Before autonomous vehicles are able to be widely deployed, a number of security and algorithmic challenges must be addressed. Current autonomous vehicles that provide motion safety guarantees exhibit excessively conservative driving behavior when operating in road environments containing highly dynamic obstacles. In this thesis we present a contingency-based motion planning framework for autonomous road vehicles. Probabilistic state predictions are generated for each discrete action of nearby obstacle vehicles, and multiple contingency trajectories are planned such that safe execution is possible under each possible discrete action. An online estimation algorithm is used to infer the discrete obstacle action from sensor observations and inform execution-time contingency selection. We present a fast upper bound on a metric of distinguishability that approximates the predicted probability of correctly identifying the discrete action of an obstacle from a set of possible hypotheses. The metric is used to optimize expected execution cost and safety of a set of contingency trajectories. Simulated experiments show that the proposed planning framework produces trajectories with a lower cost and stronger safety guarantees than that of prior work, and this performance improvement persists across a range of vehicle and obstacle initial conditions. Additionally, a prototype system architecture for a verifiably secure autonomous vehicle is presented. The system architecture is designed to enforce separation of trusted and untrusted information flows. A map verification algorithm is used to verify external data coming from an untrusted source. Motion planning and map verification software components are developed with existing tools that enforce information flow control at the language level. The architecture is implemented on a mobile robotic testbed and experiments are performed to simulate a remote attack scenario. Experimental results show that the architecture is resistant to malicious external data, and can operate safely even when external communications are compromised. Analogies are drawn between the prototype architecture and hardware and software components on real-world autonomous vehicles.

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 ).

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".

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.

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.

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

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.