Motion Planning under Uncertainty: Integrating Learning-Based Multi-Modal Predictors into Branch Model Predictive Control

Autor:	Bouzidi, Mohamed-Khalil, Derajic, Bojan, Goehring, Daniel, Reichardt, Joerg
Rok vydání:	2024
Předmět:	Computer Science - Robotics Electrical Engineering and Systems Science - Systems and Control
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2405.03470 Zobrazit plný text záznamu View this record from Arxiv