ITSC 2024 Paper Abstract

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Paper ThBT9.5

Bouzidi, Mohamed-Khalil (Free University of Berlin, Continental AG), Derajic, Bojan (Continental AG; Technical University of Berlin), Goehring, Daniel (Freie Universität Berlin), Reichardt, Joerg (Continental AG)

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

Scheduled for presentation during the Regular Session "Motion planning" (ThBT9), Thursday, September 26, 2024, 15:50−16:10, Salon 17

2024 IEEE 27th International Conference on Intelligent Transportation Systems (ITSC), September 24- 27, 2024, Edmonton, Canada

This information is tentative and subject to change. Compiled on October 8, 2024

Keywords Automated Vehicle Operation, Motion Planning, Navigation, Advanced Vehicle Safety Systems

Abstract

In complex traffic environments, autonomous vehicles face multi-modal uncertainty about other agents' future behavior. To address this, recent advancements in learning-based 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 real-time 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.

 

 

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