Non-standard intersections, which do not follow a simple orthogonal three or four way configuration, present a navigational challenge to autonomous vehicles. Conventional navigation systems which gather data from the surrounding area then plan a path through the collected data require faultless and complex analysis of extremely unstructured environments. The vehicle must then avoid obstacles as well as successfully navigate the intersection with extremely low tolerance for error. Computer decision making challenges can arise from this method of navigation, especially when interacting with non-autonomous vehicles. This research presents a computational method of optimizing navigation through intersections based on pre-planned routing data. The static nature of roadways enabled detailed path planning, using a series of lines and arcs, which reduced, even the most complex intersections, into simply navigable splines. A five way, high angle intersection, including multiple railroad grade crossings and non-standard markings, was replicated for this small-scale evaluation. The autonomous vehicle then navigated the intersection, in any routing permutation, without the aid of external sensors. This method reduces the risk associated with navigational miscues, enabling a robust network enabled autonomous navigation model. Multiple vehicles were then integrated into the model utilizing an ad-hoc vehicle-to-vehicle (V2V) simplified communication string, to coordinate sequential movements through the intersection. The results of this research provide a simplified method for intersection navigation, which does not require standardized marking or traffic management cues beyond vehicle localization and pre-planned route spline data. The results suggest that this system enables safer navigation of complex environments, while the vehicle’s environmental and obstacle sensors may be used to provide episodic modification to planned routes in execution, rather than be relied upon for primary navigation.