# The Development of Teleoperated Driving to Cooperate with the Autonomous Driving Experience
## 7 Conclusions  
This study presents the successful implementation of a real-world dual-mode driving platform that integrates low-level and high-level control systems with a 5G-based remote-control interface built on ROS and Autoware. The system allows seamless switching between autonomous and teleoperation modes, guided by real-time latency monitoring and a fallback mechanism that engages emergency braking when network conditions degrade.  
The modified electric golf cart was evaluated over four structured test phases, demonstrating stable operation under various latency conditions. An abnormality detection unit actively monitored communication status and environmental input, enabling the system to transition automatically into emergency mode in response to anomalies such as latency spikes or obstacle detection. Average, minimum, and maximum latencies were 109.7 ms, 83.7 ms, and 316.6 ms, respectively, measured using ROS timestamp tools on a 5G sub-6 GHz NSA network.  
Trajectory tracking performance was consistent across both control modes. Remote control yielded slightly lower peak deviations, with values ranging from 0.19 to 0.25 m (average) and up to 0.41 m (maximum). Autonomous mode showed slightly higher maximum deviations, reaching 0.88 m. Emergency braking tests further revealed that latency impacts stopping distance significantly--at 3 m/s, braking distance increased from 1.42 m to 2.37 m depending on delay severity. In extreme-latency scenarios (\(\geq\)500 ms), operator steering was required in addition to braking to avoid collision.  
Overall, the system validates the feasibility of real-time, latency-aware dual-mode operation over public 5G networks with full hardware integration. The architecture offers a reliable and practical approach for next-generation mobility applications, with potential for future expansion into urban-scale or higher-speed environments.  
**Author Contributions:** Conceptualization, N.N., S.C. and G.P.; data curation, K.T.; formal analysis, K.T. and S.C.; funding acquisition, N.N.; investigation, K.T. and S.C.; methodology, K.T.; project administration, N.N.; resources, N.N.; software, K.T.; supervision, N.N., S.C. and G.P.; validation, K.T. and S.C.; visualization, K.T., S.C. and G.P.; writing--original draft, N.N., S.C. and G.P.; writing--review and editing, N.N., S.C. and G.P. All authors have read and agreed to the published version of the manuscript.  
**Funding:** This research project is supported by the Thailand Science Research and Innovation Fund Chulalongkorn University: IND_FF_68_007_2100_001.  
**Data Availability Statement:** Any inquiry can be directly sent to the corresponding author.  
**Acknowledgments:** This study was partially supported financially from the Special Task Force for Activating Research (STAR), Ratchadaphiseksomphot Endowment Fund, Chulalongkorn University. The tested vehicle was supported by Electric Vehicles Thailand PCL (EVT). The tested equipment and partial funding was supported by Smart Mobility Research Center, Chulalongkorn University through a research grant from Broadcasting and Telecommunications Research and Development Fund for Public Interest. Also, We would also like to thank the Immersive Technology Laboratory (IMT Lab), Faculty of Engineering, Chulalongkorn University, for their support.  
**Conflicts of Interest:** The authors declare no conflicts of interest.