We present a systematic approach called Extended VINS-Mono to utilize VINS-Mono, a state-of-the-art monocular visual-inertial relative localization method, targeting practical vehicle localization in large-scale outdoor road environments. Our proposed fusion approach associates multiple independent localization methods and provides multiple (projected) state estimates in a desired coordinate system to satisfy different accuracy, rate and latency requirements. We extend VINS-Mono with absolute localization methods like GNSS and relative localization methods like Kalman-filter-based INS to provide global state estimation for navigation/routing and local state estimation for planning/control. Additionally, Extended VINS-Mono addresses two significant drawbacks in VINS-Mono for use in large-scale outdoor road environments. First, motion on an almost planar road surface will make scale unobservable in VINS-Mono. Secondly, moving objects in dynamic scenarios will degrade accuracy. We handle the scale estimation problem of VINS-Mono by extending its (re-)initialization process with speed readings and introducing a speed factor for use with graph optimization. A dynamic feature-point filter method with masks from DNN-based object detection handles dynamic environments and re-collects feature points on stationary objects like parked cars. Better global accuracy is obtained with Extended VINS-Mono, compared to VINS-Mono, in a 25 km-trip journey through highways, tunnels, urban areas and suburban areas in Pittsburgh. Thus, Extended VINS-Mono can be used for reliable and accurate absolute localization in dynamic road environments. We also evaluate the accuracy, localization rate and latency of multiple (projected) state estimates in the global coordinate system from multiple localization methods. Our fusion method is therefore able to satisfy different localization requirements of various tasks on an intelligent vehicle.