Route and Stopping Intent Prediction at Intersections From Car Fleet Data

In this paper, an approach is presented to predict the route and stopping intent of human-driven vehicles at urban intersections using a selection of distinctive features observed on the vehicle state (position, heading, acceleration, velocity). For potential future advanced driver assistance system...

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Bibliographic Details
Published inIEEE transactions on intelligent vehicles Vol. 1; no. 2; pp. 177 - 186
Main Authors Gross, Florian, Jordan, Justus, Weninger, Felix, Klanner, Felix, Schuller, Bjorn
Format Journal Article
LanguageEnglish
Published IEEE 01.06.2016
Subjects
Advanced driver assistance systems
Automobiles
car fleet data
driver state and intent recognition
driving data
Feature extraction
Hidden Markov models
Intelligent vehicles
machine learning
Roads
Urban areas
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Summary:In this paper, an approach is presented to predict the route and stopping intent of human-driven vehicles at urban intersections using a selection of distinctive features observed on the vehicle state (position, heading, acceleration, velocity). For potential future advanced driver assistance systems, this can facilitate the situation analysis and risk assessment at road intersections, helping to improve the protection of vulnerable road users. After extracting recorded driving data for nine intersections (featuring over 50 000 crossings) from a database, they are assigned to possible routes and transformed from a time-based representation to a distance-based one. Using random decision forests, the route intent can be predicted with a mean unweighted average recall (UAR) of 0.76 at 30 m before the relevant intersection center, the stopping intent prediction scores a mean UAR of 0.78.
ISSN:2379-8858
2379-8904
DOI:10.1109/TIV.2016.2617625