En-route planning is a dynamic planning process to find the optimal route (e.g., shortest route) while driving. The goal of this paper is to mimic a realistic drivers' en-route planning behavior under the situations with incomplete information about road conditions using the Extended Belief-Desire-Intention (E-BDI) framework. The proposed E-BDI based en-route planning is able to find a new route to the destination based on the predicted road conditions inferred by drivers' own psychological reasoning. A main challenge of such a detailed E-BDI model is a high computational demand needed to execute a large scale road network, which is typical in a big city. To mitigate such a high computational demand, a hierarchical route planning approach is also proposed in this work. The proposed approach has been implemented in Java-based E-BDI modules and DynusT® traffic simulation software, where a real traffic data of Phoenix, Arizona is used. To validate the proposed hierarchical approach, the performance of the en-route planning modules under the different aggregation levels is compared in terms of their computational efficiency and modeling accuracy. The validation results reveal that the proposed en-route planning approach efficiently generates a realistic route plan with individual driver's prediction of the road conditions.