This thesis explores the challenge of robustly handling dynamic obstacle uncertainty in autonomous driving systems. The path planning performance of Cornell's autonomous vehicle platform Skynet in the DARPA Urban Challenge (DUC) is analyzed and a new contingency planning formulation is presented that incorporates anticipated obstacle motions for improved collision avoidance capabilities. A discrete set of trajectory predictions is generated for each dynamic obstacle in the environment based on possible maneuvers the obstacle might make. A set of contingency paths is then optimized in real-time to accurately account for the mutually exclusive nature of these obstacle predictions. Computational scaling is addressed using a trajectory clustering algorithm that allows the contingency planner to plan a fixed number of paths regardless of the number of dynamic obstacles and possible obstacle goals in the environment. This contingency planning approach is evaluated using a series of human-inthe-loop experiments and simulations and is found to offer significant improvements in safety compared to the DUC planner and in performance compared to non-contingency planning approaches. A method for performing multi-step prediction over a two-stage Gaussian Process (GP) model is also presented. This prediction method is applied to a two-stage driver-vehicle obstacle model for the generation of high quality obstacle motion predictions using observed obstacle trajectories. An on-the-fly data selection technique is used to minimize computation when analytically evaluating higher order moments of the GP output. An adaptive Gaussian mixture model approach is also presented that allows this prediction technique to accurately predict the motion of highly nonlinear and multimodal systems.

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.

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.

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.

In recent years, the control of Connected and Automated Vehicles (CAVs) has attracted strong attention for various automotive applications. One of the important features demanded of CAVs is collision avoidance, whether it is a stationary or a moving obstacle. Due to complex traffic conditions and various vehicle dynamics, the collision avoidance system should ensure that the vehicle can avoid collision with other vehicles or obstacles in longitudinal and lateral directions simultaneously. The longitudinal collision avoidance controller can avoid or mitigate vehicle collision accidents effectively via Forward Collision Warning (FCW), Brake Assist System (BAS), and Autonomous Emergency Braking (AEB), which has been commercially applied in many new vehicles launched by automobile enterprises. But in lateral motion direction, it is necessary to determine a flexible collision avoidance path in real time in case of detecting any obstacle. Then, a path-tracking algorithm is designed to assure that the vehicle will follow the predetermined path precisely, while guaranteeing certain comfort and vehicle stability over a wide range of velocities. In recent years, the rapid development of sensor, control, and communication technology has brought both possibilities and challenges to the improvement of vehicle collision avoidance capability, so collision avoidance system still needs to be further studied based on the emerging technologies. In this book, we provide a comprehensive overview of the current collision avoidance strategies for traditional vehicles and CAVs. First, the book introduces some emergency path planning methods that can be applied in global route design and local path generation situations which are the most common scenarios in driving. A comparison is made in the path-planning problem in both timing and performance between the conventional algorithms and emergency methods. In addition, this book introduces and designs an up-to-date path-planning method based on artificial potential field methods for collision avoidance, and verifies the effectiveness of this method in complex road environment. Next, in order to accurately track the predetermined path for collision avoidance, traditional control methods, humanlike control strategies, and intelligent approaches are discussed to solve the path-tracking problem and ensure the vehicle successfully avoids the collisions. In addition, this book designs and applies robust control to solve the path-tracking problem and verify its tracking effect in different scenarios. Finally, this book introduces the basic principles and test methods of AEB system for collision avoidance of a single vehicle. Meanwhile, by taking advantage of data sharing between vehicles based on V2X (vehicle-to-vehicle or vehicle-to-infrastructure) communication, pile-up accidents in longitudinal direction are effectively avoided through cooperative motion control of multiple vehicles.

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.

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 ).