Development of a statistical method for predicting human driver decisions.

Details

• Creators:
  Tan, Yaoyan Vincent ; Elliott, Michael ; Flannagan, Carol
• Corporate Creators:
  University of Michigan. Transportation Research Institute
• Corporate Contributors:
  United States. Dept. of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  • Drivers
  • Decision making
  • Behavior
  • Kinematics
  • Mathematical prediction
  • Automatic data collection systems
  • Intersections
• Publication/ Report Number:
  ATLAS-2015-06
• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Corporate Publisher:
  Center for Advancing Transportation Leadership and Safety (ATLAS Center)
• Abstract:
  As autonomous vehicles enter the fleet, there will be a long period when these vehicles will have to interact with ; human drivers. One of the challenges for autonomous vehicles is that human drivers do not communicate their ; decisions well. However, the kinematic behavior of a human-driven vehicle may be a good predictor of driver intent ; within a short time frame. We analyzed the kinematic time-series data (e.g., speed) for a set of drivers making left ; turns at intersections to predict whether the driver would stop before executing the turn or not. We used principal ; components analysis (PCA) to generate independent dimensions that explain the variation in vehicle speed before a ; turn. These dimensions remained relatively consistent throughout the maneuver, allowing us to compute independent ; scores on these dimensions for different time windows throughout the approach to the intersection. We then linked ; these PCA scores to whether a driver would stop before executing a left turn using the Bayesian additive regression ; trees (BART). Our model achieved an area under the receiver operating characteristic curve (AUC) of more than 0.90 ; by -25m away from the center of an intersection.
  More +
• Format:
  PDF
• Funding:
  DTRT13-G-UTC54
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/29571/dot_29571_DS1.pdf
• File Type:
  [PDF - 1.51 MB]
• Collection(s):
  University Transportation Centers ; US Transportation Collection
• Main Document Checksum:
  urn:sha256:80933e000a747a7dda9044d03566d00a3f5c55c156a3fc7f1a8e72d575dc7529