  Quantifying and encoding occupants' preferences as an objective function for
the tactical decision making of autonomous vehicles is a challenging task. This
paper presents a low-complexity approach for lane-change initiation and
planning to facilitate highly automated driving on freeways. Conditions under
which human drivers find different manoeuvres desirable are learned from
naturalistic driving data, eliminating the need for an engineered objective
function and incorporation of expert knowledge in form of rules. Motion
planning is formulated as a finite-horizon optimisation problem with safety
constraints. It is shown that the decision model can replicate human drivers'
discretionary lane-change decisions with up to 92% accuracy. Further proof of
concept simulation of an overtaking manoeuvre is shown, whereby the actions of
the simulated vehicle are logged while the dynamic environment evolves as per
ground truth data recordings.
