Heated debates continue over the best autonomous driving framework. The classic modular pipeline is widely adopted in the industry owing to its great interpretability and stability, whereas the end-to-end paradigm has demonstrated considerable simplicity and learnability along with the rise of deep learning. We introduce a new modularized end-to-end reinforcement learning framework (ModEL) for autonomous driving, which combines the merits of both previous approaches. The autonomous driving stack of ModEL is decomposed into perception, planning, and control module, leveraging scene understanding, end-to-end reinforcement learning, and PID control respectively. Furthermore, we build a fully functional autonomous vehicle to deploy this framework. Through extensive simulation and real-world experiments, our framework has shown great generalizability to various complicated scenarios and outperforms the competing baselines.