Research in Symbiotic Transportation explores metacognition to allow an agent not only to act, but to assess how well it is acting, why it is acting in a particular way, and when it should revise its internal models or seek human input. Projects under this theme explore how metacognitive AI serves as the engine that powers multimodal digital twin analyses across humans, autonomy, and physical spaces. By advancing biomimetic mechanisms for introspection, metacognitive AI allows autonomous agents (e.g. autonomous vehicles) to actively perceive, learn from, and interact with their surrounding environment. This intrinsically-driven approach contrasts sharply with current methods that rely on extrinsic human feedback for rewards.
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Translational Capabilities
Driver Takeover Readiness in L3 Autonomous Driving
Vulnerable Road Users' (VRU) Intent Prediction in Mixed Traffic
Encoding Human Cognitive Events in Long-Tail Driving Cases
Crowdsensing for Infrastructure Condition Assessment
Publications (Click to Expand)
Zhou, X., Menassa, C. and Kamat, V. (2026). “Self‑supervised evolutionary learning for progression-aware segmentation of neurodynamic time series during safety‑critical decision making.” Cognitive Neurodynamics. Springer. Volume 20 (145).
Zhou, X., Menassa, C. and Kamat, V. (2025). “Siamese Network with Dual Attention for EEG-Driven Social Learning: Bridging the Human-Robot Gap in Long-Tail Autonomous Driving.” Expert Systems with Applications. Elsevier. Volume 291, 1 October 2025, https://doi.org/10.1016/j.eswa.2025.128470
Zhou, X., Menassa, C. and Kamat, V. (2025). “Hands-free Crowdsensing of Accessibility Barriers in Sidewalk Infrastructure: A Brain-Computer Interface Approach.” Journal of Infrastructure Systems. ASCE. 31 (2). https://doi.org/10.1061/JITSE4.ISENG-2620
Zhou, X., Menassa, C. and Kamat, V. (2025). “Exploring Annotation Automation for Urban Landscape Accessibility Assessment Based on Simulated Neurophysiological Data.” Proceedings of the 2024 ASCE International Conference on Computing in Civil Engineering (i3CE). https://doi.org/10.1061/9780784486139.021
Deng, M., Gluck, A., Zhao, Y., Li, D., Menassa, C., Kamat, V. and Brinkley, J. (2024). “A Systematic Analysis of Physiological Responses as Indicators of Driver Takeover Readiness in Conditionally Automated Driving.” Accident Analysis and Prevention. Elsevier. 195 (Feb. 2024). https://doi.org/10.1016/j.aap.2023.107372
Gluck. A., Deng. M., Zhao. Y., Menassa. C., Li. D., Brinkley. J. and Kamat. V. (2022). “Exploring Driver Physiological Response During Level 3 Conditional Driving Automation.” In the proceedings of the 2022 International Conference on Human Machine Systems (ICHMS). Orlando, FL. https://ieeexplore.ieee.org/abstract/document/9980597
Media (Click to Expand)
“Metro Detroit I-75 project nearly done, but with some drama 100 feet below.” Bridge Michigan, August 2023.
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