# The Development of Teleoperated Driving to Cooperate with the Autonomous Driving Experience
## 1 Introduction
The control station mirrored a real driving setup with multiple monitors and input devices. Despite latencies up to 500 ms, remote driving remained feasible at moderate speeds. However, high latency has been shown to significantly affect performance, particularly in complex tasks or systems with high degrees of freedom. For example, research on underwater camera platforms demonstrated that delays increase total operation time [12]. Some systems mitigate this by buffering commands during high latency and executing them when latency drops, although minimal operator input combined with autonomous fallback systems remains the most effective strategy for maintaining safety and performance. Modern remote driving applications require ultra-low latency and high-bandwidth communication, conditions met by 5G technology [13]. The fifth-generation cellular network (5G), standardized under IMT-2020, supports various communication needs: mMTCfor dense device environments, eMBB for high-throughput applications, and URLLC for ultra-reliable, low-latency tasks--ideal for autonomous vehicles. Current 5G deployments primarily use sub-6 GHz bands, which balance coverage and performance, while mmWave bands offer higher speeds but limited range and penetration [14; 15]. There are two modes in which 5G architecture can be implemented: non-standalone (NSA), built on existing 4G infrastructure, and standalone (SA), which uses native 5G architecture for superior latency, speed, and data handling [16; 17]. Complementing the communication layer, Autoware [18; 19; 20] is an open-source autonomous driving platform running on the Robot Operating System (ROS). It consolidates sensing, computing, planning, and control modules into a cohesive framework. Autoware ingests sensor data--LiDAR, GNSS, radar, and cameras--for localization, object detection, and mission and motion planning. The resulting velocity commands are sent to the vehicle's low-level controllers to execute safe and intelligent driving maneuvers. This software's modular design and extensive support tools streamline development, making it a cornerstone in academic and industrial autonomous vehicle projects. While earlier efforts have retrofitted electric platforms for autonomy [6; 7], or demonstrated public trials of autonomous golf carts [9], these systems lack the capability to switch between autonomous and remote operation. Furthermore, they do not account for network latency variations during teleoperation--a crucial factor for ensuring safety in degraded 5G environments. Prior studies on teleoperation interfaces [11; 12; 13] and empirical 5G performance [14; 16; 18] highlight the importance of robust fallback strategies, yet no previous work has implemented a latency-aware transition logic coupled with a physical emergency braking response. Compared to existing solutions that rely on uninterrupted connectivity or only partially support remote fallback, this system uniquely integrates real-time network feedback into control logic and employs hardware-level emergency handling. This addresses a critical gap in current platforms operating over public 5G networks. This research proposes a real-world dual-mode driving platform that (1) enables seamless transitions between autonomous and 5G-based remote operation, (2) incorporates real-time latency monitoring using ROS tools, and (3) triggers a hardware-based emergency braking mechanism when latency exceeds safety thresholds. Unlike existing studies, our implementation is tested in real-world 5G sub-6 GHz conditions with full hardware integration and live network feedback. These contributions position the system as a practical solution to edge-case safety in next-generation mobility platforms.