# The Development of Teleoperated Driving to Cooperate with the Autonomous Driving Experience
## 1 Introduction  
Autonomous vehicle technology is advancing rapidly, driven by efforts from both the automotive industry and academic institutions. These developments aim to enhance safety, efficiency, and driver convenience [1]. According to the Society of Automotive Engineers (SAE) [1], automation levels range from Level 0 (no automation) to Level 5 (full automation), where no human intervention is needed. However, most current systems operate below Level 5 and still require human input in complex or failure-prone scenarios [2, 3]--such as navigating through narrow lanes or reacting to unexpected obstacles. In situations where the driver is incapacitated or the vehicle is uncrewed, a remote operation system becomes essential. Despite their value, remote systems face challenges--primarily relatedto latency over communication networks. Hence, a reliable control transition framework, supported by an efficient alert system and robust communication infrastructure, is critical for enabling a seamless shift between autonomous and remote-control modes in emergency or failure situations. The transformation of conventional vehicles into autonomous platforms necessitates integration with core systems like steering, throttle, and braking through electronically controlled actuators. A common method for achieving this is through x-by-wire technology [4], which replaces mechanical control linkages with electronic signal-based systems. This study focuses on the design, implementation, and evaluation of an autonomous electric vehicle capable of transitioning between autonomous and remote-control modes using 5G communication. Building on earlier research [5; 6], a 14-seat electric golf cart was modified by integrating actuators in the steering, acceleration, and braking systems--collectively referred to as the vehicle's low-level control systems [7]. For steering, electric power steering (EPS) was interfaced with a custom controller to simulate torque signals. The braking system used a brushless DC motor to actuate the pedal via cable and pulley, delivering responsive control for speeds up to approximately 8.33 m/s (approximately 30 km/h). The acceleration system emulated throttle signals to regulate the electric motor directly, completing the core control triad. Further developments in similar research projects have used varied configurations of control systems and actuators. For example, some studies have implemented external actuators on the steering column, employing sensors and motorized torque feedback systems [8]. Others have upgraded small electric vehicles, such as the Toyota COMS, to Level 3 automation using satellite-based GNSS for high-level path planning [9]. In comparison, this study introduces a unique braking system utilizing a hydraulic mechanism driven by a DC motor to directly pressurize brake lines, resulting in smoother braking without pedal displacement--a marked improvement over previous rod-actuated designs. The remote operation concept, as defined by teleoperation [10], allows a human operator to control a vehicle from a distant location without direct visual contact. A typical teleoperation interface includes visual feeds, sensor data, and input devices like a steering wheel and pedals. Interfaces vary from direct video-based control to multimodal systems with sensor fusion, supervisory systems for semi-autonomous guidance, and experimental methods like neural control using bio-signals. In remote driving scenarios, effective control depends on a triad: the teleoperator, communication link, and operator interface. Prior studies using vehicles like the Audi Q7 have demonstrated the practicality of teleoperation using multiple cameras and sensor arrays configured to meet regulatory standards [11]. Cameras provided 180\({}^{\circ}\) frontal views and additional angles for side and rear visibility, with real-time video streaming at 25 fps. Communication used 3G and 4G networks, and latency was evaluated using round trip time (RTT) metrics.