  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
optimization-based 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.
