  We consider the problem of group interactions in urban driving.
State-of-the-art behavior planners for self-driving cars mostly consider each
single agent-to-agent interaction separately in a cost function in order to
find an optimal behavior for the ego agent, such as not colliding with any of
the other agents. In this paper, we develop risk shadowing, a situation
understanding method that allows us to go beyond single interactions by
analyzing group interactions between three agents. Concretely, the presented
method can find out which first other agent does not need to be considered in
the behavior planner of an ego agent, because this first other agent cannot
reach the ego agent due to a second other agent obstructing its way. In
experiments, we show that using risk shadowing as an upstream filter module for
a behavior planner allows to plan more decisive and comfortable driving
strategies than state of the art, given that safety is ensured in these cases.
The usability of the approach is demonstrated for different intersection
scenarios and longitudinal driving.
