  Simulation has long been an essential part of testing autonomous driving
systems, but only recently has simulation been useful for building and training
self-driving vehicles. Vehicle behavioural models are necessary to simulate the
interactions between robot cars. This paper proposed a new method to formalize
the lane-changing model in urban driving scenarios. We define human incentives
from different perspectives, speed incentive, route change incentive, comfort
incentive and courtesy incentive etc. We applied a decision-theoretical tool,
called Multi-Criteria Decision Making (MCDM) to take these incentive policies
into account. The strategy of combination is according to different driving
style which varies for each driving. Thus a lane-changing decision selection
algorithm is proposed. Not only our method allows for varying the motivation of
lane-changing from the purely egoistic desire to a more courtesy concern, but
also they can mimic drivers' state, inattentive or concentrate, which
influences their driving Behaviour. We define some cost functions and calibrate
the parameters with different scenarios of traffic data. Distinguishing driving
styles are used to aggregate decision-makers' assessments about various
criteria weightings to obtain the action drivers desire most. Our result
demonstrates the proposed method can produce varied lane-changing behaviour.
Unlike other lane-changing models based on artificial intelligence methods, our
model has more flexible controllability.
