Download or read book Planning Universal On road Driving Strategies for Automated Vehicles written by Steffen Heinrich and published by . This book was released on 2018 with total page 133 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. Contents A Framework for Universal Driving Strategy Planning Sampling-Based Planning in Phase Space A Universal Approach for Driving Strategies Modeling Ego Motion Uncertainty Target Groups Scientists and students in the field of robotics, computer science, mechanical engineering Engineers in the field of vehicle automation, intelligent systems and robotics 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 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 The End of Driving written by Bern Grush and published by Elsevier. This book was released on 2018-06-25 with total page 334 pages. Available in PDF, EPUB and Kindle. Book excerpt: While many transportation and city planners, researchers, students, practitioners, and political leaders are familiar with the technical nature and promise of vehicle automation, consensus is not yet often seen on the impact that will result, or the policies and actions that those responsible for transportation systems should take. The End of Driving: Transportation Systems and Public Policy Planning for Autonomous Vehicles explores both the potential of vehicle automation technology and the barriers it faces when considering coherent urban deployment. The book evaluates the case for deliberate development of automated public transportation and mobility-as-a-service as paths towards sustainable mobility, describing critical approaches to the planning and management of vehicle automation technology. It serves as a reference for understanding the full life cycle of the multi-year transportation systems planning processes, including novel regulation, planning, and acquisition tools for regional transportation. Application-oriented, research-based, and solution-oriented rather than predict-and-warn, The End of Driving concludes with a detailed discussion of the systems design needed for accomplishing this shift. From the Foreword by Susan Shaheen: The authors ... extend potential solutions through a set of open-ended exercises after each chapter. Their approach is both strategic and deliberate. They lead the reader from definitions and context setting to the transition toward automation, employing a range of creative strategies and policies. While our quest to understand how to deploy automated vehicles is just beginning, this book provides a thoughtful introduction to inform this evolution. - Offers a workable public transit solution design melding the traditional "acquire-and-operate mode with the absorption of new technology - Provides a step-by-step discussion of digital systems designs and effective regulation-by-data approaches needed for a new urban mobility - Learning aids include case study scenarios, chapter objectives and discussion questions, sidebars and a glossary

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 Trajectory Planning of an Autonomous Vehicle in Multi Vehicle Traffic Scenarios written by Mahdi Morsali and published by Linköping University Electronic Press. This book was released on 2021-03-25 with total page 25 pages. Available in PDF, EPUB and Kindle. Book excerpt: Tremendous industrial and academic progress and investments have been made in au-tonomous driving, but still many aspects are unknown and require further investigation,development and testing. A key part of an autonomous driving system is an efficient plan-ning algorithm with potential to reduce accidents, or even unpleasant and stressful drivingexperience. A higher degree of automated planning also makes it possible to have a betterenergy management strategy with improved performance through analysis of surroundingenvironment of autonomous vehicles and taking action in a timely manner. This thesis deals with planning of autonomous vehicles in different urban scenarios, road,and vehicle conditions. The main concerns in designing the planning algorithms, are realtime capability, safety and comfort. The planning algorithms developed in this thesis aretested in simulation traffic situations with multiple moving vehicles as obstacles. The re-search conducted in this thesis falls mainly into two parts, the first part investigates decou-pled trajectory planning algorithms with a focus on speed planning, and the second sectionexplores different coupled planning algorithms in spatiotemporal environments where pathand speed are calculated simultaneously. Additionally, a behavioral analysis is carried outto evaluate different tactical maneuvers the autonomous vehicle can have considering theinitial states of the ego and surrounding vehicles. Particularly relevant for heavy duty vehicles, the issues addressed in designing a safe speedplanner in the first part are road conditions such as banking, friction, road curvature andvehicle characteristics. The vehicle constraints on acceleration, jerk, steering, steer ratelimitations and other safety limitations such as rollover are further considerations in speedplanning algorithms. For real time purposes, a minimum working roll model is identified us-ing roll angle and lateral acceleration data collected in a heavy duty truck. In the decoupledplanners, collision avoiding is treated using a search and optimization based planner. In an autonomous vehicle, the structure of the road network is known to the vehicle throughmapping applications. Therefore, this key property can be used in planning algorithms toincrease efficiency. The second part of the thesis, is focused on handling moving obstaclesin a spatiotemporal environment and collision-free planning in complex urban structures.Spatiotemporal planning holds the benefits of exhaustive search and has advantages com-pared to decoupled planning, but the search space in spatiotemporal planning is complex.Support vector machine is used to simplify the search problem to make it more efficient.A SVM classifies the surrounding obstacles into two categories and efficiently calculate anobstacle free region for the ego vehicle. The formulation achieved by solving SVM, con-tains information about the initial point, destination, stationary and moving obstacles.These features, combined with smoothness property of the Gaussian kernel used in SVMformulation is proven to be able to solve complex planning missions in a safe way. Here, three algorithms are developed by taking advantages of SVM formulation, a greedysearch algorithm, an A* lattice based planner and a geometrical based planner. One general property used in all three algorithms is reduced search space through using SVM. In A*lattice based planner, significant improvement in calculation time, is achieved by using theinformation from SVM formulation to calculate a heuristic for planning. Using this heuristic,the planning algorithm treats a simple driving scenario and a complex urban structureequal, as the structure of the road network is included in SVM solution. Inspired byobserving significant improvements in calculation time using SVM heuristic and combiningthe collision information from SVM surfaces and smoothness property, a geometrical planneris proposed that leads to further improvements in calculation time. Realistic driving scenarios such as roundabouts, intersections and takeover maneuvers areused, to test the performance of the proposed algorithms in simulation. Different roadconditions with large banking, low friction and high curvature, and vehicles prone to safetyissues, specially rollover, are evaluated to calculate the speed profile limits. The trajectoriesachieved by the proposed algorithms are compared to profiles calculated by optimal controlsolutions.

Download or read book Decision making Strategies for Automated Driving in Urban Environments written by Antonio Artuñedo and published by Springer Nature. This book was released on 2020-04-25 with total page 205 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes an effective decision-making and planning architecture for enhancing the navigation capabilities of automated vehicles in the presence of non-detailed, open-source maps. The system involves dynamically obtaining road corridors from map information and utilizing a camera-based lane detection system to update and enhance the navigable space in order to address the issues of intrinsic uncertainty and low-fidelity. An efficient and human-like local planner then determines, within a probabilistic framework, a safe motion trajectory, ensuring the continuity of the path curvature and limiting longitudinal and lateral accelerations. LiDAR-based perception is then used to identify the driving scenario, and subsequently re-plan the trajectory, leading in some cases to adjustment of the high-level route to reach the given destination. The method has been validated through extensive theoretical and experimental analyses, which are reported here in detail.

Download or read book Autonomous Road Vehicle Path Planning and Tracking Control written by Levent Guvenc and published by John Wiley & Sons. This book was released on 2021-12-06 with total page 256 pages. Available in PDF, EPUB and Kindle. Book excerpt: Discover the latest research in path planning and robust path tracking control In Autonomous Road Vehicle Path Planning and Tracking Control, a team of distinguished researchers delivers a practical and insightful exploration of how to design robust path tracking control. The authors include easy to understand concepts that are immediately applicable to the work of practicing control engineers and graduate students working in autonomous driving applications. Controller parameters are presented graphically, and regions of guaranteed performance are simple to visualize and understand. The book discusses the limits of performance, as well as hardware-in-the-loop simulation and experimental results that are implementable in real-time. Concepts of collision and avoidance are explained within the same framework and a strong focus on the robustness of the introduced tracking controllers is maintained throughout. In addition to a continuous treatment of complex planning and control in one relevant application, the Autonomous Road Vehicle Path Planning and Tracking Control includes: A thorough introduction to path planning and robust path tracking control for autonomous road vehicles, as well as a literature review with key papers and recent developments in the area Comprehensive explorations of vehicle, path, and path tracking models, model-in-the-loop simulation models, and hardware-in-the-loop models Practical discussions of path generation and path modeling available in current literature In-depth examinations of collision free path planning and collision avoidance Perfect for advanced undergraduate and graduate students with an interest in autonomous vehicles, Autonomous Road Vehicle Path Planning and Tracking Control is also an indispensable reference for practicing engineers working in autonomous driving technologies and the mobility groups and sections of automotive OEMs.

Download or read book Path Planning for Autonomous Vehicle written by Umar Zakir Abdul Hamid and published by BoD – Books on Demand. This book was released on 2019-10-02 with total page 150 pages. Available in PDF, EPUB and Kindle. Book excerpt: Path Planning (PP) is one of the prerequisites in ensuring safe navigation and manoeuvrability control for driverless vehicles. Due to the dynamic nature of the real world, PP needs to address changing environments and how autonomous vehicles respond to them. This book explores PP in the context of road vehicles, robots, off-road scenarios, multi-robot motion, and unmanned aerial vehicles (UAVs ).

Download or read book Belief State Planning for Autonomous Driving Planning with Interaction Uncertain Prediction and Uncertain Perception written by Hubmann, Constantin and published by KIT Scientific Publishing. This book was released on 2021-09-13 with total page 178 pages. Available in PDF, EPUB and Kindle. Book excerpt: This work presents a behavior planning algorithm for automated driving in urban environments with an uncertain and dynamic nature. The algorithm allows to consider the prediction uncertainty (e.g. different intentions), perception uncertainty (e.g. occlusions) as well as the uncertain interactive behavior of the other agents explicitly. Simulating the most likely future scenarios allows to find an optimal policy online that enables non-conservative planning under uncertainty.

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 Model Fidelity and Trajectory Planning for Autonomous Vehicles at the Limit written by John Karl Subosits and published by . This book was released on 2020 with total page 135 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles have the potential to greatly improve transportation safety by eliminating many automobile accidents, the vast majority of which are caused by human error. However, for cars to be able to avoid an accident whenever physically possible, they will have to drive at least as well as the best human drivers. Racing drivers can claim to be the best drivers in the world since, by the nature of their sport, they are forced to consistently and safely operate the vehicle at its physical limits. Autonomous racing provides an avenue to rapidly develop insights and control strategies for autonomous vehicles that are applicable to emergencies on public roads. This thesis expands the understanding of what effects must be captured for a vehicle to drive at the limits of friction. First, the impact of road topography on the vehicle's limits is discussed and modeled. Experiments with an automated vehicle show that accounting for topography-driven variation in normal load is critical for ensuring that the vehicle stays within its limits. The same simple model used to generate those insights is also useful for rapid trajectory replanning, illustrated here through examples covering obstacle avoidance and racing line optimization. This approach to trajectory modification constitutes the second contribution of this thesis. While the simple model upon which the method is based captures the most fundamental limitations of the vehicle, it is worth examining the extent to which more complex models of the vehicle's dynamics lead to better performance. An evaluation of the utility of several possible models for generating trajectories at the limit on various surfaces, including ice, wet asphalt, and dry asphalt, shows that the models' prescriptions for the optimal trajectory vary little and that all can be used successfully. However, a significant advantage of the more complex models is that the many actuators available on modern vehicles may be used in a coordinated fashion to better accomplish the desired control objective. To this end, a novel model of the effects of a limited slip differential is incorporated into the double-track model of the vehicle. The insights from this work can be used to design algorithms that operate over the full range of vehicle performance, maximizing an autonomous vehicle's ability to operate skillfully when racing or safely when confronted with an emergency.

Download or read book Road Vehicle Automation 7 written by Gereon Meyer and published by Springer Nature. This book was released on 2020-07-14 with total page 171 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the seventh volume of a sub-series on Road Vehicle Automation, published as part of the Lecture Notes in Mobility. Written by researchers, engineers and analysts from around the globe, the contributions are based on oral and poster presentations from the Automated Vehicles Symposium (AVS) 2019, held on July 15–18, 2019, in Orlando, Florida, USA. The book explores public sector activities, human factors aspects, vehicle systems and other related technological developments, as well as transportation infrastructure planning, which are expect to foster and support road vehicle automation.

Download or read book Autonomous Vehicle Technology written by James M. Anderson and published by Rand Corporation. This book was released on 2014-01-10 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: The automotive industry appears close to substantial change engendered by “self-driving” technologies. This technology offers the possibility of significant benefits to social welfare—saving lives; reducing crashes, congestion, fuel consumption, and pollution; increasing mobility for the disabled; and ultimately improving land use. This report is intended as a guide for state and federal policymakers on the many issues that this technology raises.

Download or read book Road Vehicle Automation 9 written by Gereon Meyer and published by Springer Nature. This book was released on 2022-07-09 with total page 188 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the ninth volume of a sub-series on Road Vehicle Automation, published as part of the Lecture Notes in Mobility. It gathers contributions to the Automated Road Transportation Symposium (ARTS), held on July 12-15, 2021, as a fully virtual event, and as a continuation of TRB's annual summer symposia on automated vehicle systems. Written by researchers, engineers and analysts from around the globe, this book offers a multidisciplinary perspectives on the opportunities and challenges associated with automating road transportation. It highlights innovative strategies, including public policies, infrastructure planning and automated technologies, which are expected to foster sustainable and automated mobility in the near future, thus addressing industry, government and research communities alike.

Download or read book Road Vehicle Automation 8 written by Gereon Meyer and published by Springer Nature. This book was released on 2021-07-08 with total page 115 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is the eight volume of a sub-series on Road Vehicle Automation, published as part of the Lecture Notes in Mobility. Written by researchers, engineers and analysts from around the globe, the contributions are based on oral and poster presentations from the Automated Vehicles Symposium (AVS) 2020, held on July 27–30, 2020, as a fully virtual event. The book explores public sector activities, human factors aspects, vehicle systems and other related technological developments, as well as transportation infrastructure planning, which are expect to foster and support road vehicle automation.

Download or read book Safe and Scalable Planning Under Uncertainty for Autonomous Driving written by Maxime Thomas Marcel Bouton and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous driving has the potential to significantly improve safety. Although progress has been made in recent years to deploy automated driving technologies, many situations handled on a daily basis by human drivers remain challenging for autonomous vehicles, such as navigating urban environments. They must reach their goal safely and efficiently while considering a multitude of traffic participants with rapidly changing behavior. Hand-engineering strategies to navigate such environments requires anticipating many possible situations and finding a suitable behavior for each, which places a large burden on the designer and is unlikely to scale to complicated situations. In addition, autonomous vehicles rely on on-board perception systems that give noisy estimates of the location and velocity of others on the road and are sensitive to occlusions. Autonomously navigating urban environments requires algorithms that reason about interactions with and between traffic participants with limited information. This thesis addresses the problem of automatically generating decision making strategies for autonomous vehicles in urban environments. Previous approaches relied on planning with respect to a mathematical model of the environment but have many limitations. A partially observable Markov decision process (POMDP) is a standard model for sequential decision making problems in dynamic, uncertain environments with imperfect sensor measurements. This thesis demonstrates a generic representation of driving scenarios as POMDPs, considering sensor occlusions and interactions between road users. A key contribution of this thesis is a methodology to scale POMDP approaches to complex environments involving a large number of traffic participants. To reduce the computational cost of considering multiple traffic participants, a decomposition method leveraging the strategies of interacting with a subset of road users is introduced. Decomposition methods can approximate the solutions to large sequential decision making problems at the expense of sacrificing optimality. This thesis introduces a new algorithm that uses deep reinforcement learning to bridge the gap with the optimal solution. Establishing trust in the generated decision strategies is also necessary for the deployment of autonomous vehicles. Methods to constrain a policy trained using reinforcement learning are introduced and combined with the proposed decomposition techniques. This method allows to learn policies with safety constraints. To address state uncertainty, a new methodology for computing probabilistic safety guarantees in partially observable domains is introduced. It is shown that the new method is more flexible and more scalable than previous work. The algorithmic contributions present in this thesis are applied to a variety of driving scenarios. Each algorithm is evaluated in simulation and compared to previous work. It is shown that the POMDP formulation in combination with scalable solving methods provide a flexible framework for planning under uncertainty for autonomous driving.

Download or read book Creating Autonomous Vehicle Systems Second Edition written by Liu Shaoshan and published by Springer Nature. This book was released on 2022-05-31 with total page 221 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book is one of the first technical overviews of autonomous vehicles written for a general computing and engineering audience. The authors share their practical experiences designing 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 as to its future 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, new algorithms can be tested so as to update the HD map—in addition to training better recognition, tracking, and decision models. Since the first edition of this book was released, many universities have adopted it in their autonomous driving classes, and the authors received many helpful comments and feedback from readers. Based on this, the second edition was improved by extending and rewriting multiple chapters and adding two commercial test case studies. In addition, a new section entitled “Teaching and Learning from this Book” was added to help instructors better utilize this book in their classes. The second edition captures the latest advances in autonomous driving and that it also presents usable real-world case studies to help readers better understand how to utilize their lessons in commercial autonomous driving projects. 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 extensive references for an effective, deeper exploration of the various technologies.