在近年來備受關注vehicle-to-everything (V2X)通訊技術中，載具可以定期廣播安全感知訊息 (CAM)給所有周遭的其他載具來提升載具的道路安全性。
在安全感知訊息中，會包含載具的狀態與屬性，例如訊息時間戳記、載具的位置、速度、運動
狀態與車輛種類等等。
然而，來自過時的安全感知訊息的內容與載具安全相關的預測失真，可能會產生潛在的風險與嚴重的載具安全問題。
此外，在不存在沒有基地台的環境，也就是在未覆蓋環境(out-of-coverage)之中，因為沒有基地台在不同的載具之間集中地協調，所以會更容易發生潛在的頻道壅塞問題。
而且要在這樣的環境中，更進一步的權衡頻道壅塞與載具安全，更是一個困難的問題。
在本篇論文中，我們提出了一個有效率的載具安全感知訊息廣播頻率控制策略(DESBRAC)，來使載具可以同時考量不同的安全指標並共同決定出安全感知訊息廣播頻率。
我們也考量兩個不同的安全感知訊息廣播頻率的限制與實時測量的指標，來去避免頻道壅塞與提升總體的頻道使用效率。
為了更好的提升與感知安全性，我們提出了一個新的指標立即訊息年齡IAoI來去幫助載具同時考量不同的參數，例如訊息年齡、追蹤誤差與其他安全指標。
我們使用IAoI當作權重來去決定安全感知訊息廣播頻率。
此外，為了解決訊息對時間敏感與潛在的頻道壅塞問題，我們引入了空中計算(AirComp)來幫助載具即時地從周遭載具集合關鍵訊息，並以此訊息估算指標並合作決定安全感知訊息廣播頻率。
最終，分別在兩種不同的載具網路環境下的模擬結果表示，相較於現今其他算法而言，我們的策略可以有效提升約31%的載具安全性。

Promising V2X communication technologies can increase road safety by periodically broadcasting Cooperative Awareness Message (CAM) that contain vehicles’ status and attribute information, such as time, location, velocity, motion state, and vehicle type, to all nearby vehicles.
However, out-of-date information from CAM and prediction deviations may cause potential risks and severe vehicle safety problems.
Moreover, in the out-of-coverage scenario, where there is no base station to centrally coordinate between different vehicles, the absence of a central entity causes more potential issues with channel congestion.
Additionally, it is even harder to solve the trade-off problem of channel congestion and vehicle safety in the out-of-coverage scenario for the same reason.
In this thesis, we propose an efficient safety-aware CAM broadcast rate control strategy termed Decentralized Safety-aware CAM Broadcast Rate Control Strategy (DESBRAC) for vehicles to consider more safety metrics and determine the CAM broadcast rates cooperatively.
We also consider two CAM broadcast rate constraints and real-time metrics to avoid channel congestion and improve the overall channel efficiency.
To further enhance the safety-awareness, we propose a new metric called the Instant AoI (IAoI) that jointly considers different factors such as Age of Information (AoI), Tracking Error (TE), and other safety metrics. We use IAoI as a weight to determine the CAM broadcast rate.
To deal with time-sensitive and potential channel congestion issues, we introduce Over-the-Air Computation (AirComp), a novel aggregation method that helps vehicles instantly aggregate critical information from their nearby vehicles to perform metric estimation and cooperative CAM broadcast rate determination.
Finally, the simulation results based on two different scenarios of vehicular networks show that our strategy can achieve an improvement of about 31% in driving safety compared to the state-of-the-art algorithms.

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