## 5 Results and Analysis
### Results  
The number of generations in a multi-objective optimization procedure is set to cover all possible combinations of DVE states and application requests. Solutions belong to the 1026-dimensional hyperspace \(\left(\mathbf{J}(\mathbf{X})\in\mathbb{R}^{2\times 513}\right)\) constituting the Pareto frontier. Figure 3: Scheme of the physical implementation of the offline optimized truth table for the Filtering strategy in Kinecar’s A-HMI. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Coefficient** & **Value** & **Coefficient** & **Value** \\ \hline \(c\)DVE 1 & 10 [-] & \(P\)App. 2 & 5 [-] \\ \(c\)DVE 2 & 20 [-] & \(P\)App. 3 & 4 [-] \\ \(c\)DVE 4 & 10 [-] & \(P\)App. 4 & 3 [-] \\ \(c\)DVE 5 & 2 [-] & \(P\)App. 5 & 2 [-] \\ \(c\)App. 2 & 2 [-] & \(P\)Q. App. 2 & 4 [-] \\ \(c\)App. 3 & 1.5 [-] & \(P\)Q. App. 3 & 4 [-] \\ \(c\)App. 4 & 1 [-] & \(P\)Q. App. 4 & 3 [-] \\ \(c\)App. 5 & 2 [-] & \(P\)Q. App. 5 & 2 [-] \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Weights of driver workload and action queue indexes. Indeed, for each of the 513 input combinations, an optimal solution set of 4 decision variables is determined. In doing so, the output of the optimization process is a vector of decision variables of dimension \(\mathbf{X}\in\mathbb{B}^{4\times 513}\). For the sake of brevity, only some sample outputs \(\mathbf{x}_{\mu}\ \in\ \mathbf{X}\), with \(\mu\in\{1,\ldots,513\}\), are here theoretically analyzed to highlight effectiveness of the proposed procedure to derive truth tables for scheduling application requests. Specifically, solutions for three driving scenarios are presented below. * Scenario 1: the Filter and Queue module must schedule the driver request to increase the volume of the infotainment system and an incoming phone call. The decision management process is carried out, taking into account the output from the DVE state recognition and monitoring system, which infers a high driver workload level. The input vector of this driving scenario is reported in Table 4.
* Scenario 2: wipers actuation state, measurements of rain detection sensors, and microphone warn of a high level of environmental risk for both low visibility and audibility. According to these conditions, the truth table must filter two application requests, i.e., activate turn indicators and an incoming phone call. Table 5 shows the corresponding input vector. * Scenario 3: the monitoring system detects high risk in four of the five dimensions of the driver-vehicle-environment system, i.e., driver distraction, driver drowsiness, environment low visibility, and environment low audibility. The IMS addresses a complex decision management problem, where, in the high waiting queue condition, three requests are performed, i.e., system interaction, activate turn indicators, and increase the volume of the infotainment system. This driving scenario corresponds to the input vector in Table 6.  
Figure 4 shows solutions identified by the proposed optimization approach for Scenario 1 in terms of driver workload \(J_{Dw}(\mathbf{x})\) and actions queue \(J_{Aq}(\mathbf{x})\) indexes, both normalized. Among the four feasible outcomes, the optimal output is represented by a red dot in the figure, which corresponds to the output vector in Table 7. Since the monitoring system, by driver head and eye movements, detects driver distraction, the truth table should suggest to reject an incoming phone call for minimizing workload level. In contrast, to minimize the action queue, it should allow to increase the volume of the infotainment system. Similarly, Figure 5 shows, in the plane of the normalized \(J_{Dw}(\mathbf{x})\) and \(J_{Aq}(\mathbf{x})\), four possible outcomes for Scenario 2 calculated during the optimization process. In addition, here, the  
Finally, Figure 6 shows optimization process results for Scenario 3, which is a challenging scenario for the decision management process.