In the foreseeable future, connected and auto-mated vehicles (CAVs) and human-driven vehicles will share the road networks together. In such a mixed traffic environment, CAVs need to understand and predict maneuvers of surrounding vehicles for safer and more efficient interactions, especially when human drivers bring in a wide range of uncertainties. In this paper, we propose a learning-based lane-change prediction algorithm that considers the driving behaviors of the target human driver. To provide accurate maneuver prediction, we adopt a hierarchical structure that seamlessly seals both the lane-change decision prediction and the vehicle trajectory pre-diction together. Specifically, we propose a lane-change decision prediction method based on a Long-Short Term Memory (LSTM) network, and a trajectories prediction considering driver preference and vehicular interactions based on Inverse Reinforcement Learning (IRL). To validate the performance of the proposed methodology, a case study of an on-ramp merging scenario is conducted on a uniquely built human-in-the-loop simulation platform that can provide an immersive driving environment, collect data of lane-change behaviors, and test drivers' reactions to the prediction results in real time. It is shown in the simulation results that we can predict the lane-change decision 3 seconds before the vehicle crosses the line to another lane, and the Mean Euclidean Distance between the predicted trajectory and ground truth is 0.39 meters within a 4-second prediction window.