In autonomous vehicle navigation, it usually involves a basic process of estimating the vehicle pose at one instant relative to the vehicle pose at a previous instant, and we refer to this process as temporal relative localization (TRL). Accurate and reliable TRL is valuable for vehicle localization and mapping which plays a fundamental role in autonomous vehicle navigation. A common way for realizing TRL is dead reckoning based on motion sensor such as inertial navigation system (INS). To augment the performance of INS based TRL, a nine-degree-of-freedom (9-DOF) vehicle dynamics model (VDM) is proposed as the system model for the filtering process of TRL; it can effectively characterize vehicle pitch and roll motion, especially in slope road scenarios. For better fault tolerance, a distributed fusion framework which consists of a master filter and two local filters for multi-source data fusion is introduced. The split covariance intersection filter (Split CIF) is applied to handle unknown temporal and spatial correlation that inevitably exists in such distributed fusion mechanism. A comparative experimental study demonstrates the advantage of the proposed method in terms of accuracy and robustness.