TRANSP Seminar: Real-Time Sensor Anomaly Detection and Identification in Automated Vehicles, Anahita Khojandi, PhD
Connected and automated vehicles (CAVs) are expected to revolutionize the transportation industry, mainly through allowing for a real-time and seamless exchange of information between vehicles and roadside infrastructure. Although connectivity and automation are projected to bring about a vast number of benefits, they can give rise to new challenges in terms of safety, security, and privacy. To navigate roadways, CAVs need to heavily rely on their sensor readings and the information received from other vehicles and roadside units. Hence, anomalous sensor readings caused by either malicious cyber-attacks or faulty vehicle sensors can result in disruptive consequences, and possibly lead to fatal crashes. As a result, before the mass implementation of CAVs, it is important to develop methodologies that can detect anomalies and identify their sources seamlessly and in real-time. In this work, we develop an anomaly detection approach through combining a deep learning method, namely convolutional neural network (CNN), with a well-established anomaly detection method, Kalman filtering, to detect and identify anomalous behavior in CAVs. Our numerical experiments demonstrate that the developed approach can detect anomalies and identify their sources with high accuracy, sensitivity, and F1 score. In addition, this developed approach outperforms the anomaly detection and identification capabilities of both CNNs and Kalman filtering methods alone. It is envisioned that this research will contribute to the development of safer and more resilient CAV systems that implement a holistic view towards intelligent transportation system (ITS) concepts.
Anahita Khojandi is an Assistant Professor in the Department of Industrial and Systems Engineering at the University of Tennessee, Knoxville. She received her M.S. and Ph.D. in Industrial Engineering from University of Pittsburgh in 2009 and 2014, respectively, and her B.S. in Industrial Engineering from Sharif University of Technology in 2008. Her research interests include decision making under uncertainty, data mining and machine learning with applications in monitoring and control of complex systems. She is a member of INFORMS, IISE, TRB, and IEEE.
Pizza will be provided!
Thursday, November 29, 2018 at 3:40pm to 5:00pm
John D. Tickle Engineering Building, 327
851 Neyland Dr, Knoxville, TN 37996