Research
Research Interests
Dr. Qin's current research interests are sensing and learning from humans and leveraging human intelligence into embodied artificial intelligent agents such as advanced driver assistance systems and robots. Her research provides theory-grounded, data-driven, and technology-assisted solutions for improving the performance, safety, and well-being of humans in interaction with complex engineered systems ranging from intellient vehicles, transportation infrastructure, to built environments.
Recently, she focuses on research problems arising from
Sensing, perception and prediction for autonomous vehicles and advanced driver assistive systems
Image and video analysis for transportation infrastructure health monitoring
Human sensing, understanding, and task performance measurement
Current Projects
Driving Assistance to Elderly Drivers in Rural Areas
USDOT through Rural Equitable and Accessible Transportation (REAT) Center
02/22/2023-12/31/2024
GAANN: Fellowship Program in Civil Engineering for Advancing Smart Civil Infrastructure Systems (SCIS) [flyer]
Department of Education
10/1/2021-09/30/2024
FW-HTF-RM: Collaborative Research: Assistive Intelligence for Cooperative Robot and Inspector Survey of Infrastructure Systems (AI-CRISIS)
National Science Foundation
09/01/2020-08/31/2024
Selected Research Articles
Most of my papers can be searched on Google Scholar. The following is some of my recent work.
Perception and Prediction for AV and ADAS
Karim, M.M., Qin, R., & Yin, Z. (2022). An attention-guided multistream feature fusion network for early localization of risky traffic agents in driving videos. IEEE Transactions on Intelligent Vehicles. DOI: 10.1109/TIV.2023.3275543. [manuscript] [code]
Karim, M.M., Li, Y., Qin, R., & Yin, Z. (2022). A dynamic spatial-temporal attention network for early anticipation of traffic accidents. IEEE Transaction on Intelligent Transportation Systems 23(7): 9590-9600. DOI: 10.1109/TITS.2022.3155613. [manuscript] [code]
Karim, M.M., Li, Y., & Qin, R.(2022). Toward explainable artificial intelligence (XAI) for early anticipation of traffic accidents. Transportation Research Record 2626 (6): 743-755. DOI:10.1177/03611981221076121. [manuscript] [code]
Li, Y., Karim, M.M., Qin, R., Sun, Z., Wang, Z., & Yin, Z. (2021). Crash report data analysis for creating scenario-wise, spatio-temporal attention guidance to support computer vision-based perception of fatal crash risks. Accident Analysis & Prevention 151, 105962. DOI: 10.1016/j.aap.2020.105962. [paper]
Image and Video Analysis for IH&PM
Zhang, C., Yin, Z., & Qin, R. (2024). Attention-Enhanced Co-Interactive Fusion Network (AECIF-Net) for automated structural condition assessment in visual inspection. Automation in Construction 159, 105292. DOI: 10.1016/j.autcon.2024.105292 [manuscript]
Zhang, C., Karim, M.M., & Qin, R. (2023). A multitask deep learning model for parsing bridge elements and segmenting defects in bridge inspection images. Transportation Research Record. DOI: 10.1177/0361198123115541. [manuscript]
Zhang, C., Karim, M.M., Yin, Z, & Qin, R. (2022, June 5-8). A deep neural network for multiclass bridge element parsing in inspection image analysis. In Proceedings of 8th World Conference on Structural Control and Monitoring (8WCSCM). Orlando, FL, USA. [manuscript]
Karim, M.M., Qin, R., Yin, Z., & Chen, G. (2022). A semi-supervised self-training method to develop assistive intelligence for segmenting multiclass bridge elements from inspection videos. Structural Health Monitoring 21(3), 835-852. DOI: 10.1177/14759217211010422. [manuscript] [code]
Zhao, T., Yin, Z., Qin, R., & Chen, G. (2019, August 4-7). Image data analytics to support engineers’ decision-making. In Proceedings of the 9th International Conference on Structural Health Monitoring of Intelligent Infrastructure (SHMII-9), St. Louis, MO, USA. [manuscript]
Human Sensing, Understanding, and Performance Measurement for HTI
Li, Y., Parson, A., Wang, B., Dong, P., Yao, S., & Qin, R. (2022). A multi-tasking model of speaker-keyword classification for keeping human in the loop of drone-assisted inspection. Engineering Applications of Artificial Intelligence, 117 (Part A), 105597. [manuscript] [code]
Li, Y., Wang, B., Li, W., & Qin, R. (2022). Simulation study of passing drivers’ responses to the automated truck-mounted attenuator system in road maintenance. Transportation Research Record. DOI:10.1177/03611981221144281. [manuscript] [code]
Li, Y., Karim, M.M., Qin, R. (2021). A gaze data-based comparative study to build a trustworthy human-AI collaboration in crash anticipation. ASCE 2023 International Conference on Transportation Development (ICTD'23). Austin, TX, USA. June 14-17, 2023. [manuscript] [code]
Al-Amin, M., Qin, R., Tao, W., Doell, D., Lingard, R., Yin, Z., & Leu, M.C. (2022). Fusing and refining CNN models for assembly action recognition in smart manufacturing. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science 236(4), 2046-2059. DOI: 10.1177/09544062209315. [paper]
Al-Amin, M., Qin, R., Moniruzzaman, M., Yin. Z., Tao, W., & Leu, M.C. (2023). An individualized system of skeletal data-based CNN classifiers for action recognition in manufacturing assembly. Journal of Intelligent Manufacturing 34: 633-649. DOI:10.1007/s10845-021-01815-x. [paper]
Al-Amin, M., Tao, W., Doell, D., Lingard, R., Yin, Z., Leu, M.C., & Qin, R. (2019). Action recognition in manufacturing assembly using multimodal sensor fusion. Procedia Manufacturing 39, 158-167. DOI: 10.1016/j.promfg.2020.01.288. [paper]
RX-assisted Human/Algorithm Training
Li, Y., Karim, M.M., & Qin, R. (2022). A virtual reality-based training and assessment system for inspector-drone cooperative bridge inspection. IEEE Transactions on Human Machine Systems, 52(4), 591-601. DOI: 10.1109/THMS.2022.3155373. [manuscript] [code]