10.2 未來方向
經由本論文的研究探討後，相關未來研究如下：
在車輛模型／參數未知的狀況下，本論文所設計之車輛動態估測系統僅適用於 車輛輪胎尚未抬起之前，假如車輛輪胎受到各種因素而抬離地面時，車輛動態 估測系統將會失效而無法獲得目前車輛動態資訊，然而車輛輪胎抬離地面並不 代表車輛未來必定翻覆。因此為了補齊缺陷，仍必須拓展車輛動態估測系統之 適用範圍。
本論文所設計之車輛參數鑑定系統，除了要提高訊噪比，還可以適當地挑選權 重函數以增加收斂速度，然而權重函數必須要根據觀察性程度來設計，這將可 以組成一個最佳化問題。
本論文所設計之車輛參數鑑定系統，其中車體質量慣性矩之鑑定深受訊噪比之 影響，從本論文的模擬結果與討論可以知道，假如可從質量變化來評估車體質 量慣性矩，其鑑定精度將有可能會大幅提高，然而相關之計算方式仍需要分析 其可行性。
本論文所設計之車輛參數鑑定系統，其中輪胎驅動與轉向剛性係數所組成的線 性輪胎模型無法描述車輛翻覆的情況，因而需採用非線性輪胎模型（如Pacejka 輪胎模型[57][58]、Dugoff 輪胎模型[84][85]…等）來描述更多情況的車輛動態
（如車輛翻覆、雪地行走、側向滑動…等），並透過最小平方法與其他參數鑑 定方式即可獲得非線性輪胎模型之參數，然而此方法之可行性仍需藉由實驗數 據來分析。
本論文所設計之車輛軌跡跟隨系統為應用未來車輛動態資訊的車用控制系統之 一，由於車輛動態預測系統所提供之未來車輛動態資訊十分充足，因此針對車 輛各種安全與操控之車用控制系統皆可以使用未來車輛動態資訊，然而各式車 用控制系統使用未來車輛動態資訊之優缺點仍需進一步探討與分析。
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附錄 A：擴增卡曼濾波器
擴增卡曼濾波器[42]可以間接從帶有雜訊的量測值來獲得系統狀態，尤其對於雜訊 來源為高斯雜訊時，卡曼濾波器可以得到最小化的均方誤差（Mean Square Error）。然 而卡曼濾波器僅適用於線性系統，因而為了適用於非線性系統，必須將非線性系統線 性化以套用於卡曼濾波器，此方式的卡曼濾波器又稱為擴增卡曼濾波器（Extended Kalman Filter）。
考慮一個離散的非線性系統：
k kk
k k k
h f
v x y
w x x
1 (A.1)
其中x 表示為系統狀態於時間k t 的數值，k xk1表示為系統狀態於時間t 的數值，k1 y 為k 系統輸出於時間t 的數值，k f
與h
分別為離散且非線性的系統動態方程式與系統輸 出方程式，w 與k v 分別為系統雜訊與輸出雜訊，其被假設為互無關聯的高斯隨機雜k 訊，且其平均值為零，其協方差矩陣（Covariance Matrix）如下所示：
kT j k
k T j k
R v v
Q w w
E
E (A.2)
先對非線性系統作線性化的動作：
k k
k k
x x x h
x x x f
x H
x A
(A.3)
假如動態系統具有觀察性，針對此系統的卡曼濾波器可以被寫為下式：