This study advances prior work on the remote control of Advanced Driver Assistance Sys-tems (ADAS) by introducing a full manual teleoperation mode that enables remote control over both longitudinal and lateral vehicle dynamics via accelerator, brake, and steering inputs. The core contribution is a flexible, dual-mode teleoperation architecture that allows seamless switching between assisted ADAS control and full manual operation, depending on driving context or system limitations. While teleoperation has been explored primarily for autonomous fallback or direct remote driving, few existing systems integrate dynamic mode-switching in a unified, real-time control framework. Our system leverages a wireless game controller and a Robot Operating System (ROS)-based vehicle software stack to translate remote human inputs into low-latency vehicle actions, supporting robust and adaptable remote driving. This design maintains a human-in-the-loop approach, offering improved responsiveness in complex environments, edge-case scenarios, or during au-tonomous system fallback. The proposed solution extends the applicability of teleopera-tion to a broader range of use cases, including remote assistance, fleet management, and emergency response. Its novelty lies in the integration of dual-mode teleoperation within a modular architecture, bridging the gap between ADAS-enhanced autonomy and full re-mote manual control.