  Autonomous vehicles need to abide by the same rules that humans follow. Some
of these traffic rules may depend on multiple agents or time. Especially in
situations with traffic participants that interact densely, the interactions
with other agents need to be accounted for during planning. To study how
multi-agent and time-dependent traffic rules shall be modeled, a framework is
needed that restricts the behavior to rule-conformant actions during planning,
and that can eventually evaluate the satisfaction of these rules. This work
presents a method to model the conformance to traffic rules for interactive
behavior planning and to test the ramifications of the traffic rule
formulations on metrics such as collision, progress, or rule violations. The
interactive behavior planning problem is formulated as a dynamic game and
solved using Monte Carlo Tree Search, for which we contribute a new method to
integrate history-dependent traffic rules into a decision tree. To study the
effect of the rules, we treat it as a multi-objective problem and apply a
relaxed lexicographical ordering to the vectorized rewards. We demonstrate our
approach in a merging scenario. We evaluate the effect of modeling and
combining traffic rules to the eventual compliance in simulation. We show that
with our approach, interactive behavior planning while satisfying even complex
traffic rules can be achieved. Moving forward, this gives us a generic
framework to formalize traffic rules for autonomous vehicles.
