  In complex traffic environments, autonomous vehicles face multi-modal
uncertainty about other agents' future behavior. To address this, recent
advancements in learningbased motion predictors output multi-modal predictions.
We present our novel framework that leverages Branch Model Predictive
Control(BMPC) to account for these predictions. The framework includes an
online scenario-selection process guided by topology and collision risk
criteria. This efficiently selects a minimal set of predictions, rendering the
BMPC realtime capable. Additionally, we introduce an adaptive decision
postponing strategy that delays the planner's commitment to a single scenario
until the uncertainty is resolved. Our comprehensive evaluations in traffic
intersection and random highway merging scenarios demonstrate enhanced comfort
and safety through our method.
