Completed 2021 Research Project

Safety enhancement by detecting driver impairment through analysis of real-time volatilities 

Principal Investigator
Asad Khattak
University of Tennessee, Knoxville
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Co-Principal Investigator
Subhadeep Chakraborty
University of Tennessee, Knoxville
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Co-Principal Investigator
Michael Clamann
University of North Carolina, Chapel Hil

Final Report

Project Slide Deck

Project Datasets

Research Brief

Video on Related Work

Summary

The overall goal of the project is to focus on understanding early detection of driver impairment using streaming biometric information coupled with data on vehicle performance and surrounding contexts. During Phase 1 of the project, the team focused on developing a framework for driver impairment detection through analysis of driver biometric information along with vehicle and road infrastructure factors, with streaming data. This project will contribute by implementing the framework and the model developed in Phase 1 to detect impaired driving and any abnormality in the driver, vehicle, and roadway/environment system performance. The model can be used by transportation stakeholders to reduce the probability of crashes. 

The motivation behind our research is to enhance safety by monitoring driver actions and detecting impairment. To accomplish this task, our team will conduct experiments in a simulated environment where we will request the participants to emulate specific distracted driving behaviors, e.g., texting, reading, looking at scenery, drowsiness, and drinking. We will take a multimodal approach to data collection, monitoring, and analysis. Specifically, the data will include driver biometric signals, vehicle dynamics and telemetry, and external environmental conditions, e.g., traffic flow, simulated weather, day/night conditions. The outcomes of this project will include embedding leading indicators of impairment in Advanced Driver Assistance System (ADAS) that can greatly enhance safety, given the substantial interest from major automotive and information technology companies, especially for applications in fleet vehicles. 

Related Project: Driver impairment detection and safety enhancement through comprehensive volatility analysis

Publications/Presentations

• Usman, S. M., Khattak, A. J., Chakraborty, S., Mahdinia, I., & Tavassoli, R. (2024). Detection of distracted driving through the analysis of real-time driver, vehicle, and roadway volatilities. Journal of Transportation Safety & Security, 1–22. https://doi.org/10.1080/19439962.2024.2341393
• Tavassoli, R., & Chakraborty, S. (2024, April). Driver impairment detection and safety enhancement through unified analysis of driver, vehicle and traffic volatilities. [Presentation]. UT Oak Ridge Innovation Institute.
• Ahmad, N., Arvin, R., & Khattak, A. J. (2023). How is the duration of distraction related to safety–critical events? Harnessing naturalistic driving data to explore the role of driving instability. Journal of Safety Research, 85, 15–30. https://doi.org/10.1016/j.jsr.2023.01.003
• Ahmad, N., Arvin, R., & Khattak, A. J. (2022). Exploring pathways from driving errors and violations to crashes: The role of instability in driving. Accident Analysis and Prevention, 179, https://doi.org/10.1016/j.aap.2022.106876
• Ahmad N., Khattak, A., & Bozdogan, H. (2023). Predicting safety-critical events using driver behaviors and performance: Application of machine learning. [Presentation]. TRBAM-23-00144.
• Ahmad, N. (2021, December). Role of human factors, driving instability, and roadway environment in safety critical events: Safe System approach. [Ph.D., dissertation]. University of Tennessee. https://trace.tennessee.edu/utk_graddiss/6961

Project Details