  This paper concerns automated vehicles negotiating with other vehicles,
typically human driven, in crossings with the goal to find a decision algorithm
by learning typical behaviors of other vehicles. The vehicle observes distance
and speed of vehicles on the intersecting road and use a policy that adapts its
speed along its pre-defined trajectory to pass the crossing efficiently. Deep
Q-learning is used on simulated traffic with different predefined driver
behaviors and intentions. The results show a policy that is able to cross the
intersection avoiding collision with other vehicles 98% of the time, while at
the same time not being too passive. Moreover, inferring information over time
is important to distinguish between different intentions and is shown by
comparing the collision rate between a Deep Recurrent Q-Network at 0.85% and a
Deep Q-learning at 1.75%.

Machine Learning;Computer Science and Game Theory;Machine Learning