Download or read book A Driver vehicle Model for Impaired Motorists and Strategies for Planning Autonomous Vehicles written by Thanh Phuc Le and published by . This book was released on 2013 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:

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 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 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 Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions written by Harald Waschl and published by Springer. This book was released on 2018-06-28 with total page 235 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book describes different methods that are relevant to the development and testing of control algorithms for advanced driver assistance systems (ADAS) and automated driving functions (ADF). These control algorithms need to respond safely, reliably and optimally in varying operating conditions. Also, vehicles have to comply with safety and emission legislation. The text describes how such control algorithms can be developed, tested and verified for use in real-world driving situations. Owing to the complex interaction of vehicles with the environment and different traffic participants, an almost infinite number of possible scenarios and situations that need to be considered may exist. The book explains new methods to address this complexity, with reference to human interaction modelling, various theoretical approaches to the definition of real-world scenarios, and with practically-oriented examples and contributions, to ensure efficient development and testing of ADAS and ADF. Control Strategies for Advanced Driver Assistance Systems and Autonomous Driving Functions is a collection of articles by international experts in the field representing theoretical and application-based points of view. As such, the methods and examples demonstrated in the book will be a valuable source of information for academic and industrial researchers, as well as for automotive companies and suppliers.

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 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 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 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 Theories and Practices of Self Driving Vehicles written by Qingguo Zhou and published by Elsevier. This book was released on 2022-07-03 with total page 346 pages. Available in PDF, EPUB and Kindle. Book excerpt: Self-driving vehicles are a rapidly growing area of research and expertise. Theories and Practice of Self-Driving Vehicles presents a comprehensive introduction to the technology of self driving vehicles across the three domains of perception, planning and control. The title systematically introduces vehicle systems from principles to practice, including basic knowledge of ROS programming, machine and deep learning, as well as basic modules such as environmental perception and sensor fusion. The book introduces advanced control algorithms as well as important areas of new research. This title offers engineers, technicians and students an accessible handbook to the entire stack of technology in a self-driving vehicle. Theories and Practice of Self-Driving Vehicles presents an introduction to self-driving vehicle technology from principles to practice. Ten chapters cover the full stack of driverless technology for a self-driving vehicle. Written by two authors experienced in both industry and research, this book offers an accessible and systematic introduction to self-driving vehicle technology. Provides a comprehensive introduction to the technology stack of a self-driving vehicle Covers the three domains of perception, planning and control Offers foundational theory and best practices Introduces advanced control algorithms and high-potential areas of new research Gives engineers, technicians and students an accessible handbook to self-driving vehicle technology and applications

Download or read book Nonlinear Model Predictive Control written by Frank Allgöwer and published by Birkhäuser. This book was released on 2012-12-06 with total page 463 pages. Available in PDF, EPUB and Kindle. Book excerpt: During the past decade model predictive control (MPC), also referred to as receding horizon control or moving horizon control, has become the preferred control strategy for quite a number of industrial processes. There have been many significant advances in this area over the past years, one of the most important ones being its extension to nonlinear systems. This book gives an up-to-date assessment of the current state of the art in the new field of nonlinear model predictive control (NMPC). The main topic areas that appear to be of central importance for NMPC are covered, namely receding horizon control theory, modeling for NMPC, computational aspects of on-line optimization and application issues. The book consists of selected papers presented at the International Symposium on Nonlinear Model Predictive Control – Assessment and Future Directions, which took place from June 3 to 5, 1998, in Ascona, Switzerland. The book is geared towards researchers and practitioners in the area of control engineering and control theory. It is also suited for postgraduate students as the book contains several overview articles that give a tutorial introduction into the various aspects of nonlinear model predictive control, including systems theory, computations, modeling and applications.

Download or read book Leveraging Learning for Vehicle Control at the Limits of Handling written by Nathan Spielberg and published by . This book was released on 2021 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt: Autonomous vehicles have the capability to revolutionize human mobility and vehicle safety. To prove safe, they must be capable of navigating their environment as well as or better than the best human drivers. The best human drivers can leverage the limits of a vehicle's capabilities to avoid collisions and stabilize the vehicle while sliding on pavement, ice, and snow. Automated vehicles should similarly be capable of navigating safety-critical scenarios when friction is limited, and one large advantage they hold over human drivers is the amount of data they can generate. With self-driving vehicles in the San Francisco Bay Area collecting almost two human lifetimes worth of data just during 2020, this abundance of data holds the key to improving vehicle safety. This dissertation examines how data generated by self-driving vehicles can be used to learn control policies and models to improve vehicle control near the limits of handling. As data collection and vehicle operation near the limits can be expensive, this work uses skilled humans as an inspiration for learning policies because of their incredible data efficiency. This ability is clearly demonstrated in racing where skilled human drivers act to improve their performance after each lap by shifting their braking point to maximize corner entry speed and minimize lap time. Starting from a benchmark feedforward and feedback control architecture already comparable to skilled human drivers, this work directly learns feedforward policies to improve vehicle performance over time. By using an approximate physics-based model of the vehicle, recorded lap data, and the gradient of lap time, this approach improves lap time by almost seven tenths of a second on a nineteen second lap over an initial optimization-based approach for racing. Additionally, this approach generalizes to low-friction driving. While model-based policy search shows improvement over a solely optimization-based approach, model-based policy search is ultimately limited by the vehicle model used. Physics-based models are useful for interpretability and understanding, but fail to make use of the abundance of data self-driving vehicles generate and often do not capture high-order or complex-to-model effects. Additionally, to operate at a vehicle's true limits, precise identification of the vehicle's road-tire friction coefficient is required which is a very difficult task. To overcome the drawbacks of physics-based models, this thesis next examines the ability of neural networks to use vehicle data to learn vehicle dynamics models. These models are capable of not only modeling higher-order and complex effects, but also vehicle motion on high- and low-friction surfaces. Furthermore, these models do so while retaining comparable control performance near the limits to a benchmark physics-based feedforward and feedback control architecture. Though this control approach shows promise in operating near the limits, feedforward and feedback control is ultimately limited in its ability to trade of small errors in the short term to prevent larger errors in the future. Additionally, actuator and road boundary constraints play an increasingly important role in safety as the vehicle nears the limits. To deal with these limitations, this work presents neural network model predictive control for automated driving near the limits of friction. Neural network model predictive control not only leverages the neural network model's ability to predict dynamics on high- and low-friction test tracks, but also retains comparable or better performance to MPC using a well-tuned physics model optimized to the corresponding high- or low-friction test track. While neural network MPC shows improved performance over physics-based MPC when operating near the limits, MPC leverages its dynamics model with complete certainty. These effects can lead to MPC overleveraging its dynamics model, which in the presence of model mismatch can lead to poor controller performance. Additionally, when using neural network models in MPC, the network predicts vehicle motion with complete certainty regardless of the presence or absence of training data in the corresponding modeled region. To mitigate this issue, this work presents an approach which leverages a neural network model to learn the uncertainty in the underlying dynamics model used in MPC. By learning the uncertainty in MPC's dynamics model, the vehicle can take actions to avoid highly uncertain regions of operation while still attempting to optimize the original MPC cost function. The insights from this work can be used to design automated vehicles capable of leveraging vehicle data to more effectively operate near the limits of handling.

Download or read book Vehicles Drivers and Safety written by John Hansen and published by Walter de Gruyter GmbH & Co KG. This book was released on 2020-05-05 with total page 274 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book presents works from world-class experts from academia, industry, and national agencies representing countries from across the world focused on automotive fields for in-vehicle signal processing and safety. These include cutting-edge studies on safety, driver behavior, infrastructure, and human-to-vehicle interfaces. Vehicle Systems, Driver Modeling and Safety is appropriate for researchers, engineers, and professionals working in signal processing for vehicle systems, next generation system design from driver-assisted through fully autonomous vehicles.

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

Download or read book 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 Decision Making Techniques for Autonomous Vehicles written by Jorge Villagra and published by Elsevier. This book was released on 2023-01-15 with total page 424 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 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".