Download or read book Swarm based Trajectory Planning for Autonomous Cars written by Jan Fritz Ulbrich and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Trajectory Planning of Autonomous Vehicles written by Manuel Esquer Cerezo and published by . This book was released on 2018 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous driving is an emerging technology that is advancing in a very fast way. It is a complex challenge that involves many sections with plenty of different disciplines. One of the more important parts is trajectory planning, where this thesis it has been focused. This project revises the different algorithms of trajectory planning that have been proposed for autonomous cars. The reason why a trajectory planner based on numerical optimization algorithm such that Model Predictive Control (MPC) is proposed is also discussed. The main advantages are the possibility of generating the planning online allowing the replanning if unexpected events occurs (objects in the middle of the road, pedestrians appearing unexpectedly, etc.) and the facility of including several objectives in the optimization problem. This thesis studies different parameter that can define an optimal generated trajectory and how it is structured in the optimization program. Moreover there are several weights that should be tuned to orientate the trajectory planner in the direction that it is desired. All this tuning process is explained providing guidelines on how can be done for future cases. Finally, several testing results were included that are obtained with different parameters and structures of the program. These results are analysed and some conclusions of the efficiency of the MPC-based planning algorithm are obtained highlighting the advantages that it presents.

Download or read book A based Trajectory Planning in Dynamic Environments for Autonomous Vehicles written by Chinnawut Nantabut and published by . This book was released on 2023 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 Handbook of Research on Design Control and Modeling of Swarm Robotics written by Tan, Ying and published by IGI Global. This book was released on 2015-12-09 with total page 889 pages. Available in PDF, EPUB and Kindle. Book excerpt: Studies on robotics applications have grown substantially in recent years, with swarm robotics being a relatively new area of research. Inspired by studies in swarm intelligence and robotics, swarm robotics facilitates interactions between robots as well as their interactions with the environment. The Handbook of Research on Design, Control, and Modeling of Swarm Robotics is a collection of the most important research achievements in swarm robotics thus far, covering the growing areas of design, control, and modeling of swarm robotics. This handbook serves as an essential resource for researchers, engineers, graduates, and senior undergraduates with interests in swarm robotics and its applications.

Download or read book Moment based Risk bounded Trajectory Planning for Autonomous Vehicles written by Allen Mengyu Wang and published by . This book was released on 2020 with total page 154 pages. Available in PDF, EPUB and Kindle. Book excerpt: Uncertainty in the behavior of agents on the road is arguably one of the greatest challenges preventing the large scale deployment of fully autonomous vehicles on public roads. This uncertainty is complex and challenging to characterize: empirical data shows multi-modal and non-Gaussian distributions of future positions of human driven vehicles. To drive safely, autonomous driving systems should generate prediction distributions of agent future positions that are representative of the uncertainty and use these distributions to plan trajectories with risk bounds (i.e. certificates on risk). Risk-bounded trajectory planning is a challenging problem, especially given the stringent run-time constraints imposed by autonomous driving. Thus, current approaches that are fast enough for autonomous driving are largely restricted to assuming Gaussian sources of uncertainty with linear constraints and model the ego vehicle as a point mass. To address these limitations, this thesis aims to develop a risk-bounded trajectory planner that can: 1) use multi-modal non-Gaussian predictions of agent positions, 2) account for ego vehicle and agent geometries, and 3) run in real time. To achieve generality, we dene a prediction representation, AMM-PFT, that represents uncertainty in terms of statistical moments of the prediction distribution. This approach provides generality as statistical moments are universal properties of distributions, and we provide methods for computing them from agent predictions. We then develop methods for bounding risk, given an AMM-PFT, by using statistical moments in deterministic inequalities known as concentration inequalities. These concentration inequalities are then encoded in a fixed risk allocation optimization problem, which we show can plan trajectories with 50 time step horizons in 12.9ms on average. In some scenarios, these concentration inequalities can be excessively conservative, so we develop non-differentiable methods for risk assessment that are tighter, but cannot be directly encoded in a gradient based optimization routine. Instead, these risk assessment methods are used to inform the outer loop that sets the risk allocation for optimizations; we call this algorithm SRAR and show that it can significantly reduce the average cost of trajectories while remaining safe. We provide controlled experiments demonstrating the advantages and stability of our approach, and we also provide demonstrations of our trajectory planning system in a simulation environment where it can safely drive through a neighborhood with multiple uncertain agents to get to its goal destination. We also consider the problem of using prediction distributions of agent actions, such as accelerating and turning. To use such predictions, we need to compute AMM-PFTs from these action distributions by propagating the uncertainty in actions into uncertainty in positions using nonlinear dynamics models such as the Dubin's Car. While the particle filter and variants of the Kalman filter can perform this propagation approximately, we develop an algorithm, TreeRing, that can search for closed form systems of equations to perform this propagation exactly for discrete time polynomial systems. We show that the Dubin's car can be transformed into a polynomial system, thus allowing us to apply TreeRing to develop a method for exactly computing AMM-PFTs given distributions of agent acceleration and turning. In numerical experiments, we show that it is more accurate than linearized propagation with the Kalman filter and, with a run-time of less than a microsecond per time step, it is much faster than Monte Carlo methods. While we only explore this particular application of TreeRing, it has the potential to improve performance in other filtering applications.

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 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 Decision Making Techniques for Autonomous Vehicles written by Jorge Villagra and published by Elsevier. This book was released on 2023-03-03 with total page 426 pages. Available in PDF, EPUB and Kindle. Book excerpt: Decision-Making Techniques for Autonomous Vehicles provides a general overview of control and decision-making tools that could be used in autonomous vehicles. Motion prediction and planning tools are presented, along with the use of machine learning and adaptability to improve performance of algorithms in real scenarios. The book then examines how driver monitoring and behavior analysis are used produce comprehensive and predictable reactions in automated vehicles. The book ultimately covers regulatory and ethical issues to consider for implementing correct and robust decision-making. This book is for researchers as well as Masters and PhD students working with autonomous vehicles and decision algorithms. Provides a complete overview of decision-making and control techniques for autonomous vehicles Includes technical, physical, and mathematical explanations to provide knowledge for implementation of tools Features machine learning to improve performance of decision-making algorithms Shows how regulations and ethics influence the development and implementation of these algorithms in real scenarios

Download or read book Safe Interactive Motion Planning for Autonomous Cars written by Mingyu Wang and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: In the past decade, the autonomous driving industry has seen tremendous advancements thanks to the progress in computation, artificial intelligence, sensing capabilities, and other technologies related to autonomous vehicles. Today, autonomous cars operate in dense urban traffic, compared to the last generation of robots that were confined to isolated workspaces. In these human-populated environments, autonomous cars need to understand their surroundings and behave in an interpretable, human-like manner. In addition, autonomous robots are engaged in more social interactions with other humans, which requires an understanding of how multiple reactive agents act. For example, during lane changes, most attentive drivers would slow down to give space if an adjacent car shows signs of executing a lane change. For an autonomous car, understanding the mutual dependence between its action and others' actions is essential for the safety and viability of the autonomous driving industry. However, most existing trajectory planning approaches ignore the coupling between all agents' behaviors and treat the decisions of other agents as immutable. As a result, the planned trajectories are conservative, less intuitive, and may lead to unsafe behaviors. To address these challenges, we present motion planning frameworks that maintain the coupling of prediction and planning by explicitly modeling their mutual dependency. In the first part, we examine reciprocal collision avoidance behaviors among a group of intelligent robots. We propose a distributed, real-time collision avoidance algorithm based on Voronoi diagrams that only requires relative position measurements from onboard sensors. When necessary, the proposed controller minimally modifies a nominal control input and provides collision avoidance behaviors even with noisy sensor measurements. In the second part, we introduce a nonlinear receding horizon game-theoretic planner that approximates a Nash equilibrium in competitive scenarios among multiple cars. The proposed planner uses a sensitivity-enhanced objective function and iteratively plans for the ego vehicle and the other vehicles to reach an equilibrium strategy. The resulting trajectories show that the ego vehicle can leverage its influence on other vehicles' decisions and intentionally change their courses. The resulting trajectories exhibit rich interactive behaviors, such as blocking and overtaking in competitive scenarios among multiple cars. In the last part, we propose a risk-aware game-theoretic planner that takes into account uncertainties of the future trajectories. We propose an iterative dynamic programming algorithm to solve a feedback equilibrium strategy set for interacting agents with different risk sensitivities. Through simulations, we show that risk-aware planners generate safer behaviors when facing uncertainties in safety-critical situations. We also present a solution for the "inverse" risk-sensitive planning algorithm. The goal of the inverse problem is to learn the cost function as well as risk sensitivity for each individual. The proposed algorithm learns the cost function parameters from datasets collected from demonstrations with various risk sensitivity. Using the learned cost function, the ego vehicle can estimate the risk profile of an interacting agent online to improve safety and efficiency.

Download or read book Hierarchical Concept of Optimization Based Path Planning for Autonomous Driving written by Reza Dariani and published by . This book was released on 2016 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Robotic Systems Concepts Methodologies Tools and Applications written by Management Association, Information Resources and published by IGI Global. This book was released on 2020-01-03 with total page 2075 pages. Available in PDF, EPUB and Kindle. Book excerpt: Through expanded intelligence, the use of robotics has fundamentally transformed a variety of fields, including manufacturing, aerospace, medicine, social services, and agriculture. Continued research on robotic design is critical to solving various dynamic obstacles individuals, enterprises, and humanity at large face on a daily basis. Robotic Systems: Concepts, Methodologies, Tools, and Applications is a vital reference source that delves into the current issues, methodologies, and trends relating to advanced robotic technology in the modern world. Highlighting a range of topics such as mechatronics, cybernetics, and human-computer interaction, this multi-volume book is ideally designed for robotics engineers, mechanical engineers, robotics technicians, operators, software engineers, designers, programmers, industry professionals, researchers, students, academicians, and computer practitioners seeking current research on developing innovative ideas for intelligent and autonomous robotics systems.

Download or read book Autonomous Vehicles Applications and Perspectives written by Petar Piljek and published by BoD – Books on Demand. This book was released on 2023-09-27 with total page 160 pages. Available in PDF, EPUB and Kindle. Book excerpt: In recent times, remarkable progress has taken place in the field of autonomous vehicles, reshaping industries such as logistics, transportation, defense, and more. The quest for achieving fully autonomous systems has been a thrilling yet demanding journey, as researchers and engineers continually push the limits of technological ingenuity. Autonomous Vehicles - Applications and Perspectives delves into the field of autonomous vehicles across eight chapters that cover various facets of this domain. The book is organized into four sections: "Introduction", "Autonomous Vehicles Enabling Technologies", "Autonomous Vehicles Applications and Potentials", and "Challenges and Perspectives". Its main goal is to provide an informative resource for those interested in autonomous vehicles, inspiring progress and discussions for researchers, students, and professionals alike.

Download or read book Guidance and Control of Ocean Vehicles written by Thor I. Fossen and published by John Wiley & Sons. This book was released on 1994-09-20 with total page 504 pages. Available in PDF, EPUB and Kindle. Book excerpt: A comprehensive and extensive study of the latest research in control systems for marine vehicles. Demonstrates how the implementation of mathematical models and modern control theory can reduce fuel consumption and improve reliability and performance. Coverage includes ocean vehicle modeling, environmental disturbances, the dynamics and stability of ships, sensor and navigation systems. Numerous examples and exercises facilitate understanding.

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.

Download or read book Hybrid System Design and Optimization Based Path Planning for Autonomous Vehicles written by Shannon Zelinski and published by . This book was released on 2003 with total page 104 pages. Available in PDF, EPUB and Kindle. Book excerpt: