## 5 Results and Analysis
### Discussion  
This paper introduces an innovative methodology aimed at providing a more generalized and adaptable approach for designing rules driving the adaptive strategies of A-HMIs. The primary objective of this methodology is to design an A-HMI architecture geared towards reducing driver distraction. Within this architecture, the A-HMI effectively manages application requests through a request-response mechanism. Various applications submit requests for driver interaction, and these requests are overseen by the IMS. The IMS employs a combination of rules-based strategies that allow decisions capable of minimizing driver distraction. These strategies could encompass Limiting, Simplifying, Filtering, Delaying, Activating, Advancing, Supplementing, and Augmenting, among others [26]. Such rules-based strategies are implemented using truth tables. These truth tables take as \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Output (Action)** & **Value** & **Output (Action)** & **Value** \\ \hline system interaction & 0 [-] & volume increase & 0 [-] \\ turn indicators & 1 [-] & phone call & 0 [-] \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Output vector for Scenario 2. Figure 6: Solutions for Scenario 3: black dots are feasible outcomes; red dot is the optimal output. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Output (Action)** & **Value** & **Output (Action)** & **Value** \\ \hline system interaction & 0 [-] & volume increase & 1 [-] \\ turn indicators & 1 [-] & phone call & 0 [-] \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Output vector for Scenario 3.  
input requests of the applications and the DVE parameters, generating action-management decisions as output. DVE parameters are assessed through a DVE state recognition and monitoring system, which processes data from in-vehicle sensors and cameras. It is this dependence on the current DVE state that contributes to endowing the strategies with adaptability [37]. At the core of the proposed methodology to design rules for adaptivity strategies lies the application of multi-objective optimization. Using this optimization methodology, this study is focused on designing a truth table specifically tailored for a Filtering strategy. According to this strategy, the truth table has been designed for optimizing attentional demands to mitigate overload or underload. The overarching goals of this optimization endeavor were twofold: to minimize the driver's cognitive workload and reduce actions queuing. To achieve these objectives, a crucial component of the optimization process was the comprehensive evaluation of how requests of applications impact the driver's cognitive load and the queue of pending actions. To facilitate this assessment, two indexes were introduced, grounded in the actual state of the DVE and requests of the applications. These indexes serve as invaluable tools for estimating the driver's cognitive workload and the queue of pending actions throughout the optimization procedure. The proposed approach has undergone evaluation through a case study, with the objective of formulating a truth table tailored for scheduling five application requests: FCW, system interaction, turn indicators, volume adjustment of the infotainment system, and phone calls. These requests are inputs of the truth table, together with the five DVE parameters assessed via the DVE state recognition and monitoring system. Additionally, the IMS decision-making process takes into account the length of the action waiting queue, as represented by the parameter LQue. Collectively, these elements contribute to the development of a truth table characterized by 11 inputs, each with discrete values within the Boolean domain. Notably, this truth table encompasses \(2^{11}=2048\) potential driving scenarios. It is important to note that, considering the simplifications outlined in Section 5.1, the truth table condenses to encompass \(1+2^{9}=513\) feasible driving scenarios.