This paper explores two system-level architecture approaches for Autonomous Driving algorithms: Modular and End-to-end solutions. Modular solutions employ a Modular design, breaking down complex autonomous driving tasks into manageable modules such as perception, decision and planning, and control, to achieve more effective system debugging and optimization. However, these Modular systems often suffer from information loss and response time delays due to extensive inter-module communication. End-to-end solutions, by using advanced machine learning techniques, map raw sensor data directly to driving behavior, simplifying the system architecture, improving overall system efficiency, and reducing latency. Nevertheless, End-to-end solutions still face challenges such as high training data requirements and difficulties in simulation validation and closed-loop evaluation. Through a systematic comparison, this study highlights the strengths and weaknesses of both studies and proposes a gradual integration strategy. This strategy involves initially integrating modularly and then progressively expanding their application. This allows Modular and End-to-end solutions to run in parallel, complementing each other and eventually leading to a fully integrated system.