Human-driven vehicles (HDVs) and automated vehicles (AVs) of all levels (Level 1-5, AVs1-5) may share the highways in the long and foreseeable future. The increasing vehicle autonomy heterogeneity and diversity may jeopardize the safe and harmonious interaction among such vehicles with mixed autonomy on highways and pose a threat to the safety of all vehicles. This may exacerbate an already growing and alarming national concern on traffic safety. This project aims to advance the state of the art in the Cyber-Physical Systems (CPS) research areas of Autonomy, Safety, and Transportation by ushering in a new CPS paradigm of harmonious and safe integration of highway vehicles with heterogeneous, varying, and mixed human / machine autonomy. Through collaborative research, the project may create new methods and tools to enhance the highway driving safety of heterogeneous vehicles. The outcomes of this work may also be extended to advance other CPS in manufacturing, warehousing, and healthcare applications where interaction among humans and heterogeneous autonomous robots is pervasive and safe coordination among them is critical.
Autonomous vehicles (AVs) are revolutionizing the transportation ecosystem and are expected to become a critical part of our society. AVs are equipped with many electronic devices, including various sensors, electronic control units (ECUs), internal control networks, as well as capabilities in artificial intelligence, computing, storage, and communication. Although the automotive industry, as well as the public, are optimistic that an AV can perform many basic functions on par with human drivers, few are confident about the security and safety of AVs, especially when AVs are highly vulnerable to potential attacks from cyberspace, as demonstrated in recent series of car hacking incidents. In this project, a team of researchers from Virginia Tech aims to address some of the fundamental security and safety challenges for AVs. The research team follows a novel defense-in-depth approach that combines three layers of defense against attacks on software systems, in-vehicle networks, and safety-critical ECUs in an AV. Each layer can be designed and deployed independently from the other layers and when working jointly, they can not only effectively thwart most system and network attacks but also provide fail-operational protection against both known and potentially unforeseen cyberattacks.
Autonomous driving is on the verge of revolutionizing the transportation system and significantly improving the well-being of people. An autonomous vehicle relies on multiple sensors and AI algorithms to facilitate sensing and perception for navigating the world. As the automotive industry primarily focuses on increasing autonomy levels and enhancing perception performance in mainly benign environments, the security and safety of perception technologies against physical attacks have yet to be thoroughly investigated. Specifically, adversaries creating physical-world perceptual illusions may pose a significant threat to the sensing and learning systems of autonomous vehicles, potentially undermining trust in these systems. This research project aims to deepen our understanding of the security and safety risks under physical attacks. The project endeavors to bolster sensing and learning resilience in autonomous driving against malicious perceptual illusion attacks. The success of the project will significantly advance the security and safety of autonomous driving in the face of emerging physical-world threats, paving the way for the safe deployment of autonomous vehicles in next-generation transportation systems.
In the current state-of-the-art machine learning based real-time control of large complex networks such as electric power systems is largely bottlenecked by the curse of dimensionality. Even the simplest control designs demand numerical complexity to accomplish. The problem becomes even more challenging when the network model is unknown, due to which an additional learning time needs to be accommodated. This project will take a new stance for solving this problem, and develop a suite of hierarchical or nested machine learning-based schemes that take advantage of various forms of physical redundancies in the network dynamics to learn only the most important traits of its behavior instead of wasting time in learning minor traits that may improve the closed-loop performance only by a small amount. This selective learning approach will reduce learning time by several orders of magnitude, making real-time control more tractable and more implementable. Products will include numerical algorithms that are applicable across a wide range of machine learning based control. In terms of societal impact, the project is strongly envisioned to bring control theorists closer to data scientists so that these two research communities can work together, and answer important questions such as: why the value of big data has traditionally been under-utilized in controls, what new dimensions can control theory gain from machine learning and vice versa, and what primary analytical and experimental tools are needed to make this marriage more successful. The research will also support the cross-disciplinary development of a diverse cohort of PhD and undergraduate students, and the development of a graduate-level course on the applications of machine learning in control.
This project investigates new reinforcement learning (RL) approaches for cyber-physical autonomy to bridge the gap between current intelligent systems and human-level intelligence. The nature of many cyber-physical systems (CPS) is distributed, heterogeneous, and high-dimensional, making the hand-coded functions and task-specific information hard to design in the learning scheme. Large amount of training data is often required for achieving the desired performance, however this limits the generalization to other tasks. Hence, this project is to explore the new RL strategies to enable CPS with the capabilities of autonomous learning and generalization to rapidly adapt in unknown situations that were not assumed in the design phase. The results are expected to transform how agents interact in high-dimensional and heterogeneous environment, and therefore could potentially provide in-depth findings for exploring creativity in frontier Artificial Intelligence techniques.
Unmanned Aircraft Systems (UASs), or drones, have tremendous scientific, military, and civilian potential for data collection, monitoring, and interacting with the environment. These activities require high levels of reasoning, perception, and control, and the flexibility to adapt to changing environments. However, like other automated agents, UAS don't possess the ability to refocus their attention or reallocate resources to adapt to new scenarios and adjust performance. This project will provide a new class of control and planning algorithms capable of adjusting performance as computing resources are continually reallocated, such as when transitioning from waypoint navigation to environmental sample collection. A computing framework to make use of freed resources will be developed allowing autonomous agents to focus attention where it is needed, for example, away from navigation and to perception. Together, these will provide a blueprint for making use of similar algorithms with adjustable performance (e.g., anytime algorithms) which can be adapted to other robotics platforms, as well as water, space, or ground vehicles.
These technology innovations will improve the ability of agents to learn more, perceive more accurately, collect better data, and respond more appropriately to changing environments and mission objectives. Specific to UAS, this project will help maintain U.S. air superiority goals through agile planning, targeted and persistent Intelligence, Surveillance, and Reconnaissance (ISR), and flexibility and adaptability. The project goals are coupled with outreach and educational activities focused on increasing the understanding of rural populations of the value of investing in scientific and technological research. The educational efforts, targeted at K-12, undergraduate, graduate, and adult engagement are designed to dramatically increase the CPS educational pipeline in the Midwest.
The project focuses on achieving its goals by providing a complete framework for a class of performance-adjustable, resource-aware algorithms called "co-regulation." First, a new modeling and analysis framework, Co-regulated Hybrid Systems (CHS), will provide a mathematical foundation for optimal control, control synthesis, and performance analysis for systems that can dynamically vary sampling rate and other computational resources to adjust performance. Next, using the CHS formalism, computational workload is predicted forming the basis for a novel Co-regulated Real-Time Kernel (CRTK) to dynamically reallocate computing resources while guaranteeing real-time schedule feasibility. Finally, a co-regulated Markov Decision Process (MDP) forms the planning portion of a resource-aware autopilot for adaptable UAS. The system will be implemented in a multi-agent, rainforest monitoring scenario requiring periods of surveillance, sampling of plants, and emplacement of sensors.
Human-driven vehicles (HDVs) and automated vehicles (AVs) of all levels (Level 1-5, AVs1-5) may share the highways in the long and foreseeable future. The increasing vehicle autonomy heterogeneity and diversity may jeopardize the safe and harmonious interaction among such vehicles with mixed autonomy on highways and pose a threat to the safety of all vehicles. This may exacerbate an already growing and alarming national concern on traffic safety. This project aims to advance the state of the art in the Cyber-Physical Systems (CPS) research areas of Autonomy, Safety, and Transportation by ushering in a new CPS paradigm of harmonious and safe integration of highway vehicles with heterogeneous, varying, and mixed human / machine autonomy. Through collaborative research, the project may create new methods and tools to enhance the highway driving safety of heterogeneous vehicles. The outcomes of this work may also be extended to advance other CPS in manufacturing, warehousing, and healthcare applications where interaction among humans and heterogeneous autonomous robots is pervasive and safe coordination among them is critical.
The project seeks to address the emerging challenges associated with vehicles of heterogeneous autonomy in highway transportation by creating a universal framework that can augment AVs1-5 systems to enable safe and harmonious integration of vehicles in highway traffic. The research team will use an interdisciplinary research approach to understand driving behaviors and assess individual perceived safety of other HDVs and AVs1-5, as well as to achieve cooperative, decentralized behavioral coordination and verifiably safe control in highway traffic scenarios. Human-in-the-loop driving simulation experiments and scaled vehicle-traffic system experiments will be conducted to investigate and evaluate the developed methods. Educational activities such as curriculum development and graduate/undergraduate student research participation will be conducted. Research dissemination and K-12 outreach activities will also be pursued to further increase the broader impact of the research.
Artificial Intelligence (AI) has shown superior performance in enhancing driving safety in advanced driver-assistance systems (ADAS). State-of-the-art deep neural networks (DNNs) achieve high accuracy at the expense of increased model complexity, which raises the computation burden of onboard processing units of vehicles for ADAS inference tasks. The primary goal of this project is to develop innovative collaborative AI inference strategies with the emerging edge computing paradigm. The strategies can adaptively adjust cooperative inference techniques for best utilizing available computation and communication resources and ultimately enable high-accuracy and real-time inference. The project will inspire greater collaborations between experts in wireless communication, edge computing, computer vision, autonomous driving testbed development, and automotive manufacturing, and facilitate AI applications in a variety of IoT systems. The educational testbed developed from this project can be integrated into courses to provide hands-on experiences. This project will benefit undergraduate, master, and Ph.D. programs and increase under-represented groups? engagement by leveraging the existing diversity-related outreach efforts.
A multi-disciplinary team with complementary expertise from Rowan University, Temple University, Stony Brook University, and Kettering University is assembled to pursue a coordinated study of collaborative AI inference. The PIs explore integrative research to enable deep learning technologies in resource-constrained ADAS for high-accuracy and real-time inference. Theory-wise, the PIs plan to take advantage of the observation that DNNs can be decomposed into a set of fine-grained components to allow distributed AI inference on both the vehicle and edge server sides for inference acceleration. Application-wise, the PIs plan to design novel DNN models which are optimized for the cooperative AI inference paradigm. Testbed-wise, a vehicle edge computing platform with V2X communication and edge computing capability will be developed at Kettering University GM Mobility Research Center. The cooperative AI inference system will be implemented, and the research findings will be validated on realistic vehicular edge computing environments thoroughly. The data, software, and educational testbeds developed from this project will be widely disseminated. Domain experts in autonomous driving testbed development, intelligent transportation systems, and automotive manufacturing will be engaged in project-related issues to ensure relevant challenges in this project are impactful for real-world applications.
This NSF CIVIC grant will provide a sustainable, scalable, and transferable proof of concept for addressing the spatial mismatch between housing affordability and jobs in US cities by co-creating a Community Hub for Smart Mobility (CHSM) in vulnerable neighborhoods with civic partners. The spatial mismatch between housing affordability and jobs causes commuter traffic congestion resulting in an annual $29 billion loss to the U.S. economy alone. Rather than a one-size-fits-all solution, this grant will provide a sustainable, scalable, and transferrable method for local communities to co-create solutions that meet their needs and align with their values. CHSMs will be community-level hubs where residents can access multiple modes of transport such as shared (e-)bikes, e-scooters, ride hailing, electric vehicle charging stations, and public transit. It will provide more mobility options to residents, increase their access to jobs, ameliorate existing transit deserts, and improve the efficiency of the overall transportation system. The CHSM concept will likely benefit all kinds of communities, with the greatest benefits going to those who are most underserved by the current transportation system and most under-resourced due to the job/housing mismatch. The results and findings from this project will provide new insights into how to improve residents? mobility in vulnerable neighborhoods and alleviate the job/housing mismatch nationwide.
The goal of this project is to develop, implement, and evaluate a Community Hub for Smart Mobility (CHSM) to solve the job/housing mismatch in US cities. The research team will focus on Georgian Acres, a historically under-resourced neighborhood in northeast Austin, Texas and work with multiple civic partners to address the unique transportation needs of the neighborhood. The project will be carried out through the following five thrusts: 1. Co-Designing the GA-CHSM with the Georgian Acres (GA) Neighborhood, 2. Co-Developing the GA-CHSM, 3. Co-Operating the CHSM with Civic Partners, 4. Evaluating the GA-CHSM?s Impacts, and 5. Scaling and transferring the CHSM concept through the City of Austin?s Project Connect. Both quantitative (e.g., GIS analysis, transportation modeling, machine learning) and qualitative (e.g., interviews, surveys, focus groups) research methods will be used in this project to evaluate the success of the CHSM. These findings will contribute to the literatures on transportation planning, community planning, and participatory design and will serve as a model for tackling the job/housing mismatch through community-based mobility solutions.
This project is part of the CIVIC Innovation Challenge which is a collaboration of NSF, Department of Energy Vehicle Technology Office, Department of Homeland Security Science and Technology Directorate and Federal Emergency Management Agency.