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.

翻译：在复杂的交通环境中，自动驾驶车辆面临其他智能体未来行为的多模态不确定性。为解决这一问题，近期基于学习的运动预测器能够输出多模态预测。我们提出了一种新颖框架，利用分支模型预测控制来整合这些预测。该框架包含一个由拓扑和碰撞风险准则引导的在线场景选择过程，能够高效选出最少预测集，使分支模型预测控制具备实时能力。此外，我们引入了一种自适应决策延迟策略，可将规划器对单一场景的承诺延迟至不确定性消除。在交通路口和随机高速公路汇流场景中的全面评估表明，我们的方法提升了舒适性与安全性。