## 5 High-Speed Scenarios Based on LEO Satellites for Future Autonomous Vehicles
Some examples include: [84], where the authors implement Reinforcement Learning (RL) to maximize the robustness of UAV dynamics control to cyber-physical attacks, and [85], where the authors implement a Proportional-Integral Derivative (PID) controller using PSO as a tuning method to achieve high stability. Furthermore, another optimization aspect that directly relates to security is resource management. Generally speaking, security measures require their own share of compu \begin{table}
\begin{tabular}{p{71.1 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Requirement** & **Domain** & **Ranges** & **Examples of Relevant References** \\ \hline High range for mobility support & C & 0–1000 km/h & UAV airspeed estimation [67]; survey of sensor fusion techniques for all-speed autonomous vehicles [68] \\ \hline High positioning accuracy & P, S & 0.1–10 m & 5G-based positioning [69]; positioning metrics in Cellular vehicle-to-everything (C-V2X) communications [\(\mathbb{P}\)]; precision needed for fully autonomous driving [12] \\ \hline High throughputs & C & 0.1–50,000 Gbps & Air-to-ground (A2G) communications for flying vehicles [7]; high throughputs through cognitive internet of vehicles [2] \\ \hline Low latencies & C, P, S & 1–30 ms & LEO latencies compared with terrestrial network latencies [73] \\ \hline High Coverage & C, P, S & \textgreater{}90\% & Global coverage design [74,75]; CubeSat constellation design for IoT [76] \\ \hline Environmental mapping & P, S & N/A & Aircraft runaway detection via remote sensing [77]; Space-Air-Ground integrated vehicular network [78] \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Communications (C), Positioning (P), and Sensing (S) Requirements for Autonomous Vehicles. tational resources, so any optimization that handles management of resources within a LEO system must consider the requirements of the possible security applications, even if as briefly as only a threshold or reserve. Such threshold is typically straightforward to define as an additional boundary in the optimization problem. A similar, scenario-based limitation that arises frequently in LEO satellite systems is the problem of the satellite handovers (i.e., changing end-user connection from one satellite to another), which heavily impacts the requirements of autonomous vehicle systems in terms of latency, throughput, and accuracy. Due to the low orbital altitude ( 200-2000 km) of LEO constellations, LEO satellites move at higher speeds (i.e., 7.5 km/s at 600 km altitude) than MEO and GEO satellites [16]. This effectively means that, for any particular autonomous vehicle, the satellite is available for only up to a few minutes before a handover is required [14], even when the LEO constellation is dense enough to provide 100% coverage for the area of interest. The frequency at which the handovers happen changes dynamically as the vehicle moves, and it can highly affect the latency, throughput, and accuracy targets, especially in high-speed motion scenarios with opposite directions with respect to the satellite's movement. Again, this remains a limitation that is being investigated to various degrees by academia in studies such as [40; 42].