# Integrating Gear Shifting Preference into Personalized Shift-Scheduling Calibration
## 4 Numerical Experiments and Results
### Effects of Driving Preference
Then the shifting behaviors are evaluated via the statistics on acceleration and deceleration. It is clear that the mean acceleration and deceleration of the calibrated shifting behaviors are closer to the raw data than the uncalibrated behaviors, despite the driving styles. Furthermore, there is a clear decrease trend in the mean acceleration and deceleration for the raw shifting data and calibrated data from the aggressive to the mild drivers. Thus, in a sense, this could also prove that the calibrated shifting behaviors could retain the original shifting preferences for MT drivers. Figure 10: Gear indications of three drivers: Before and after calibrations. (**a**) Aggressive driver; (**b**) Moderate driver; (**c**) Mild driver. In addition, the shifting performance statistics of three calibrated shift schedules are listed in Table 6. The performance index (PI) was cut down from benchmark (set as 1) to 0.9477, 0.9591, and 0.9755 for the three styles. Apparently, the PI got enhanced for all of the three styled robot drivers, both in shift quality (SQ) and fuel consumption (FC). In Figure 12, the base shifting map and the calibrated shifting schedules of the three styled MTDM and ATVM are illustrated, in which label '1\(\rightarrow\)2' denotes upshift of 1 th gear to 2 nd gear, while '6\(\rightarrow\)5' denotes downshift of sixth gear to fifth gear, and so on. For the shift schedules in Figure 10, most of the calibrated shift points in low TP openings are close to the base map. While in the large TP openings, the upshift and down shift curves are on the left side and the right side of base lines, respectively. This could be explained by the reason that, in large TP openings, the early upshifts and delayed downshifts could \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Index**} & \multicolumn{2}{c}{**Aggressive No.1**} & \multicolumn{2}{c}{**Moderate No.1**} & \multicolumn{2}{c}{**Mild No.1**} \\ \cline{2-7}  & **Before** & **After** & **Before** & **After** & **Before** & **After** \\ \hline FC & 3162.3 & 3032.5 & 3012.8 & 2978.6 & 2912.2 & 2885.7 \\ SQ & 365.9 & 342.7 & 346.1 & 321.7 & 308.7 & 296.4 \\ PI & 1 & 0.9477 & 1 & 0.9591 & 1 & 0.9755 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Performance index comparison among three styled drivers. Figure 11: Gear counts and shifting performance of 12 drivers: Before VS after calibration. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Driver ID**} & \multicolumn{2}{c}{**Mean Acceleration for Upshifts (m/s2)**} & \multicolumn{2}{c}{**Mean Deceleration for Downshifts (m/s2)**} \\ \cline{2-7}  & **Raw** & **Before** & **After** & **Raw** & **Before** & **After** \\ \hline Aggr1 & 1.228 & 1.065 & 1.332 & 0.534 & 0.419 & 0.522 \\ Aggr2 & 1.302 & 1.182 & 1.236 & 0.612 & 0.487 & 0.607 \\ Aggr3 & 1.352 & 1.195 & 1.378 & 0.589 & 0.367 & 0.617 \\ Mode1 & 0.746 & 0.589 & 0.814 & 0.350 & 0.281 & 0.373 \\ Mode2 & 0.795 & 0.572 & 0.842 & 0.432 & 0.335 & 0.448 \\ Mode3 & 0.839 & 0.645 & 0.881 & 0.419 & 0.316 & 0.435 \\ Mild1 & 0.537 & 0.488 & 0.563 & 0.227 & 0.167 & 0.243 \\ Mild2 & 0.521 & 0.451 & 0.542 & 0.284 & 0.245 & 0.275 \\ Mild3 & 0.492 & 0.379 & 0.502 & 0.316 & 0.260 & 0.349 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Statistics of acceleration and deceleration among three styled drivers. reduce the fuel consumption. Specifically, according to Figure 12a, the shift schedules of aggressive driver (marked in red dotted lines) tend to shift earlier than most of the base map, which are also in accordance with his/her shift preference, that is, anxious to shift the gears. While in Figure 12b, a delayed shift strategy is adopted by the aggressive driver to retain more velocity. The experimental results manifest that it is feasible to retain and incorporate the driving preferences into the automated shift-scheduling calibration via PSO and the robot driver models through the evaluations.