Accurate localization with high availability is a key requirement for autonomous vehicles. It remains a major challenge when using automotive sensors such as single-frequency GNSS receivers, a lane detection camera and proprioceptive sensors. This paper describes a method that enables the estimation of standalone L1-GNSS errors by integrating the measurements from a forward looking camera matched with lane markings stored in a digital map. It includes a parameter identification method for a shaping model which is evaluated using experimental data. An algebraic observability study is then conducted to prove that the proposed state vector is fully observable in a road-oriented frame. This observability property is the basis to develop a road-centred Extended Kalman Filter (EKF) which can maintain the observability of every component of the state vector on any road, whatever its orientation. To accomplish this the flter needs to handle road changes, which it does using bijective transformations. The fllter was implemented and tested intensely on an experimental vehicle for driverless valet parking services. Field results have shown that the performance of the estimation process is better than solutions based on EKF implemented in a fixed working frame. The proposed filter guarantees that the drift along the road direction remains bounded. This is very important when the vehicle navigates autonomously. Further, the road-centred modeling improves the accuracy, consistency and robustness of the localization solver.