UTD Home UTD Home
Human-Centered Research Lab

Research at MSP

Monitoring driver distraction

Even a small distraction in drivers can lead to life-threatening accidents that affect the life of many. Monitoring distraction is a key aspect of any feedback system intended to keep the driver attention. Toward this goal, we are interested in modeling the behaviors observed when the driver is performing in-vehicle common secondary tasks such as operating a cellphone, radio or navigation system. The study employs the UTDrive platform - a car equipped with multiple sensors, including cameras, microphones, and Controller Area Network-Bus (CAN-Bus) information.

UTDrive Car

Cognitive-Visual Space





Selected Publications:

  1. Nanxiang Li and Carlos Busso, "Detecting drivers' mirror-checking actions and its application to maneuver and secondary task recognition," IEEE Transactions on Intelligent Transportation Systems, vol. 17, no. 4, pp. 980-992, April 2016. [soon cited] [pdf] [cited] [bib]
  2. Sumit Jha and Carlos Busso, "Probabilistic estimation of the driver's gaze from head orientation and position," in IEEE International Conference on Intelligent Transportation (ITSC), Yokohama, Japan, October 2017, pp. 1630-1635. [pdf] [cited] [bib] [slides]
  3. Sumit Jha and Carlos Busso, "Challenges in head pose estimation of drivers in naturalistic recordings using existing tools," in IEEE International Conference on Intelligent Transportation (ITSC), Yokohama, Japan, October 2017, pp. 1624-1629. [soon cited] [pdf] [bib] [slides]
  4. Sumit Jha and Carlos Busso, "Analyzing the Relationship Between Head Pose and Gaze to Model Driver Visual Attention," in IEEE Conference on Intelligent Transportation Systems (ITSC 2016), Rio de Janeiro, Brazil, November 2016, pp. 2157-2162. [pdf] [cited] [bib] [slides]
  5. John H.L. Hansen, Carlos Busso, Yang Zheng, and Amardeep Sathyanarayana, "Driver Modeling for Detection & Assessment of Distraction: Examples from the UTDrive Testbed," IEEE Signal Processing Magazine, vol. 34, no. 4, pp. 130-142, July 2017. [pdf] [cited] [bib]
  6. Nanxiang Li and Carlos Busso, "Predicting perceived visual and cognitive distractions of drivers with multimodal features," IEEE Transactions on Intelligent Transportation Systems, vol. 16, no. 1, pp. 51-65, February 2015. [pdf] [cited] [bib]
  7. Nanxiang Li, Jinesh J. Jain, and Carlos Busso, "Modeling of driver behavior in real world scenarios using multiple noninvasive sensors," IEEE Transactions on Multimedia, vol. 15, no. 5, pp. 1213-1225, August 2013. [pdf] [cited] [bib]
  8. Nanxiang Li and Carlos Busso, "Using perceptual evaluation to quantify cognitive and visual driver distractions," To appear in Smart Mobile In-Vehicle Systems - Next Generation Advancements, G. Schmidt, H. Abut, K. Takeda, and J. H. L. Hansen, Eds. pp. 183-207. Springer, New York, NY, USA, January 2014. [link-to-pdf] [cited] [bib]
  9. Carlos Busso and Jinesh J. Jain, "Advances in multimodal tracking of driver distraction," in DSP for In-Vehicle Systems & Safety, J. Hansen, P. Boyraz, K. Takeda, and H. Abut, Eds., p. In Press. Springer, New York, NY, USA, 2012. [link-to-pdf] [cited] [bib]
  10. Nanxiang Li and Carlos Busso, "Driver mirror-checking action detection using multi-modal signals," in The 6th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems, Seoul, Korea, September-October 2013, pp. 101-108. [pdf] [cited] [bib] [slides]
  11. Nanxiang Li, Amardeep Sathyanarayana, Carlos Busso, and John H.L. Hansen, "Rear-end collision prevention using mobile devices," in The 6th Biennial Workshop on Digital Signal Processing for In-Vehicle Systems, Seoul, Korea, September-October 2013, pp. 36-43. [pdf] [soon cited] [bib] [poster]

Affective computing

Synthesizing Behaviors

Multimodal Interfaces

Driver Distractions



(c) Copyrights. All rights reserved.