Motion Planning under Uncertainty: Integrating Learning-Based Multi-Modal Predictors into Branch Model Predictive Control
<|reference_start|>Motion Planning under Uncertainty: Integrating Learning-Based Multi-Modal Predictors into Branch Model Predictive Control: 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.<|reference_end|>
@article{bouzidi2024motion,
    title={Motion Planning under Uncertainty: Integrating Learning-Based
  Multi-Modal Predictors into Branch Model Predictive Control},
    author={Mohamed-Khalil Bouzidi, Bojan Derajic, Daniel Goehring, Joerg
  Reichardt},
    journal={arXiv preprint arXiv:2405.03470},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.03470},
    primaryClass={cs.RO cs.SY eess.SY}
}