Connected vehicles are an integral part of the future of intelligent transportation systems. They use wireless and sensing technologies to enable communication and cooperation between vehicles and infrastructure. Nonetheless, sensor reliability and data integrity play a crucial role in these vehicles. As vehicles and infrastructures grow increasingly networked and automated, there is a pressing need to identify sensor-related anomalies and mitigate potential safety hazards they might pose. The overarching goal of this project is to protect the connected vehicular network against anomalous sensor readings from any cause to ensure the safety of vehicles and passengers. The research aims to (1) provide new capabilities to broadly address safety concerns in connected vehicles to meet emerging future needs of intelligent transportation systems, and (2) enable a diverse and inclusive community of scientists and engineers to work in multidisciplinary areas such as cognitive machine learning and cybersecurity.
With the ever increasing complexity of connected vehicles operating in a more complicated cyber-physical social environment, conventional anomaly detection methods will likely not be able to keep pace with the demands of these challenges and function safely in a tomorrow's smart and connected communities. This project will explore (1) novel algorithmic methods that will enable the vehicles to quickly classify different types of sensor failures, learn new emerging anomalous patterns of sensor activity, and assess their risks relative to vehicle safety, and (2) designs for efficient scalable safe multi-agent models to build reputational trust among the connected vehicles in order to facilitate V2V information sharing, learning, and cooperative decision-making, and (3) new consensus-based protocols for connected vehicles that provide for resilience and adaptivity in the presence of disruptions, interruptions, and changes to vehicle participation. Initial test and evaluations are conducted by computer simulations with publicly-available data sets on connected vehicles and autonomous systems.
Dr Heena Rathore is presently Assistant Professor in Department of Computer Science at Texas State University, San Marcos, Texas, USA. She formerly held positions as Assistant Professor of Practice at University of Texas at San Antonio and Visiting Assistant Professor at Texas A&M University at Texarkana. She has also worked as Data Scientist and Program Manager at Hiller Measurements, Austin. She received her Ph.D. from Indian Institute of Technology Jodhpur India while she was a Tata Consultancy Services Research Scholar. For her postdoctoral research, she worked on the US Qatar joint project on Medical Device Security, which included collaborators from Qatar University, the University of Idaho, and Temple University. Her research interests include applied machine learning for distributed, intelligent systems with complimentary areas of security. She has been the winner of several prestigious awards, including Educationist Empowering India, IEEE Region 5 Outstanding Individual Achievement Award, IEEE Central Texas Section Achievements Award, IIT Alumni Award for Recognizing Excellence in Young Alumni, MPUAT Young Engineer Award, NI Global Engineering Impact Award, and NI Graphical System Design Achievement Award.