My work lies in the learning, control, and design of autonomous systems with an emphasis on connected automated vehicles (CAVs). I have been committed to developing robust autonomous vehicles, augmented reality (AR) technology, and V2X systems at Mcity. The highlights include: (1) a robust self-driving algorithm/software stack enabling high-level CAVs; (2) a data-and-AI-driven sensor-level augmented reality (AR) system for efficient safe CAV tests. These systems have been deployed on the Mcity CAV fleet and Mcity testing track for daily operations. I am interested in using big naturalistic human-driving data to train motion planning and control algorithms of self-driving cars, so the automated cars could behave with better roadmanship and thus higher acceptance. I am also interested in data-driven low-uncertainty learning algorithms for object detection, tracking, and fusion, in order to build the perception system of safety-critical autonomous systems.
Dr. Arpan Kusari has joined UMTRI as an Assistant Research Scientist, a position where he will bring his cutting-edge industry experience. Dr. Kusari has spent five years at Ford Motor Company researching exclusively on making autonomous vehicles safe and viable, working collaboratively with researchers from MIT and University of Michigan to advance the state-of-the-art knowledge in autonomous vehicles. His research interest spans through the spheres of sensing and perception; and decision-making and control, in the domain of autonomous vehicles. In the sensing and perception realm, his interests lie in uncertainty quantification and fault tolerance of a generic sensor suite. Dr. Kusari is also interested in utilizing noise reduction methods for designing cost-effective low SNR (signal-to-noise ratio) LiDARS. In decision making and control, he is focused on creating a robust framework capable of handling the uncertainty stemming from other road users’ behavior. In that regard, Dr. Kusari is pursuing development of methods for increasing the efficiency and robustness of probabilistic formalisms such as reinforcement learning and evolutionary algorithms to safely navigate the dynamic environment. His doctoral research was in LiDAR mapping in the areas of sensor calibration, precise estimation of earthquake displacement and uncertainty quantification in the point cloud.
Anthony Vanky develops and applies data science and computational methods to design, plan, evaluate cities, emphasizing their applications to urban planning and design. Broadly, his work focuses on the domains of transportation and human mobility; social behaviors and urban space; policy evaluation; quantitative social sciences; and the evaluation of urban form. Through this work, he has extensively collaborated with public and private partners. In addition, he considers creative approaches toward data visualization, public engagement and advocacy, and research methods.
Anthony Vanky’s Cityways project analyzed 2.2 million trips from 135,000 people over one year to understand the factors that influence outdoor pedestrian path choice. Factors considered included weather, urban morphology, businesses, topography, traffic, the presence of green spaces, among others.
Gabor Orosz is an Associate Professor of Mechanical Engineering and Civil and Environmental Engineering. His theoretical research include dynamical systems, control, and reinforcement learning with particular interests in the roles of nonlinearities and time delays in such systems. In terms of applications he focuses on connected and automated vehicles, traffic flow, and biological networks. His research has been supported by the National Science Foundation and industrial funds. His recent work appeared in journals like IEEE Transactions on Automated Control, IEEE Transactions on Control Systems Technology, IEEE Transactions on Intelligent Transportation Systems, and Transportation Research Part C. For the latter journal he has also be serving as an Editor. WIRED magazine reported on his experimental results when his team built a connected automated vehicle and evaluated it in real traffic. He served as the program chair for the 12th IFAC Workshop on Time Delay Systems and served as the general chair for 3rd IAVSD Workshop on Dynamics of Road Vehicles, Connected and Automated Vehicles.
For human-machine systems, I first collect data from human users, whether it’s an individual, a team, or even a society. Different kinds of methods can be used, including self-report, interview, focus groups, physiological and behavioral data, as well as user-generated data from the Internet.
Based on the data collected, I attempt to understand human contexts, including different aspects of the human users, such as emotion, cognition, needs, preferences, locations and activities. Such understanding can then be applied to different human-machine systems, including healthcare systems, automated driving systems, and product-service systems.
Based on the different design theory and methodology, from the perspective of the machine dimension, I apply knowledge of computing and communication as well as practical and theoretical knowledge of social and behavior to design various systems for human users. From the human dimension, I seek to understand human needs and decision making processes, and then build mathematical models and design tools that facilitate integration of subjective experiences, social contexts, and engineering principles into the design process of human-machine systems.
My main interest is theoretical statistics as implied to complex model from semiparametric to ultra high dimensional regression analysis. In particular the negative aspects of Bayesian and causal analysis as implemented in modern statistics.
An analysis of the position of SCOTUS judges.
He develops and applies operations research, data science, and systems approaches to public and private service industries. His research focuses on the management and policy analysis of emerging networked industries and innovative mobility services such as smart parking, connected vehicles, autonomous vehicles, ride-hailing, bike sharing, and car sharing. He has worked extensively with both public and private sector partners worldwide. He is a queueing theorist that uses statistics, stochastic modeling, simulation and dynamic optimization.
Transportation is the backbone of the urban mobility system and is one of the greatest sources of environmental emissions and pollutions. Making urban transportation efficient, equitable and sustainable is the main focus of my research. My students and I analyze small scale survey data as well as large scale spatiotemporal data to identify travel behavior trends and patterns at a disaggregate level using econometric methods, which we then scale up to the population level through predictive and statistical modeling. We also design our own data collection methods and instruments, be it a network of smart devices or stated preference experiments. Our expertise lies in identifying latent constructs that influence decisions and choices, which in turn dictate demands on the systems and subsystems. We use our expertise to design incentives and policy suggestions that can help promote sustainable and equitable multimodal transportation systems. Our team also uses data analytics, particularly classification and pattern recognition algorithms, to analyze crash context data and develop safety-critical scenarios for automated and connected vehicle (CAV) deployment. We have developed an online game based on such scenarios to promote safe shared mobility among teenagers and young adults and plan to expand research in that area. We are also currently expanding our research to explore the use of NN in context information synthesis.
This is a project where we used classification and Bayesian models to identify scenarios that are risky for pedestrians and bicyclists. We then developed an online game based on those scenarios for middle schoolers so that they are better prepared for shared road conflicts.
My areas of interest are control, estimation, and optimization, with applications to energy systems in transportation, automotive, and marine domains. My group develops model-based and data-driven tools to explore underlying system dynamics and understand the operational environments. We develop computational frameworks and numerical algorithms to achieve real-time optimization and explore connectivity and data analytics to reduce uncertainties and improve performance through predictive control and planning.