Typically, mobile vehicles follow the same paths repeatedly, resulting in a common path bounded with some variance. These paths are often punctuated by branches into other paths based on decision-making in the area around the branch. This work applies a statistical methodology to determine decision-making regions for branching paths. An average path is defined in the proposed algorithm, as well as boundaries representing variances along the path. The boundaries along each branching path intersect near the decision point; these intersections in path variances are used to determine path-branching locations. The resulting analysis provides decision points that are robust to typical path conditions, such as two paths that may not clearly diverge at a specific location. Additionally, the methodology defines decision region radii that encompass statistical memberships of a location relative to the branching paths. To validate the proposed technique, an off-line implementation of the decision-making region algorithm is applied to previously classified wheelchair path subsets. Results show robust detection of decision regions that intuitively agree with user decision-making in real-world path following. For the experimental situation of this study, approximately 70% of path locations were outside of decision regions and thus could be navigated with a significant reduction in user inputs.