## 5 Conclusions  
The critical faults have high potential to threaten the safety of automotive CPS. In particular, the transient faults in the anomalous data causes the unexpected behavior of the system during operation; however, their effects might not appear immediately. In this paper, in order to drive CPS into the \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Velocity**} & \multirow{2}{*}{**Type**} & \multirow{2}{*}{**AT**} & \multirow{2}{*}{**KT**} & \multirow{2}{*}{**RT**} \\ \cline{3-4} \cline{5-5}  & & **Mean(FNR, FPR)** & & **Mean(FNR, FPR)** \\ \hline \multirow{3}{*}{0.7 m/s} & Constant & (0.401, 0) & (0.456, 0) & (0.664, 0.636) \\  & Random & (0.210, 0) & (0.245, 0) & (0.515, 0.640) \\  & Noise & (0.000, 0) & (0.114, 0) & (0.195, 0.646) \\ \hline \multirow{3}{*}{1.0 m/s} & Constant & (0.078, 0) & (0.422, 0) & (0.861, 0.690) \\  & Random & (0.121, 0) & (0.278, 0) & (0.515, 0.648) \\  & Noise & (0, 0.001) & (0.001, 0) & (0.240, 0.644) \\ \hline \multirow{3}{*}{1.3 m/s} & Constant & (0.260, 0) & (0.438, 0.278) & (0.785, 0.650) \\  & Random & (0.195, 0.001) & (0.416, 0.278) & (0.515, 0.649) \\ \cline{1-1}  & Noise & (0.001, 0) & (0.306, 0.245) & (0.276, 0.647) \\ \hline \multirow{3}{*}{1.6 m/s} & Constant & (0.026, 0) & (0.285, 0) & (0.709, 0.632) \\ \cline{1-1}  & Random & (0.018, 0) & (0.192, 0) & (0.539, 0.635) \\ \cline{1-1}  & Noise & (0, 0) & (0.014, 0) & (0.367, 0.642) \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The false negative rate (FNR) and false positive rate (FPR) of the experimental results for all scenarios are averaged. stable state and to perform the operations of automotive CPS safely by detecting transient faults, we have developed a novel way to determine the adaptive threshold as the equilibrium point between competing variables. In the experimental evaluation using the real UGV data, our threshold yields a significantly better accuracy, recall, and precision than RT and KT. Against noise faults, AT shows nearly 100% accuracy, recall, and precision at each of the operation, regardless of the intensity and duration of faults. Under the constant faults, it achieves the accuracy from 85.4% to 100%, the recall of 100% from the lowest 54.2%, and the precision of 100%. AT also gives the accuracy of 100% from the lowest 83.2%, the recall of 100% from the lowest 43.8%, and the precision of 100% against random faults. In this regard, we have demonstrated that the proposed generation method enables adaptive thresholds to enhance the adaptability and sensitivity to the various environments and modeling errors and achieves a high level of detectability simultaneously in fault detection. Since our method is based on the model-based detection that performs both residual generation and evaluation, the dynamics of the targeted physical system as one of automotive CPS is first modeled and is integrated into the Kalman filter to estimate the state of the physical system and generate the residual. Furthermore, we design a structure that adds the adaptive threshold pool to the basic structure of the model-based detection and extracts the pair of thresholds from it according to the referenced velocity. In order to cope with fluctuations caused by noise, environment uncertainty, and modeling uncertainty, the normality of the physical system needs to be considered with some flexibility and it also needs to find out optimally how to determine an adaptive threshold which plays an important role in distinguishing faults from anomalous data. In our approach, since the normality cannot be obviously defined due to lack of prior knowledge of the fault occurrence, we should consider many different possibilities of normality by examining the residual under normal driving conditions. By statistical analysis, the normality is assessed in order to define the normal and anomalous states from the data obtained under the normal operation of automotive CPS.