  We consider the problem of intelligently navigating through complex traffic.
Urban situations are defined by the underlying map structure and special
regulatory objects of e.g. a stop line or crosswalk. Thereon dynamic vehicles
(cars, bicycles, etc.) move forward, while trying to keep accident risks low.
  Especially at intersections, the combination and interaction of traffic
elements is diverse and human drivers need to focus on specific elements which
are critical for their behavior. To support the analysis, we present in this
paper the so-called Risk Navigation System (RNS). RNS leverages a graph-based
local dynamic map with Time-To-X indicators for extracting upcoming sharp
curves, intersection zones and possible vehicle-to-object collision points.
  In real car recordings, recommended velocity profiles to avoid risks are
visualized within a 2D environment. By focusing on communicating not only the
positional but also the temporal relation, RNS potentially helps to enhance
awareness and prediction capabilities of the user.
