Driver impairment detection and safety enhancement through comprehensive volatility analysis

Safe driving is highly correlated with driving behavior, as it has been widely reported that over 90% of crashes are a result of human error [NHTSA 2017]. Impaired driving is a key contributing factor leading to 10,497 fatalities (28% of all transportation crash-related deaths) in 2016 [FHWA 2018]. In recent years, with the ubiquity of sensors and increasing computational resources, it has become possible to monitor driver, vehicle and roadway/environment to extract useful information from multi-dimensional data streams coming in from diverse sources. Through a National Science Foundation study, the team has developed the concept of driving volatility to quantify variations in vehicle kinematics, driver biometrics, and the roadway environment. The study will explore how to measure driver-vehicle-roadway volatilities using Naturalistic Driving Study data and a driving simulator. By integrating and fusing multiple data sources including driver biometrics, vehicle kinematics, and roadway/environment conditions in real-time, this project aims to generate useful feedback to drivers and warnings to surrounding vehicles regarding hazards. The key objectives of this research are to: 1) Develop a framework for obtaining, processing, and analyzing high-frequency multi-dimensional large-scale data using sensors that monitor the driver, vehicle, and roadways. The framework will harness the data and quantify variations in driver biometrics and behavior, vehicle kinematics, and roadway/environmental conditions utilizing the concept of volatility. 2) Analyze Naturalistic Driving Study data to explore correlations of driver biometrics, driving style, and roadway / environmental characteristics with driver impairment and crash risk. 3) Using a multi-modal-multi-user virtual reality simulator, collect and process driver, vehicle and roadway data, develop algorithms to identify driving impairment by monitoring data streams emanating from driver, vehicle, and roadway in real-time. Based on algorithms, provide feedback and warnings to the driver and possibly to the surrounding vehicles.

• Record URL:
  https://www.roadsafety.unc.edu/research/projects/2019r23/
• Supplemental Notes:
  • CSCRS2019R23

Language

• English

Project

• Status: Active
• Funding: $37,930
• Contract Numbers:

  69A3551747113

• Sponsor Organizations:

  Office of the Assistant Secretary for Research and Technology

  University Transportation Centers Program
  Department of Transportation
  Washington, DC  United States  20590
• Managing Organizations:

  Collaborative Sciences Center for Road Safety

  University of North Carolina, Chapel Hill
  Chapel Hill, NC  United States  27514
• Project Managers:

  Sandt, Laura

• Performing Organizations:

  University of Tennessee, Knoxville

  Knoxville, TN  United States 

  University of North Carolina, Chapel Hill

  Chapel Hill, NC  United States  27514
• Principal Investigators:

  Chakraborty, Subhadeep

  Clamann, Michael

  Khattak, Asad J

• Start Date: 20190801
• Expected Completion Date: 20211231
• Actual Completion Date: 0
• USDOT Program: University Transportation Centers Program

Subject/Index Terms

• TRT Terms: Algorithms; Automatic data collection systems; Behavior; Biometrics; Data analysis; Drivers; Driving simulators; Impaired drivers; Sensors; Virtual reality; Volatility
• Subject Areas: Data and Information Technology; Highways; Safety and Human Factors;

Filing Info

• Accession Number: 01715885
• Record Type: Research project
• Source Agency: Collaborative Sciences Center for Road Safety
• Contract Numbers: 69A3551747113
• Files: UTC, RiP
• Created Date: Aug 29 2019 3:23PM