This paper presents an integrated bottom-up and top-down computing process for parsing cars. By parsing, it means detecting all instances in an input image, and aligning their constituent parts, if appeared. The output of parsing is to construct configurations of car instances. In real scenarios such as in street scenes, cars often appear with different degree of occlusions, which bring two problems in car parsing: (1) Occlusions often fail those holistic methods, so we use a deformable part-based model. In terms of generative models, this paper proposed a star-like pictorial structural model based on the active basis model. The presented model is hierarchical and deformable. (2) In turn, part-based models entail integrated bottom-up and top-down computing processes. Bottom-up processes generated hypotheses from input images for each node in the deformable model. Top-down processes are followed to verify those bottom-up hypotheses in terms of their configurations. In order to evaluate the proposed method, we build up a dataset in which different kinds of occlusions are randomly added to cars. Experiment results show that the integrated bottom-up and topdown process improves the performance greatly.