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Citation |
Tan YV, Elliott MR, Flannagan CAC. Accid. Anal. Prev. 2017; 106: 428-436. |
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Affiliation |
University of Michigan, Transportation Research Institute, United States. |
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Copyright |
(Copyright © 2017, Elsevier Publishing) |
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DOI |
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PMID |
28735178 |
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Abstract |
As connected autonomous vehicles (CAVs) enter the fleet, there will be a long period when these vehicles will have to interact with human drivers. One of the challenges for CAVs is that human drivers do not communicate their decisions well. Fortunately, 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. 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 random intercept Bayesian additive regression trees. Five more road and observable vehicle characteristics were included to enhance prediction. Our model achieved an area under the receiver operating characteristic curve (AUC) of 0.84 at 94m away from the center of an intersection and steadily increased to 0.90 by 46m away from the center of an intersection. Language: en |
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Keywords |
Bayesian additive regression trees; Connected autonomous vehicles; Connected driverless vehicles; Longitudinal prediction; Naturalistic driving data; Principal components analysis |