  This literature review focuses on three important aspects of an autonomous
car system: tracking (assessing the identity of the actors such as cars,
pedestrians or obstacles in a sequence of observations), prediction (predicting
the future motion of surrounding vehicles in order to navigate through various
traffic scenarios) and decision making (analyzing the available actions of the
ego car and their consequences to the entire driving context). For tracking and
prediction, approaches based on (deep) neural networks and other, especially
stochastic techniques, are reported. For decision making, deep reinforcement
learning algorithms are presented, together with methods used to explore
different alternative actions, such as Monte Carlo Tree Search.
