Shiyan Yang, Senior Research Scientist, Seeing Machines

About Me

The goal of my career is to empower machines and AI to better understand and support humans. Specifically, my research focuses on integrating human factors—behavioral, cognitive, and physiological—into machine learning and AI systems to enhance user safety, performance, and overall experience.

This vision inspired me to join Seeing Machines (2017-2024), where I contributed to the innovation of driver monitoring systems, a groundbreaking technology at the time aimed at saving lives on the road. By combining human factors expertise with machine learning, I advanced the detection of driver distraction, drowsiness, and the transition of control in complex driving scenarios.

My research has been instrumental in shaping the technical roadmaps, algorithm development, and industry guidelines for automotive-grade driver monitoring systems. It has also garnered recognition through publications in top-tier journals and prestigious international awards, such as Patricia F. Waller Award.

Research Projects and Publications

2017-2024, Seeing Machines

MIT Advanced Vehicle Technology Consortium
Yang, S., McKerral, A., Mulhall, M. D., Lenné, M. G., Reimer, B., & Gershon, P. (2023). Takeover Context Matters: Characterising Context of Takeovers in Naturalistic Driving using Super Cruise and Autopilot. In Proceedings of the 15th International Conference on Automotive User Interfaces and Interactive Vehicular Applications, 112-122.

Yang, S., Lenné, M.G., Reimer, B., Gershon, P. (2022). Modeling Driver-Automation Interaction using A Naturalistic Multimodal Driving Dataset. In Proceedings of the 66th Human Factors & Ergonomics Society International Annual Meeting,66(1), 1462-1466.
Advanced Safe Truck Concept (ASTC)
Yang, S., Kuo, J., Lenné, M. G., Fitzharris, M., Horberry, T., Blay, K., … and Truche, C. (2021). The impacts of temporal variation and individual differences in driver cognitive workload on ECG-based detection. Human Factors, 63(5), 2021.
CANdrive Automated Vehicle Trial
Yang, S., Wilson, K.M., Shiferaw, B., Trey, R., Kuo, J., and Lenné, M.G. (2024). Sensor fusion to connect gaze fixation with dynamic driving context for driver attention management. Transportation Research Part F, (107)578-588.

Yang, S., Wilson, K.M., Trey, R., Kuo, J., and Lenné, M.G. (2022). Beyond gaze fixation: Modeling peripheral vision in relation to speed, Tesla Autopilot, cognitive load, and age in highway driving. Accident Analysis & Prevention, 177, 106670.

Yang, S., Wilson, K.M., Trey, R., Kuo, J., and Lenné, M.G. (2022). Evaluating driver features for cognitive distraction detection and validation in manual and Level 2 automated driving. Human Factors, 64(4), 746-759.

Yang, S., Kuo, J., and Lenné, M.G. (2021). Effects of distraction in on-road level-2 automated driving: Impacts on glance behavior and take-over performance. Human Factors, 63(8), 1485-1497.

Wilson, K. M., Yang, S., Roady, T., Kuo, J., and Lenné, M. G. (2020). Driver trust and mode confusion in an on-road study of automated vehicle technology. Safety Science, 130, 104845.

2016-2017, PATH, The University of California, Berkeley

Connected and Automated Truck Platooning
Yang, S., Shladover, S.E., Lu, X., Ramezani, H., Kailas, A., and Altan, O.D. (2021). A Bayesian regression analysis of truck drivers' use of cooperative adaptive cruise control (CACC) for platooning on California highways. Journal of Intelligent Transportation Systems, 1-12.

Yang, S., Shladover, S.E., Lu, X., Ramezani, H., Kailas, A., and Altan, O.D. (2018). A first investigation of truck drivers' preferences and behaviors using a prototype cooperative adaptive cruise control system. Transportation Research Record, 2672(2), 39-48.

2010-2016, Industrial Engineering, Texas A&M University

Multimodal Display Design
Yang, S., & Ferris, T. K. (2019). Supporting Multitracking Performance with Novel Visual, Auditory, and Tactile Displays. IEEE Transactions on Human-Machine Systems, 50(1), 79-88.
Cognitive Efficiency in Human-Machine Systems
Yang, S., & Ferris, T. K. (2018). Cognitive efficiency in human-machine systems: Metrics of display effectiveness for supporting multitask performance. Journal of Cognitive Engineering and Decision Making, 12(2), 153-169.