Abstract: To solve the problem of the interaction effects between vehicles in dynamic driving scenarios, an autonomous lane change decision making and motion planning method were proposed for intelligent vehicles based on graph convolution network (GCN) and quadratic programming (QP). Firstly, some interested regions were hierarchically modeled, and the global and local dynamic interaction information of the driving scene were aggregated in a form of graph-structured data, and the driving behavior instructions should take in the ego vehicle were output with the GCN. Then, combined with motion planning module, the free spaces were divided based on the local sub-graph, a quadratic programming model was constructed and solved to obtain collision-free motion trajectory satisfied with kinematics constraints, completing the autonomous lane change without collision finally. The results of simulation experiments and real vehicle verification show that the proposed method can provide better performance than the conventional decision making and motion planning method, showing better experimental success rate and scene generalization performance.