Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing
Factor graph, as a bipartite graphical model, offers a structured representation by revealing local connections among graph nodes. This study explores the utilization of factor graphs in modeling the autonomous racecar planning problem, presenting an alternate perspective to the traditional optimiza...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2024-01-01
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| Series: | IEEE Open Journal of Intelligent Transportation Systems |
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| Online Access: | https://ieeexplore.ieee.org/document/10571575/ |
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| _version_ | 1832590326169075712 |
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| author | Salman Bari Xiagong Wang Ahmad Schoha Haidari Dirk Wollherr |
| author_facet | Salman Bari Xiagong Wang Ahmad Schoha Haidari Dirk Wollherr |
| author_sort | Salman Bari |
| collection | DOAJ |
| description | Factor graph, as a bipartite graphical model, offers a structured representation by revealing local connections among graph nodes. This study explores the utilization of factor graphs in modeling the autonomous racecar planning problem, presenting an alternate perspective to the traditional optimizationbased formulation. We model the planning problem as a probabilistic inference over a factor graph, with factor nodes capturing the joint distribution of motion objectives. By leveraging the duality between optimization and inference, a fast solution to the maximum a posteriori estimation of the factor graph is obtained via least-squares optimization. The localized design thinking inherent in this formulation ensures that motion objectives depend on a small subset of variables. We exploit the locality feature of the factor graph structure to integrate the minimum curvature path and local planning computations into a unified algorithm. This diverges from the conventional separation of global and local planning modules, where curvature minimization occurs at the global level. The evaluation of the proposed framework demonstrated superior performance for cumulative curvature and average speed across the racetrack. Furthermore, the results highlight the computational efficiency of our approach. While acknowledging the structural design advantages and computational efficiency of the proposed methodology, we also address its limitations and outline potential directions for future research. |
| format | Article |
| id | doaj-art-515b171340e5494693f38f16bc6a9d6e |
| institution | Kabale University |
| issn | 2687-7813 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Open Journal of Intelligent Transportation Systems |
| spelling | doaj-art-515b171340e5494693f38f16bc6a9d6e2025-01-24T00:02:51ZengIEEEIEEE Open Journal of Intelligent Transportation Systems2687-78132024-01-01538039210.1109/OJITS.2024.341895610571575Factor Graph-Based Planning as Inference for Autonomous Vehicle RacingSalman Bari0https://orcid.org/0000-0003-2321-2922Xiagong Wang1Ahmad Schoha Haidari2Dirk Wollherr3https://orcid.org/0000-0003-2810-6790TUM School of Computation, Information and Technology, Technical University of Munich, Munich, GermanyTUM School of Computation, Information and Technology, Technical University of Munich, Munich, GermanyTUM School of Computation, Information and Technology, Technical University of Munich, Munich, GermanyTUM School of Computation, Information and Technology, Technical University of Munich, Munich, GermanyFactor graph, as a bipartite graphical model, offers a structured representation by revealing local connections among graph nodes. This study explores the utilization of factor graphs in modeling the autonomous racecar planning problem, presenting an alternate perspective to the traditional optimizationbased formulation. We model the planning problem as a probabilistic inference over a factor graph, with factor nodes capturing the joint distribution of motion objectives. By leveraging the duality between optimization and inference, a fast solution to the maximum a posteriori estimation of the factor graph is obtained via least-squares optimization. The localized design thinking inherent in this formulation ensures that motion objectives depend on a small subset of variables. We exploit the locality feature of the factor graph structure to integrate the minimum curvature path and local planning computations into a unified algorithm. This diverges from the conventional separation of global and local planning modules, where curvature minimization occurs at the global level. The evaluation of the proposed framework demonstrated superior performance for cumulative curvature and average speed across the racetrack. Furthermore, the results highlight the computational efficiency of our approach. While acknowledging the structural design advantages and computational efficiency of the proposed methodology, we also address its limitations and outline potential directions for future research.https://ieeexplore.ieee.org/document/10571575/Motion planningautonomous racing vehiclesprobabilistic inferencefactor graphmodel predictive control |
| spellingShingle | Salman Bari Xiagong Wang Ahmad Schoha Haidari Dirk Wollherr Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing IEEE Open Journal of Intelligent Transportation Systems Motion planning autonomous racing vehicles probabilistic inference factor graph model predictive control |
| title | Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing |
| title_full | Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing |
| title_fullStr | Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing |
| title_full_unstemmed | Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing |
| title_short | Factor Graph-Based Planning as Inference for Autonomous Vehicle Racing |
| title_sort | factor graph based planning as inference for autonomous vehicle racing |
| topic | Motion planning autonomous racing vehicles probabilistic inference factor graph model predictive control |
| url | https://ieeexplore.ieee.org/document/10571575/ |
| work_keys_str_mv | AT salmanbari factorgraphbasedplanningasinferenceforautonomousvehicleracing AT xiagongwang factorgraphbasedplanningasinferenceforautonomousvehicleracing AT ahmadschohahaidari factorgraphbasedplanningasinferenceforautonomousvehicleracing AT dirkwollherr factorgraphbasedplanningasinferenceforautonomousvehicleracing |