# Novel Motion Sickness Minimization Control via Fuzzy-PID Controller for Autonomous Vehicle
## 4 Conclusions and Future Work  
In terms of MS occurrence factors, there is an existing correlation with the steering, lateral acceleration and head roll. This correlation makes it possible to implement the usage of head roll as the controlled variable to generate an additional corrective wheel angle in mitigating MS, by reducing the lateral acceleration. It is compulsory to utilize a controller to compute the corrective wheel angle. Therefore, this study proposed the utilization of a fuzzy-PID controller for a MS minimization control strategy for an autonomous vehicle. The aim of the control structure which is inspired by an active steering system is to diminish the vehicle lateral acceleration. The reduction of the lateral acceleration will then minimize the head roll angle with respect to the lateral acceleration \begin{table}
\begin{tabular}{c c c} \hline \hline
**Participant** & **Before (\%)** & **After (\%)** & **Reduction (\%)** \\ \hline
1 & 0.003539 & 0.003417 & 3.45 \\
2 & 0.003269 & 0.003099 & 5.20 \\
3 & 0.003346 & 0.003218 & 3.83 \\
4 & 0.003431 & 0.003342 & 2.59 \\
5 & 0.003275 & 0.003159 & 3.54 \\
6 & 0.003401 & 0.003247 & 4.53 \\
7 & 0.003327 & 0.003202 & 3.76 \\
8 & 0.003527 & 0.003391 & 3.86 \\
9 & 0.003518 & 0.003375 & 4.06 \\
10 & 0.003327 & 0.003170 & 4.72 \\ Average & & & 3.95 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: MSI results. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Participant** & **Before (\%)** & **After (\%)** & **Reduction (\%)** \\ \hline
1 & 0.2140 & 0.1804 & 15.70 \\
2 & 0.1996 & 0.1787 & 10.47 \\
3 & 0.1939 & 0.1733 & 10.62 \\
4 & 0.2093 & 0.1863 & 10.99 \\
5 & 0.1874 & 0.1703 & 9.12 \\
6 & 0.2094 & 0.1848 & 11.75 \\
7 & 0.2159 & 0.1912 & 11.44 \\
8 & 0.2029 & 0.1804 & 11.09 \\
9 & 0.1951 & 0.1729 & 11.38 \\
10 & 0.2127 & 0.1865 & 12.32 \\ Average & & & 11.49 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: MSI results for ten laps trajectory. direction, thus mitigating the MSI of the passenger. The gain parameters of the fuzzy-PID controller are tuned by heuristic and PSO methods. It is necessary to use estimated head roll response instead of the real response as the controlled variable, because it is considered inappropriate to measure the occupant's head tilt movement during travelling. Thus, the RBFNN modeling method is utilized to develop prediction models to estimate both driver's and passenger's head roll responses. An experiment that can provoke MS is set to acquire data for the RBFNN modeling process. The efficiency of the proposed control system is evaluated by investigating the MSI index of ten participants using a driving simulator to represent the real autonomous vehicle. The experiment results indicated that the MSI for single and ten laps trajectories are lowered by 3.95% and 11.49% in average, respectively. Hence, it is proven that the recommended control strategy consisting of a fuzzy-PID controller managed to mitigate the passenger's MS level by reducing the lateral acceleration, as compared to the system without the controller utilization. In the future, the evaluation of the proposed control system performance can be improvised through a series of tests in different trajectories. The control system can also be improvised by redesigning the head roll prediction modeling process using various trajectories and speeds. The enhancement in the modeling process can also increase the generalization capability of the prediction models. Lastly, it is suggested that to validate the proposed control strategy, a real driving test should be performed in order to examine its effectiveness in a real driving environment. Conceptualization, M.H.M.A. and H.Z. ; methodology, N.H.A., N.W. and N.H.; software, S.A.S., N.H.A. and N.W. ; validation, S.'A.S. ; formal analysis, S.A.S., N.W. and N.H.; investigation, S.'A.S., resources, M.H.M.A., K.A.A.K. and H.Z. ; data curation, S.'A.S. and N.H.; writing--original draft preparation, S.'A.S. ; writing--review and editing, M.H.M.A. ; visualization, S.'A.S. ; supervision, M.H.M.A. and H.Z.