  Training intelligent agents that can drive autonomously in various urban and
highway scenarios has been a hot topic in the robotics society within the last
decades. However, the diversity of driving environments in terms of road
topology and positioning of the neighboring vehicles makes this problem very
challenging. It goes without saying that although scenario-specific driving
policies for autonomous driving are promising and can improve transportation
safety and efficiency, they are clearly not a universal scalable solution.
Instead, we seek decision-making schemes and driving policies that can
generalize to novel and unseen environments. In this work, we capitalize on the
key idea that human drivers learn abstract representations of their
surroundings that are fairly similar among various driving scenarios and
environments. Through these representations, human drivers are able to quickly
adapt to novel environments and drive in unseen conditions. Formally, through
imposing an information bottleneck, we extract a latent representation that
minimizes the \textit{distance} -- a quantification that we introduce to gauge
the similarity among different driving configurations -- between driving
scenarios. This latent space is then employed as the input to a Q-learning
module to learn generalizable driving policies. Our experiments revealed that,
using this latent representation can reduce the number of crashes to about
half.
