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Autonomous vehicles need to abide by the same rules that humans follow. Someof these traffic rules may depend on multiple agents or time. Especially insituations with traffic participants that interact densely, the interactionswith other agents need to be accounted for during planning. To study howmulti-agent and time-dependent traffic rules shall be modeled, a framework isneeded that restricts the behavior to rule-conformant actions during planning,and that can eventually evaluate the satisfaction of these rules. This workpresents a method to model the conformance to traffic rules for interactivebehavior planning and to test the ramifications of the traffic ruleformulations on metrics such as collision, progress, or rule violations. Theinteractive behavior planning problem is formulated as a dynamic game andsolved using Monte Carlo Tree Search, for which we contribute a new method tointegrate history-dependent traffic rules into a decision tree. To study theeffect of the rules, we treat it as a multi-objective problem and apply arelaxed lexicographical ordering to the vectorized rewards. We demonstrate ourapproach in a merging scenario. We evaluate the effect of modeling andcombining traffic rules to the eventual compliance in simulation. We show thatwith our approach, interactive behavior planning while satisfying even complextraffic rules can be achieved. Moving forward, this gives us a genericframework to formalize traffic rules for autonomous vehicles.

Modeling and Testing Multi-Agent Traffic Rules within Interactive Behavior Planning