  Understanding and predicting highway lane-change maneuvers is essential for
driving modeling and its automation. The development of data-based
lane-changing decision-making algorithms is nowadays in full expansion. We
compare empirically in this article different machine and ensemble learning
classification techniques to the MOBIL rule-based model using trajectory data
of European two-lane highways. The analysis relies on instantaneous
measurements of up to twenty-four spatial-temporal variables with the four
neighboring vehicles on current and adjacent lanes. Preliminary descriptive
investigations by principal component and logistic analyses allow identifying
main variables intending a driver to change lanes. We predict two types of
discretionary lane-change maneuvers: overtaking (from the slow to the fast
lane) and fold-down (from the fast to the slow lane). The prediction accuracy
is quantified using total, lane-changing and lane-keeping errors and associated
receiver operating characteristic curves. The benchmark analysis includes
logistic model, linear discriminant, decision tree, na\"ive Bayes classifier,
support vector machine, neural network machine learning algorithms, and up to
ten bagging and stacking ensemble learning meta-heuristics. If the rule-based
model provides limited predicting accuracy, the data-based algorithms, devoid
of modeling bias, allow significant prediction improvements. Cross validations
show that selected neural networks and stacking algorithms allow predicting
from a single observation both fold-down and overtaking maneuvers up to four
seconds in advance with high accuracy.
