As self-driving cars are introduced into road networks, the overall safety and efficiency of the resulting traffic system must be established and guaranteed. Numerous critical software-related recalls of existing automotive systems indicate that current design practices are not yet up to this challenge. This project seeks to address this problem, by developing methods to analyze and coordinate networks of fully and partially self-driving vehicles that interact with conventional human-driven vehicles on roads. The outcomes of the research are expected to also contribute to the safety of other cyber-physical systems with scalable configurable hierarchical structures, by developing a mathematical framework and corresponding software tools that analyze the safety and reliability of a class of systems that combine physical, mechanical and biological components with purely computational ones. The project research spans four technical areas: autonomous and human-controlled collaborative driving; scheduling computations over heterogeneous distributed computing systems; security and trust in V2X (Vehicle-to-Vehicle and Vehicle-to-Infrastructure) networks; and Verification & Validation of V2X systems through semi-virtual environments and scenarios. The integrating aspect of this research is the development of a distributed system calculus for Cyber-Physical Systems (CPS) that enables modeling, simulation and analysis of collaborative vehicular systems. The development of a comprehensive framework to model, analyze and test reconfiguration, hierarchical control, security and trust differentiates this research from previous attempts to address the same problem. Educational and outreach activities include integration of project research in undergraduate and graduate courses, and a summer camp at Ohio State University for high-school students through the Women in Engineering program.

The goal of this research is to develop fundamental theory, efficient algorithms, and realistic experiments for the analysis and design of safety-critical cyber-physical transportation systems with human operators. The research focuses on preventing crashes between automobiles at road intersections, since these account for about 40% of overall vehicle crashes. Specifically, the main objective of this work is to design provably safe driver-assist systems that understand driver?s intentions and provide warnings/overrides to prevent collisions. In order to pursue this goal, hybrid automata models for the driver-vehicles-intersection system, incorporating driver behavior and performance as an integral part, are derived from human-factors experiments. A partial order of these hybrid automata models is constructed, according to confidence levels on the model parameters. The driver-assist design problem is then formulated as a set of partially ordered hybrid differential games with imperfect information, in which games are ordered according to parameter confidence levels. The resulting designs are validated experimentally in a driving simulator and in large-scale computer simulations. This research leverages the potential of embedded control and communication technologies to prevent crashes at traffic intersections, by enabling networks of smart vehicles to cooperate with each other, with the surrounding infrastructure, and with their drivers to make transportation safer, more enjoyable, and more efficient. The work is based on a collaboration among researchers in formal methods, autonomous control, and human factors who are studying realistic and provably correct warning/override algorithms that can be readily transitioned to production vehicles.

Until now, the cyber component of automobiles has consisted of control algorithms and associated software for vehicular subsystems designed to achieve one or more performance, efficiency, reliability, comfort, or safety goals, primarily based on short-term intrinsic vehicle sensor data. However, there exist many extrinsic factors that can affect the degree to which these goals can be achieved. These factors can be determined from: longer-term traces of in-built sensor data that can be abstracted as triplines, socialized versions of these that are shared amongst vehicle users, and online databases. These three sources of information collectively constitute the automotive infoverse. This project harnesses this automotive infoverse to achieve these goals through high-confidence vehicle tuning and driver feedback decisions. Specifically, the project develops software called Headlight that permits the rapid development of apps that use the infoverse to achieve one or more goals. Advisory apps can provide feedback to the driver in order to ensure better fuel efficiency, while auto-tuning goals can set car parameters to promote safety. Allowing vehicles and such apps to share vehicle data with others and to use extrinsic information results in novel information processing, assurance, and privacy challenges. The project develops methods, algorithms and models to address these challenges. Broader Impact - This project can have significant societal impact by reducing carbon emissions and improving vehicular safety, can spur innovation in tuning methods and encourage researchers to experiment with this class of cyber-physical systems. The active participation of General Motors will strongly facilitate technology transfer. The program has outreach through internships, course material, high school and undergraduate involvement, and through creating an open infrastructure usable by diverse developers.

The objective of this project is to research tools to manage uncertainty in the design and certification process of safety-critical aviation systems. The research focuses on three innovative ideas to support this objective. First, probabilistic techniques will be introduced to specify system-level requirements and bound the performance of dynamical components. These will reduce the design costs associated with complex aviation systems consisting of tightly integrated components produced by many independent engineering organizations. Second, a framework will be created for developing software components that use probabilistic execution to model and manage the risk of software failure. These techniques will make software more robust, lower the cost of validating code changes, and allow software quality to be integrated smoothly into overall system-level analysis. Third, techniques from Extreme Value Theory will be applied to develop adaptive verification and validation procedures. This will enable early introduction of new and advanced aviation systems. These systems will initially have restricted capabilities, but these restrictions will be gradually relaxed as justified by continual logging of data from in-service products. The three main research aims will lead to a significant reduction in the costs and time required for fielding new aviation systems. This will enable, for example, the safe and rapid implementation of next generation air traffic control systems that have the potential of tripling airspace capacity with no reduction in safety. The proposed methods are also applicable to other complex systems including smart power grids and automated highways. Integrated into the research is an education plan for developing a highly skilled workforce capable of designing safety critical systems. This plan centers around two main activities: (a) creation of undergraduate labs focusing on safety-critical systems, and (b) integration of safety-critical concepts into a national robotic snowplow competition. These activities will provide inspirational, real-world applications to motivate student learning.