## 6 Conclusions and Future Directions
For example, the studies can include testing performance in uncertain and dynamic environments, the trade-off between video quality and control responsiveness, and quantifying the robustness of predictive algorithms. * When developing latency mitigation methods, it would be beneficial to integrate various approaches. For example, traditional statistical methods (i.e., first-order prediction techniques) can be integrated with modern AI-based approaches to overcome their limitations. This will allow the methods to be effective and reliable against non-linear dynamic data (such as high-latency and high-speed teleoperations) without heavily relying on complex and accurate system models. Another example, integrating predictive methods with probabilistic approaches can provide the likelihood and uncertainty of predictions. This would help in determining the reliability of the developed method. * During teleoperation system experimentation, it is recommended to replicate real-world network conditions (including communication networks and injected delays) as closely as possible, as they offer robust evaluation by extensively testing the system on time-varying and fluctuating latency. Note that teleoperation in the presence of time-varying latency is more challenging than under constant and predictable latency. * While there are a few white papers and initiatives from industry projects and alliances for CAV teleoperation, there is a lack of sufficient standardisation in critical aspects such as network latency requirements to guarantee consistent performance evaluation, data security protocols to protect sensitive information and ensure the integrity of communication, and interoperability between different teleoperation system compo nents. Therefore, we recommend additional standardisation efforts to provide formal guidelines, products, services, specifications, and requirements accordingly. Conceptualisation, S.B.K. ; methodology, S.B.K. and Q.L. ; formal analysis, S.B.K., Q.L., F.B., O.C.L.H. and S.B. ; investigation, S.B.K. ; writing--original draft preparation, S.B.K. ; writing--review and editing, Q.L., F.B., O.C.L.H. and S.B. ; visualisation, S.B.K., Q.L., F.B. and O.C.L.H. ; supervision, Q.L., F.B., O.C.L.H. and S.B. ; project administration, Q.L. All authors have read and agreed to the published version of the manuscript. This research was funded by Coventry University grant number BDN68 KDUX. Not applicable. The authors declare no conflicts of interest. The following abbreviations are used in this manuscript:  
1G First generation  
3G Third generation  
3 GPP Third Generation Partnership Project  
4G Fourth generation  
5G Fifth generation  
6G Sixth generation  
ADS Automated driving system  
AE Auto-encoder  
AE Autonomous emergency braking  
AI Artificial intelligence  
ASUV Autonomous surface and underwater vehicle  
BSI British Standards Institution  
CAV Connected and autonomous vehicle  
C-ITS Cooperative intelligent transport system  
C-V2X Cellular vehicle-to-everything  
DL Downlink  
DNN Deep neural network  
DSRC Dedicated short-range communication  
DTMF Dual-tone multi-frequency  
DVI Digital visual interface  
EBs Exabytes  
ETSI European Telecommunications Standards Institute  
FOV Field-of-view  
FPS Frames per second  
GAN Generative adversarial network  
GB Gigabytes  
GEV Generalised extreme value distribution  
GPS General Packet Radio Service  
HMI Human-machine interface  
IEEE Institute of Electrical and Electronics Engineers  
IoE Internet of Everything  
IoV Internet of Vehicles  
IRC Inter-process communication  
ITS Intelligent transport system  
ITU International Telecommunication Union  
LAN Local area network  
LCD Liquid-crystal display  
LSTM Long short-term memory  
LTE Long-term evolution  
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