Cost and time-efficient system development is a major challenge in the automotive industry. Although modeling standards like EAST-ADL have shown promising results to expedite system development, such standards are unable to cope with the growing demands of the automotive industry. A typical example of this phenomenon is autonomous vehicle perception (AVP) where deep learning architectures (DLA) are required for computer vision (CV) tasks like real-time object recognition and detection. However, existing modeling standards in the automotive industry are unable to manage such CV tasks at a higher abstraction level. Consequently, system development is currently accomplished through modeling approaches like EAST-ADL while DLA-based CV features for AVP are implemented in isolation at a lower abstraction level. This significantly compromises productivity due to integration challenges. In this article, we introduce MoDLF - A Model-Driven Deep learning Framework to design deep convolutional neural network (CNN) architectures for AVP tasks. Particularly, Model Driven Architecture (MDA) is leveraged to propose a metamodel along with a conformant graphical modeling workbench to model deep CNNs for CV tasks in AVP at a higher abstraction level. Furthermore, Model-To-Text (M2T) transformations are provided to generate executable code for MATLAB and Python. The framework is validated via two case studies on benchmark datasets for key AVP tasks of object recognition and detection. The results prove that MoDLF effectively enables model-driven architectural exploration of deep convnets for AVP system development while supporting integration with renowned existing standards like EAST-ADL.