## 6 Conclusions and Future Directions  
This paper provides a systematic literature review on the impact of network latency during teleoperation in CAVs. First, the latest trends and existing challenges have been discussed, followed by a system model of teleoperation along with sources of latency, the wireless technologies in teleoperation, and the impact of network latency across domains. Performing the teleoperation of CAVs is challenging, as they operate in significantly complex and dynamic environments with high mobility over long distances. The specifics and unique demands may vary across domains, but a consistent finding is the detrimental impact of network latency on teleoperation performance. The CAV teleoperation system has a distinctive set of requirements, including low latency, sufficient data rates, and high reliability. The most critical QoS parameters for CAV teleoperation are the uplink data rates and the downlink latency. This is because CAV sensor perception has significantly greater data needs than control commands, and a delay in control commands is more alarming than a delay in perception. For ideal CAV teleoperation,an acceptable latency threshold is preferred to be less than 250 ms or 300 ms, with the average uplink latency in the range of 50 to 120 ms and downlink latency in the 20 to 80 ms range. This threshold correlates with a few safety-critical applications such as telesurgery, UAV, nuclear, ordnance disposal, and some network games. Similarly, when it comes to data rate, the average uplink data rate can range from 8 to 50 Mbps, while data rates for downlink are in the range of 0.25 to 5 Mbps. The best wireless technology candidates to meet these needs are cellular networks, which provide high-speed communication and sufficient coverage for long-distance CAV teleoperation. As these technologies continue to evolve, from 4G to 5G and anticipated 6G, they play a crucial role in shaping the future of autonomous and remotely operated vehicles. The groundwork was laid by 4G by providing the basic infrastructure necessary for initial teleoperation capabilities, despite its limitations in latency and bandwidth. The advent of 5G significantly enhanced ultra-low latency, high data rates, and robust reliability, enabling real-time high-definition video streaming and precise remote control during teleoperation. This has made 5G the current standard for advanced remote driving applications, enhancing safety and operational efficiency. While still in the conceptual and development stage, the next generation 6G networks will be better suited to sustain emerging applications and network traffic. Then, state-of-the-art latency mitigation and compensation strategies have been analysed. The latencies can occur in different sources of the CAV teleoperation system, including transmission delay, operator response time, encoding and decoding, visual display, sensors, and actuator delays, which makes delay mitigation quite challenging. The predictive methods are well suited for latency mitigation in both teleoperation control and perception approaches. Recent studies use model-free and online predictive approaches for control and perception latencies, respectively. Among these, the most used are deep neural networks for time-series predictions. In addition, there are also other equally capable methods involving regression, probabilistic models, and local autonomy. Enlightened by the aforementioned analysis, this paper proposes the following future research directions:  
* More empirical studies across diverse environmental settings would be beneficial in determining the true and accurate CAV requirements. This also would establish a comprehensive understanding of CAV teleoperation. It is important to consider the impact of different levels of network reliability with varying QoS parameters, particularly latencies and bandwidths.