Cyber-physical systems (CPS) encompass the next generation of computerized control for countless aspects of the physical world and interactions thereof. The typical engineering process for CPS reuses existing designs, models, components, and software from one version to the next. For example, in automotive engineering, it is common to reuse significant portions of existing model-year vehicle designs when developing the next model-year vehicle, and such practices are common across CPS industries, from aerospace to biomedical. While reuse drastically enhances efficiency and productivity, it leads to the possibility of introducing unintended mismatches between subcomponents' specifications. For example, a 2011 US National Highway Traffic Safety Administration (NHTSA) recall of over 1.5 million model-year 2005-2010 vehicles was due to the upgrade of a physical transmission component that was not appropriately addressed in software. A mismatch between cyber and physical specifications may occur when a software or hardware upgrade (in effect, a cyber or physical specification change) is not addressed by an update (in effect, a matching specification change) in the other domain. This research will develop new techniques and software tools to detect automatically if cyber-physical specification mismatches exist, and then mitigate the effects of such mismatches at runtime, with the overall goal to yield more reliable and safer CPS upon which society increasingly depends. The detection and mitigation methods developed will be evaluated in an energy CPS testbed. While the evaluation testbed is in the energy domain, the methods are applicable to other CPS domains such as automotive, aerospace, and biomedical. The educational goals will bridge gaps between computer science and electrical engineering, preparing a diverse set of next-generation CPS engineers by developing education platforms to enhance CPS engineering design and verification skills. The proposed research is to develop new techniques and tools to automatically identify and mitigate the effects of cyber-physical specification mismatches. There are three major research objectives. The first objective is to identify cyber-physical specification mismatches. To identify mismatches, a detection problem will be formalized using the framework of hybrid input/output automata (HIOA). Offline algorithms will be designed to find candidate specifications from models and implementations using static and dynamic analyses, and then identify candidate mismatches. The second objective is to monitor and assure safe CPS upgrades. As modern CPS designs are complex, it may be infeasible to determine all specifications and mismatches between all subcomponents at design time. Runtime monitoring and verification methods will be developed for inferred specifications to detect mismatches at runtime. When they are identified, a runtime assurance framework building on supervisory control and the Simplex architecture will assure safe CPS runtime operation. The third objective is to evaluate safe CPS upgrades in an example CPS. The results of the other objectives and their ability to ensure safe CPS upgrades will be evaluated in an energy CPS testbed, namely an AC electrical distribution microgrid that interfaces DC-producing renewables like photovoltaics to AC.

Recent progress in autonomous and connected vehicle technologies coupled with Federal and State initiatives to facilitate their widespread use provide significant opportunities in enhancing mobility and safety for highway transportation. This project develops signalized intersection control strategies and other enabling sensor mechanisms for jointly optimizing vehicle trajectories and signal control by taking advantage of existing advanced technologies (connected vehicles and vehicle to infrastructure communications, sensors, autonomous vehicle technologies, etc.) Traffic signal control is a critical component of the existing transportation infrastructure and it has a significant impact on transportation system efficiency, as well as energy consumption and environmental impacts. In addition to advanced vehicle technologies, the strategies developed consider the presence of conventional vehicles in the traffic stream to facilitate transition to these new strategies in a mixed vehicle environment. The project also develops and uses simulation tools to evaluate these strategies as well as to provide tools that can be used in practice to consider the impacts of automated and connected vehicles in arterial networks. The project involves two industry partners (ISS and Econolite) to help facilitate new product development in anticipation of increased market penetration of connected and autonomous vehicles. The approach will be tested through simulation at University of Florida, through field tests at the Turner Fairbank Highway Research Center (TFHRC) and through the control algorithms that also will be deployed and tested in the field. The project will support multiple graduate students and will support creation of on-line classes. The project is at the intersection of several different disciplines (optimization, sensors, automated vehicles, transportation engineering) required to produce a real-time engineered system that depends on the seamless integration of several components: sensor functionality, connected and autonomous vehicle information communication, signal control optimization strategy, missing and erroneous information, etc. The project develops and implements optimization processes and strategies considering a seamless fusion of multiple data sources, as well as a mixed vehicle stream (autonomous, connected, and conventional vehicles) under real-world conditions of uncertain and missing data. Since trajectories for connected and conventional vehicles cannot be optimized or guaranteed, the project examines the impacts of the presence of automated vehicles on the following vehicles in a queue. The project also integrates advanced sensing technology needed to control a mixed vehicle stream, as well as address malfunctioning communications in connected and autonomous vehicles.

Today's automobiles are increasingly autonomous. The latest Mercedes S-class sedan applies corrective action when its driver strays out of lane or tailgates too closely. Semi-autonomy will soon yield to full autonomy. Nissan has promised a line of self-driving cars by 2020. Maritime craft are likewise moving from rudimentary autopilots to full autonomy, and autonomous aerial vehicles will doubtless play a significant role in the future economy. Current versions of these vehicles are cocooned in an array of sensors, but neither the sensors nor the timing, navigation, and collision avoidance algorithms they feed have been designed for security against malicious attacks. Radar and acoustic sensors transmit predictable, uncoded signals; vehicle-to-vehicle communication protocols are either unauthenticated or critically dependent on insecure civil GPS signals (or both); and vehicle state estimators are designed for robustness but not security. These vulnerabilities are not merely conceptual: GPS spoofing attacks have been demonstrated against a drone and an ocean vessel, causing the drone to crash and the vessel to veer off course; likewise, it appears possible to cause road accidents by fooling a car's radar sensor into thinking a crash is imminent, thus triggering automatic braking. This proposal seeks funding to fix these vulnerabilities by developing sensors and high-level decision-making algorithms that are hardened against such so-called field attacks. The goal of secure control systems is to survive and operate safely despite sensor measurements or control commands being compromised. This proposal focuses on an emergent category of cyber-physical attack that has seen little scrutiny in the secure control literature. Like cyber attacks, these attacks are hard to detect and can be executed from a distance, but unlike cyber attacks, they are effective even against control systems whose software, data, and communications networks are secure, and so can be considered a more menacing long-term threat. These are attacks on the physical fields such as electromagnetic, magnetic, acoustic, etc. measured by system sensors. As specialized sensor attacks, field attacks seek to compromise a system's perception of reality non-invasively from without, not from within. We emphasize field attacks against navigation, collision avoidance, and synchronization sensors, as these are of special importance to the rise of autonomous vehicles and the smart grid. This proposal's goal is to develop a coherent analytical foundation for secure perception in the presence of field attacks and to develop a suite of algorithms and tools to detect such attacks. A key insight behind this proposal's approach is that the physics of field attacks impose fundamental difficulties on the attacker that can be exploited and magnified to enable attack detection. This work will progressively build security into navigation, collision avoidance, and timing perception from the physical sensory layer to the top-level state estimation algorithms. The outcome of this work will be smarter, more skeptical sensor systems for autonomous vehicles and other autonomous systems.

The automotive industry finds itself at a cross-roads. Current advances in MEMS sensor technology, the emergence of embedded control software, the rapid progress in computer technology, digital image processing, machine learning and control algorithms, along with an ever increasing investment in vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) technologies, are about to revolutionize the way we use vehicles and commute in everyday life. Automotive active safety systems, in particular, have been used with enormous success in the past 50 years and have helped keep traffic accidents in check. Still, more than 30,000 deaths and 2,000,000 injuries occur each year in the US alone, and many more worldwide. The impact of traffic accidents on the economy is estimated to be as high as $300B/yr in the US alone. Further improvement in terms of driving safety (and comfort) necessitates that the next generation of active safety systems are more proactive (as opposed to reactive) and can comprehend and interpret driver intent. Future active safety systems will have to account for the diversity of drivers' skills, the behavior of drivers in traffic, and the overall traffic conditions. This research aims at improving the current capabilities of automotive active safety control systems (ASCS) by taking into account the interactions between the driver, the vehicle, the ASCS and the environment. Beyond solving a fundamental problem in automotive industry, this research will have ramifications in other cyber-physical domains, where humans manually control vehicles or equipment including: flying, operation of heavy machinery, mining, tele-robotics, and robotic medicine. Making autonomous/automated systems that feel and behave "naturally" to human operators is not always easy. As these systems and machines participate more in everyday interactions with humans, the need to make them operate in a predictable manner is more urgent than ever. To achieve the goals of the proposed research, this project will use the estimation of the driver's cognitive state to adapt the ASCS accordingly, in order to achieve a seamless operation with the driver. Specifically, new methodologies will be developed to infer long-term and short-term behavior of drivers via the use of Bayesian networks and neuromorphic algorithms to estimate the driver's skills and current state of attention from eye movement data, together with dynamic motion cues obtained from steering and pedal inputs. This information will be injected into the ASCS operation in order to enhance its performance by taking advantage of recent results from the theory of adaptive and real-time, model-predictive optimal control. The correct level of autonomy and workload distribution between the driver and ASCS will ensure that no conflicts arise between the driver and the control system, and the safety and passenger comfort are not compromised. A comprehensive plan will be used to test and validate the developed theory by collecting measurements from several human subjects while operating a virtual reality-driving simulator.

This project addresses urgent challenges in high confidence validation and verification of automotive vehicles due to on-going and anticipated introduction of advanced, connected and autonomous vehicles into mass production. Since such vehicles operate across both physical and cyber domains, faults can occur in traditional physical components, in cyber components (i.e., algorithms, processors, networks, etc.), or in both. Thus, advanced vehicles need to be tested for both physical and cyber-related fault conditions. The goal of this project is to develop theory, methods, and novel tools for generating and optimizing test trajectories and data inputs that can uncover both physical and cyber faults of future automotive vehicles. The level of vehicle reliability and safety achieved for current vehicles is remarkable considering their mass production, low cost, and wide range of operating conditions. If successful, the research advances made in this project will enable achieving similar levels of reliability and safety for future vehicles relying on advanced driver assistance technologies, connectivity and autonomy. The project will advance the field of cyber-physical systems, in general, and their lifecycle management, in particular. The validation and verification theory and methodology for cyberphysical systems will be expanded for uncovering anomalies and faults, especially using comprehensive case-based and optimization-based techniques for test scenario generation. The theoretical advances and case studies will contribute to the state-of-the-art in optimal control theory, game theory, information theory, data collection and processing, autonomous and connected vehicles, and automotive control. Sampling-based vehicle data acquisition and vehicle-aware data management strategies will be developed which can be applied more broadly, e.g., to cloud-based vehicle prognostics / conditional maintenance and mobile health-monitoring devices. Finally, approaches for efficient on-board data collection and aggregation will be implemented in a Cyber-physical system (CPS) Black Box prototype. The development of a vehicle-aware data management system (VDMS) will be pursued, leading to optimized use of data mining and compression inside the CPS Black Box to aggressively reduce the communication and computational costs. Synergistically with theoretical and methodological advances, automotive case studies will be undertaken with both realistic simulations and real experiments in collaboration with an industrial partner (AVL).