read book Decision Making Planning and Control Strategies for Intelligent Vehicles written by Haotian Cao and published by Springer Nature. This book was released on 2022-05-31 with total page 128 pages. Available in PDF, EPUB and Kindle. Book excerpt: 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.

Download or read book Motion Planning and Control of Autonomous Vehicles Using Collision and Rendezvous Cones written by Vishwamithra Reddy Sunkara and published by . This book was released on 2018 with total page 168 pages. Available in PDF, EPUB and Kindle. Book excerpt: This dissertation uses the notion of collision cones and rendezvous cones to address several motion planning problems for autonomous vehicles. Collision avoidance is fundamental to the problem of planning safe trajectories in dynamic environments. This problem appears in several diverse elds including robotics, air vehicles, underwater vehicles and computer animation. In the rendezvous problem, generating appropriate trajectories to achieve overlap of footprints of unmanned aerial vehicles is important in problems related to search and surveillance, and for establishing communication between a network of UAVs, or between a user and a base station in remote areas. In the collision avoidance problem, much of the collision avoidance literature assumes shapes of the objects as circles. However, when objects are operating in closer proximity, or when objects are elongated and/or have non-convex shapes, a less conservative approach, that considers the exact shapes of the objects, is more desirable. This dissertation presents analytical collision avoidance laws in cooperative and non-cooperative dynamic environments. The collision avoidance laws are simulated on Ionic Polymer-Metal Composite (IPMC) actuated robotic sh. Collision cones are also used to analyze pursuit evasion games between two objects of arbitrary shapes. Collision avoidance of objects that can deform by changing their shape as a function of time is also presented. The rendezvous problem requires communication/sensing footprints of vehicles to overlap. The need of the footprints to overlap is dictated by the requirement that no part of the sensed area is left uncovered in a search and surveillance operation; or by the need to position a relay UAV in the overlap region of two distant UAVs in order to enable them to communicate with each other. The concept of a rendezvous cone is used as the basis for the development of nonlinear analytical guidance laws that enable the overlap of footprints to the requisite depth.

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 On Road Intelligent Vehicles written by Rahul Kala and published by Butterworth-Heinemann. This book was released on 2016-04-27 with total page 538 pages. Available in PDF, EPUB and Kindle. Book excerpt: On-Road Intelligent Vehicles: Motion Planning for Intelligent Transportation Systems deals with the technology of autonomous vehicles, with a special focus on the navigation and planning aspects, presenting the information in three parts. Part One deals with the use of different sensors to perceive the environment, thereafter mapping the multi-domain senses to make a map of the operational scenario, including topics such as proximity sensors which give distances to obstacles, vision cameras, and computer vision techniques that may be used to pre-process the image, extract relevant features, and use classification techniques like neural networks and support vector machines for the identification of roads, lanes, vehicles, obstacles, traffic lights, signs, and pedestrians. With a detailed insight into the technology behind the vehicle, Part Two of the book focuses on the problem of motion planning. Numerous planning techniques are discussed and adapted to work for multi-vehicle traffic scenarios, including the use of sampling based approaches comprised of Genetic Algorithm and Rapidly-exploring Random Trees and Graph search based approaches, including a hierarchical decomposition of the algorithm and heuristic selection of nodes for limited exploration, Reactive Planning based approaches, including Fuzzy based planning, Potential Field based planning, and Elastic Strip and logic based planning. Part Three of the book covers the macroscopic concepts related to Intelligent Transportation Systems with a discussion of various topics and concepts related to transportation systems, including a description of traffic flow, the basic theory behind transportation systems, and generation of shock waves. Provides an overall coverage of autonomous vehicles and Intelligent Transportation Systems Presents a detailed overview, followed by the challenging problems of navigation and planning Teaches how to compare, contrast, and differentiate navigation algorithms