## 3 Teleoperation Challenges  
CAV teleoperation or remote driving has a distinctive set of challenges. CAVs heavily rely on multiple sensors and communication networks, unlike mobile robots or other vehicles. Since CAVs are connected to networks, they face increased cyber threats. Further, as CAVs are expected to operate in the real world, they can be dynamically operated, over long distances and with a full range of speed environments [3; 7]. They are also expected to make safe and ethical decisions. Hence, they require a robust, reliable and seamless communication system.  
Communication systems can be compromised by network latency during CAV teleoperation. It is widely acknowledged in the literature that network latency is one of the major challenges of teleoperation, which can cause significant disruptions in communication systems and performance [1; 3; 7; 8; 9; 10; 12]. Long and variable latency poses an even more significant challenge, making it nearly impossible to operate effectively. Even today, it is an unsolved problem.  
Latency can affect the quality of feedback from sensory data (visual, audio, and haptic) of the vehicle to the teleoperation station, making it difficult for the operator to acquire the current state and environment of the vehicle. Simultaneously, it affects the control command data from the station to the vehicle, in turn degrading the overall teleoperation performance in the control loop. Certain studies have indicated that latency can influence the decision making, behaviour, and mental workload of remote drivers, causing over- and under-steering of the remote vehicle and leading them to irregularly adjust their pressure on the acceleration and brake pedals [7; 8; 15], which thereby increases the driver's anger and frustration [17]. Latency simultaneously impacts the vehicle in terms of energy consumption, motion sickness, and degrading the comfort of passengers [15].  
It has been hypothesised in the literature that latency would have detrimental effects on the performance of both teleoperation and operator perception [18]. The performance will only decrease with increasing latency. Different types of latency (from various sources) can distinctly impact performance and adaptation strategies based on individuals [18]. For example, the effect of latency will be larger on complex system tasks than on simple ones [19]. Further, the level of automation can improve the system's ability to tackle latency, thereby increasing the trust in the system.  
Another major challenge of teleoperation is situational awareness limitations [7; 8]. Remote operators may need added data to improve their perception, including viewpoint shifting and adjustment, map merging, depth, spatial awareness, and multi-sensory feedback. All these data need a good system design and interface to avoid a cognitive workload for remote operators, as it can trigger confusion and distractions that can lead to accidents.  
Figure 4: Paper taxonomy classified for CAVs and other domains.  
There are also other teleoperation challenges, such as lack of physical sensing (e.g., force, sound), requiring improvement in the user interface with sensor fusion, bandwidth constraints, control issues such as lack of model estimation, uncertainties, and reliable local autonomy, and other issues related to human factors and workload requirements [7; 8].