Download or read book Safe Intention aware Maneuvering of Autonomous Vehicles written by Xin Cyrus Huang and published by . This book was released on 2019 with total page 123 pages. Available in PDF, EPUB and Kindle. Book excerpt: In order to improve driving performance, while achieving safety in a dynamic environment, it is crucial for a vehicle motion planner to be aware of the intentions of the surrounding agent vehicles. Many existing approaches that ignore the intentions of surrounding vehicles would produce risky or over-conservative plans. In this thesis, we describe a maneuver motion planning system that achieves both safety and efficiency by estimating the types of surrounding drivers and the vehicle motions being executed by them over a finite horizon in the future. Our claim is that a vehicle is able to efficiently and safely navigate in dynamic traffic situations by estimating the possible types of drivers in its immediate vicinity, such as aggressive or careful, and by predicting their likely maneuvers and motions as probability distributions. To perform these predictions, we first employ a vehicle model that incorporates different driving styles, possible types of maneuvers, the likely trajectories that these maneuvers produce, and the likelihood of transitioning between successive maneuvers. The vehicle models are combined and encoded as hybrid partially observable Markov decision processes (POMDPs) whose discrete elements represent driving styles and maneuvers for each style and whose continuous parts represent vehicle motions. We then frame the problem of recognizing a vehicle's current driving style and maneuver as a belief state update on the hybrid POMDP. The driving style is assessed using multinomial logistic regression classification, while the maneuver is estimated using Bayesian filtering over a variant of probabilistic hybrid automata and a library of pre-learned motion primitive models. Multinomial logistic regression classification allows us to predict driving styles probabilistically using multiple driving features, and Bayesian filtering provides robust estimation results based on the prior information. Given the recognition results and the learned motion primitive models, we provide probabilistically sound predictions of the future maneuver and trajectory sequence of each agent vehicle. Finally, we compute safe motion plans of the ego vehicle in light of recognized agent vehicle driver styles, intended maneuvers, and future vehicle trajectories, by performing risk-bounded planning on the hybrid POMDP model. We demonstrate our system in a number of challenging simulated environments, including unprotected intersection left turns and lane changes with multiple dynamic vehicles. The demonstration shows that our intent recognition algorithms achieve an average driving style estimation accuracy of 89.89%, an average maneuver estimation accuracy of 98.9%, and an average trajectory prediction error of 2.12 meters. Furthermore, our maneuver planning system guarantees safety with respect to the safety constraint, while arriving at the goal 13.71% faster compared to a state-of-the-art planner without the intent recognition capability.
Download or read book Autonomous Driving written by Markus Maurer and published by Springer. This book was released on 2016-05-21 with total page 698 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book takes a look at fully automated, autonomous vehicles and discusses many open questions: How can autonomous vehicles be integrated into the current transportation system with diverse users and human drivers? Where do automated vehicles fall under current legal frameworks? What risks are associated with automation and how will society respond to these risks? How will the marketplace react to automated vehicles and what changes may be necessary for companies? Experts from Germany and the United States define key societal, engineering, and mobility issues related to the automation of vehicles. They discuss the decisions programmers of automated vehicles must make to enable vehicles to perceive their environment, interact with other road users, and choose actions that may have ethical consequences. The authors further identify expectations and concerns that will form the basis for individual and societal acceptance of autonomous driving. While the safety benefits of such vehicles are tremendous, the authors demonstrate that these benefits will only be achieved if vehicles have an appropriate safety concept at the heart of their design. Realizing the potential of automated vehicles to reorganize traffic and transform mobility of people and goods requires similar care in the design of vehicles and networks. By covering all of these topics, the book aims to provide a current, comprehensive, and scientifically sound treatment of the emerging field of “autonomous driving".
Download or read book Intent aware Collision Avoidance for Autonomous Marine Vehicles written by Joseph William Leavitt and published by . This book was released on 2017 with total page 278 pages. Available in PDF, EPUB and Kindle. Book excerpt: Applications of autonomous marine vehicles in dynamic and uncertain environments continuously grow as research unveils new enabling technology and academic, commercial, and government entities pursue new marine autonomy concepts. The safe operation of these vehicles in the marine domain, which is currently dominated by human-operated vehicles, demands compliance with collision avoidance protocol, namely the International Regulations for Preventing Collisions at Sea (COLREGS). Strict application of this protocol can lead to a highly constrained motion planning problem, in which it is difficult for a vehicle to identify a safe and efficient motion plan. This thesis proposes a multi-objective optimization-based method for COLREGS-compliant autonomous surface vehicle collision avoidance in which vehicles use shared intent information, in addition to vehicle state information, to identify safe and efficient collision avoidance maneuvers. The proposed method uses intent information to relax certain COLREGS-specified constraints with the goal of providing sufficient maneuvering flexibility to enable improvements in safety and efficiency over a non-intent-aware system. In order to arrive at an intent-aware solution, this thesis explores the concept of intent, including intent formulations for the marine domain, intent communications, and the application of intent to the COLREGS-compliant motion planning problem. Two types of intent information are specifically evaluated: COLREGS mode intent, in which the give-way vessel in an overtaking or crossing scenario communicates its intent to maneuver in a certain direction with respect the stand-on vessel, and discrete trajectory intent in which vehicles communicate projected future positions. Simulations and on-water experiments demonstrate the feasibility of the proposed intent-aware method, as well as improvements in performance, in terms of both vehicle safety and mission efficiency, over a non-intent-aware, COLREGS-compliant collision avoidance method.
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 Autonomous Vehicle Maneuvering at the Limit of Friction written by Victor Fors and published by Linköping University Electronic Press. This book was released on 2020-10-23 with total page 60 pages. Available in PDF, EPUB and Kindle. Book excerpt: Without a driver to fall back on, a fully self-driving car needs to be able to handle any situation it can encounter. With the perspective of future safety systems, this research studies autonomous maneuvering at the tire-road friction limit. In these situations, the dynamics is highly nonlinear, and the tire-road parameters are uncertain. To gain insights into the optimal behavior of autonomous safety-critical maneuvers, they are analyzed using optimal control. Since analytical solutions of the studied optimal control problems are intractable, they are solved numerically. An optimization formulation reveals how the optimal behavior is influenced by the total amount of braking. By studying how the optimal trajectory relates to the attainable forces throughout a maneuver, it is found that maximizing the force in a certain direction is important. This is like the analytical solutions obtained for friction-limited particle models in earlier research, and it is shown to result in vehicle behavior close to the optimal also for a more complex model. Based on the insights gained from the optimal behavior, controllers for autonomous safety maneuvers are developed. These controllers are based on using acceleration-vector references obtained from friction-limited particle models. Exploiting that the individual tire forces tend to be close to their friction limits, the desired tire slip angles are determined for a given acceleration-vector reference. This results in controllers capable of operating at the limit of friction at a low computational cost and reduces the number of vehicle parameters used. For straight-line braking, ABS can intervene to reduce the braking distance without prior information about the road friction. Inspired by this, a controller that uses the available actuation according to the least friction necessary to avoid a collision is developed, resulting in autonomous collision avoidance without any estimation of the tire–road friction. Investigating time-optimal lane changes, it is found that a simple friction-limited particle model is insufficient to determine the desired acceleration vector, but including a jerk limit to account for the yaw dynamics is sufficient. To enable a tradeoff between braking and avoidance with a more general obstacle representation, the acceleration-vector reference is computed in a receding-horizon framework. The controllers developed in this thesis show great promise with low computational cost and performance not far from that obtained offline by using numerical optimization when evaluated in high-fidelity simulation.
Download or read book Autonomous Vehicles for Safer Driving written by Ronald K Jurgen and published by SAE International. This book was released on 2013-04-16 with total page 142 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self-driving cars are no longer in the realm of science fiction, thanks to the integration of numerous automotive technologies that have matured over many years. Technologies such as adaptive cruise control, forward collision warning, lane departure warning, and V2V/V2I communications are being merged into one complex system. The papers in this compendium were carefully selected to bring the reader up to date on successful demonstrations of autonomous vehicles, ongoing projects, and what the future may hold for this technology. It is divided into three sections: overview, major design and test collaborations, and a sampling of autonomous vehicle research projects. The comprehensive overview paper covers the current state of autonomous vehicle research and development as well as obstacles to overcome and a possible roadmap for major new technology developments and collaborative relationships. The section on major design and test collaborations covers Sartre, DARPA contests, and the USDOT and the Crash Avoidance Metrics Partnership-Vehicle Safety Communications (CAMP-VSC2) Consortium. The final section presents seven SAE papers on significant recent and ongoing research by individual companies on a variety of approaches to autonomous vehicles. This book will be of interest to a wide range of readers: engineers at automakers and electronic component suppliers; software engineers; computer systems analysts and architects; academics and researchers within the electronics, computing, and automotive industries; legislators, managers, and other decision-makers in the government highway sector; traffic safety professionals; and insurance and legal practitioners.
Download or read book Optimal Braking Patterns and Forces in Autonomous Safety Critical Maneuvers written by Victor Fors and published by Linköping University Electronic Press. This book was released on 2019-05-02 with total page 19 pages. Available in PDF, EPUB and Kindle. Book excerpt: The trend of more advanced driver-assistance features and the development toward autonomous vehicles enable new possibilities in the area of active safety. With more information available in the vehicle about the surrounding traffic and the road ahead, there is the possibility of improved active-safety systems that make use of this information for stability control in safety-critical maneuvers. Such a system could adaptively make a trade-off between controlling the longitudinal, lateral, and rotational dynamics of the vehicle in such a way that the risk of collision is minimized. To support this development, the main aim of this licentiate thesis is to provide new insights into the optimal behavior for autonomous vehicles in safety-critical situations. The knowledge gained have the potential to be used in future vehicle control systems, which can perform maneuvers at-the-limit of vehicle capabilities. Stability control of a vehicle in autonomous safety-critical at-the-limit maneuvers is analyzed by the use of optimal control. Since analytical solutions of the studied optimal control problems are intractable, they are discretized and solved numerically. A formulation of an optimization criterion depending on a single interpolation parameter is introduced, which results in a continuous family of optimal coordinated steering and braking patterns. This formulation provides several new insights into the relation between different braking patterns for vehicles in at-the-limit maneuvers. The braking patterns bridge the gap between optimal lane-keeping control and optimal yaw control, and have the potential to be used for future active-safety systems that can adapt the level of braking to the situation at hand. A new illustration named attainable force volumes is introduced, which effectively shows how the trajectory of a vehicle maneuver relates to the attainable forces over the duration of the maneuver. It is shown that the optimal behavior develops on the boundary surface of the attainable force volume. Applied to lane-keeping control, this indicates a set of control principles similar to those analytically obtained for friction-limited particle models in earlier research, but is shown to result in vehicle behavior close to the globally optimal solution also for more complex models and scenarios.
Download or read book Artificial Intelligence for Autonomous Vehicles written by Sathiyaraj Rajendran and published by John Wiley & Sons. This book was released on 2024-02-27 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: With the advent of advanced technologies in AI, driverless vehicles have elevated curiosity among various sectors of society. The automotive industry is in a technological boom with autonomous vehicle concepts. Autonomous driving is one of the crucial application areas of Artificial Intelligence (AI). Autonomous vehicles are armed with sensors, radars, and cameras. This made driverless technology possible in many parts of the world. In short, our traditional vehicle driving may swing to driverless technology. Many researchers are trying to come out with novel AI algorithms that are capable of handling driverless technology. The current existing algorithms are not able to support and elevate the concept of autonomous vehicles. This addresses the necessity of novel methods and tools focused to design and develop frameworks for autonomous vehicles. There is a great demand for energy-efficient solutions for managing the data collected with the help of sensors. These operations are exclusively focused on non-traditional programming approaches and depend on machine learning techniques, which are part of AI. There are multiple issues that AI needs to resolve for us to achieve a reliable and safe driverless technology. The purpose of this book is to find effective solutions to make autonomous vehicles a reality, presenting their challenges and endeavors. The major contribution of this book is to provide a bundle of AI solutions for driverless technology that can offer a safe, clean, and more convenient riskless mode of transportation.
Download or read book Characterizing the Safety of Automated Vehicles written by Juan Pimentel and published by SAE International. This book was released on 2019-03-07 with total page 190 pages. Available in PDF, EPUB and Kindle. Book excerpt: Safety has been ranked as the number one concern for the acceptance and adoption of automated vehicles since safety has driven some of the most complex requirements in the development of self-driving vehicles. Recent fatal accidents involving self-driving vehicles have uncovered issues in the way some automated vehicle companies approach the design, testing, verification, and validation of their products. Traditionally, automotive safety follows functional safety concepts as detailed in the standard ISO 26262. However, automated driving safety goes beyond this standard and includes other safety concepts such as safety of the intended functionality (SOTIF) and multi-agent safety. Characterizing the Safety of Automated Vehicles addresses the concept of safety for self-driving vehicles through the inclusion of 10 recent and highly relevent SAE technical papers. Topics that these papers feature include functional safety, SOTIF, and multi-agent safety. As the first title in a series on automated vehicle safety, each will contain introductory content by the Editor with 10 SAE technical papers specifically chosen to illuminate the specific safety topic of that book.
Download or read book Autonomous Vehicles written by Kasap, Atilla and published by Edward Elgar Publishing. This book was released on 2022-10-11 with total page 215 pages. Available in PDF, EPUB and Kindle. Book excerpt: Delving deep into the emerging international and federal statutory and legislative developments surrounding Autonomous Vehicle (AV) technologies, Atilla Kasap assesses whether current motor vehicle regulations, liability law and the liability insurance system are fit for purpose today and in the future.
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 Safe and Robust Connected and Autonomous Vehicles in Mixed autonomy Traffic written by Rodolfo Valiente Romero and published by . This book was released on 2022 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous Vehicles (AVs) are expected to transform transportation in the near future. Although considerable progress has been made, widespread adoption of AVs will not become a reality until solutions are developed that enable AVs to co-exist with Human-driven Vehicles (HVs). There are still many challenges preventing Connected and Autonomous Vehicles (CAVs) from safely and smoothly navigating. We identify two major challenges in this direction. First, the communication system is not always reliable and suffers from noise and information loss. Second, AV navigation in the presence of HVs is challenging, as HVs continuously update their policies in response to AVs and the social preferences and behaviors of human drivers are unknown. Towards this end, we first propose solutions to improve situational awareness by enabling reliable and robust Cooperative Vehicle Safety (CVS) systems that mitigate the effect of information loss and propose a hybrid learning-based predictive modeling technique for CVS systems. Our prediction system is based on a Hybrid Gaussian Process (HGP) approach that provides accurate vehicle trajectory predictions to compensate for information loss. We use offline real-world data to learn a finite bank of driver models that represent the joint dynamics of the vehicle and the driver's behavior. AVs and HVs equipped with such reliable vehicular communication can coordinate, improving safety and efficiency. However, even in the presence of perfect communication, is still challenging for CAVs to navigate in the presence of humans. Therefore, we study the cooperative maneuver planning problem in a mixed autonomy environment. We frame the mixed-autonomy problem as a Multi-Agent Reinforcement Learning (MARL) problem and propose an approach that allows AVs to learn the decision-making of HVs implicitly from experience, account for all vehicles' interests, and safely adapt to other traffic situations. In contrast with existing works, we quantify AVs' social preferences and propose a distributed reward structure that introduces altruism into their decision-making process, allowing the altruistic AVs to learn to establish coalitions and influence the behavior of HVs. Inspired by humans, we provide our AVs with the capability of anticipating future states and leveraging prediction in the MARL decision-making framework. We propose the integration of two essential components of AVs, i.e, social navigation and prediction, and present a prediction-aware planning and social-aware optimization RL framework. Our proposed framework take advantage of a Hybrid Predictive Network (HPN) that anticipates future observations. The HPN is used in a multi-step prediction chain to compute a window of predicted future observations to be used by the Value Function Network (VFN). Finally, a safe VFN is trained to optimize a social utility using a sequence of previous and predicted observations, and a safety prioritizer is used to leverage the predictions to mask the unsafe actions, constraining the RL policy. The experiments on real-world and simulated data demonstrated the performance improvement of the proposed solutions in both safety and traffic-level metrics and validate the advantages and applicability of our solutions.
Download or read book Human Like Decision Making and Control for Autonomous Driving written by Peng Hang and published by CRC Press. This book was released on 2022-07-25 with total page 201 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book details cutting-edge research into human-like driving technology, utilising game theory to better suit a human and machine hybrid driving environment. Covering feature identification and modelling of human driving behaviours, the book explains how to design an algorithm for decision making and control of autonomous vehicles in complex scenarios. Beginning with a review of current research in the field, the book uses this as a springboard from which to present a new theory of human-like driving framework for autonomous vehicles. Chapters cover system models of decision making and control, driving safety, riding comfort and travel efficiency. Throughout the book, game theory is applied to human-like decision making, enabling the autonomous vehicle and the human driver interaction to be modelled using noncooperative game theory approach. It also uses game theory to model collaborative decision making between connected autonomous vehicles. This framework enables human-like decision making and control of autonomous vehicles, which leads to safer and more efficient driving in complicated traffic scenarios. The book will be of interest to students and professionals alike, in the field of automotive engineering, computer engineering and control engineering.
Download or read book Advanced Driver Intention Inference written by Yang Xing and published by Elsevier. This book was released on 2020-03-15 with total page 260 pages. Available in PDF, EPUB and Kindle. Book excerpt: Advanced Driver Intention Inference: Theory and Design describes one of the most important function for future ADAS, namely, the driver intention inference. The book contains the state-of-art knowledge on the construction of driver intention inference system, providing a better understanding on how the human driver intention mechanism will contribute to a more naturalistic on-board decision system for automated vehicles. Features examples of using machine learning/deep learning to build industry products Depicts future trends for driver behavior detection and driver intention inference Discuss traffic context perception techniques that predict driver intentions such as Lidar and GPS
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 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 Human Like Decision Making and Control for Autonomous Driving written by Peng Hang and published by CRC Press. This book was released on 2022-07-25 with total page 237 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book details cutting-edge research into human-like driving technology, utilising game theory to better suit a human and machine hybrid driving environment. Covering feature identification and modelling of human driving behaviours, the book explains how to design an algorithm for decision making and control of autonomous vehicles in complex scenarios. Beginning with a review of current research in the field, the book uses this as a springboard from which to present a new theory of human-like driving framework for autonomous vehicles. Chapters cover system models of decision making and control, driving safety, riding comfort and travel efficiency. Throughout the book, game theory is applied to human-like decision making, enabling the autonomous vehicle and the human driver interaction to be modelled using noncooperative game theory approach. It also uses game theory to model collaborative decision making between connected autonomous vehicles. This framework enables human-like decision making and control of autonomous vehicles, which leads to safer and more efficient driving in complicated traffic scenarios. The book will be of interest to students and professionals alike, in the field of automotive engineering, computer engineering and control engineering.
