Development of a statistical method for predicting human driver decisions.
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2015-09-01
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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.
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