Development of a Statistical Method for Predicting Human Driver Decisions and a Paradigm for Communicating Predicted Decisions to Automated Vehicles in the Connected Environment

As the world moves towards a future of vehicle automation, there will be a long transition period in which the driving environment will include a mix of automated and non-automated vehicles. While automated vehicles can communicate future trajectories and speeds to each other, human drivers do not communicate their intent. Being able to predict what a human driver is planning to do can substantially aid automated vehicles in planning and even improve the performance of driver assistance systems. The project will propose to use functional data analysis approaches to develop predictive models of driver decision-making in three contexts: 1) left turn go/stop-then-go, stop/no-stop at stop signs, and yellow-light go/stop. The project will use existing driving data to identify a sample of these scenarios. Predictors will be developed from kinematic variables that can be measured using vehicle-based sensors. These may include speed, lateral and longitudinal acceleration, pedal use, position in lane, and following distance. Key outcomes of this effort include: 1) A general statistical modeling approach for handling prediction of driver decisions based entirely on sensor data; 2) A paradigm for conveying predictions and prediction uncertainty to an automated vehicle in a vehicle-communication setting; and 3) Prediction models of driver decision for the scenarios analyzed. This work can be generalized to a variety of driver decisions and has wide applicability to improve driver assistance systems and the performance of automated vehicles co-existing in an environment with human-controlled vehicles.


  • English


  • Status: Completed
  • Contract Numbers:


  • Sponsor Organizations:

    Department of Transportation

    Office of the Assistant Secretary for Research and Technology
    1200 New Jersey Avenue, SE
    Washington, DC  United States  20590
  • Performing Organizations:

    University of Michigan, Ann Arbor

    Transportation Research Institute
    Ann Arbor, MI  United States 
  • Principal Investigators:

    Flannagan, Carol

  • Start Date: 20140901
  • Expected Completion Date: 0
  • Actual Completion Date: 20150831
  • Source Data: RiP Project 37105

Subject/Index Terms

Filing Info

  • Accession Number: 01536415
  • Record Type: Research project
  • Source Agency: Center for Advancing Transportation Leadership and Safety (ATLAS Center)
  • Contract Numbers: DTRT13-G-UTC54
  • Files: UTC, RiP
  • Created Date: Aug 31 2014 1:00AM