## 5 Results and Analysis
### Discussion
Furthermore, for each distinct DVE system state and queue length, there exist \(2^{5}+2^{4}+\ldots+2^{0}=63\) plausible outcomes concerning the scheduling of the five actions. Consequently, the total cases to be investigated for optimizing the 513 possible outcomes of the truth table amount to \(2^{4+1}\big{(}2^{5}+2^{4}+\ldots+2^{0}\big{)}=2016\). The study's results theoretically demonstrated the remarkable capability of the proposed optimization framework in automatically addressing this large amount of feasible driving scenarios. This capability has enabled the derivation of an effective strategy for prioritizing actions requested by applications. Compared to existing methodologies documented in the technical literature [25; 28; 39], the proposed approach has significantly streamlined the design of rules driving the Filtering strategy within the A-HMI architecture. Notably, it has endowed the system with adaptive capabilities, effectively mitigating driver cognitive overload. Indeed, many known approaches for designing predefined rules for adaptive strategies heavily rely on empirical methods, leaning on the competences of expert pools [43]. However, when facing a large number of possible driving scenarios and in-vehicle applications to be managed, this conventional approach proves to be complex and time-consuming. The process of deriving truth tables, in particular, escalates in complexity as the number of DVE parameters and application requests to be managed increase. More specifically, the total cases to be investigated for optimizing the truth table exponentially increase with these two variables. This complexity issue is compounded by the sheer volume of potential driving conditions and the extensive data handling necessitated by new ICVs [16]. In stark contrast, our proposed optimization approach significantly simplifies the rule design process for the Filtering strategy, making A-HMI development within the context of ICVs automatic and less labor-demanding. Furthermore, our methodology facilitates the formulation of comprehensive strategies to effectively manage the diverse tasks associated with the information management process [41]. It is noteworthy that the multi-objective optimization procedure has explored all combinations of inputs, i.e., DVE states and application requests. Specifically, for each of the 513 input combinations, an optimal solution set of 4 decision variables, each with value in the Boolean domain, hasbeen determined. Each decision variable corresponds to one of the application requests and dictates whether the corresponding action is executed or not. Consequently, the outcome of our optimization process is a vector of decision variables with a dimension of \(\mathbf{X}\in\mathbb{B}^{4\times 513}\). In contrast, when adhering to conventional empirical approaches, obtaining comprehensive strategies to cope with all diverse tasks involved in the information management process is not a trivial task [42]. This issue accentuates a well-recognized issue of rules-based strategies. Specifically, the brittleness of rules-based A-HMIs becomes evident when faced with missing or unforeseen input values, which can compromise the system's adaptability and robustness. The main limitation of the proposed methodology pertains to the user-specific characteristics of the optimized truth table. It is essential to recognize that the truth table should exhibit adaptability over time to accommodate changes in a driver's experience and the effects of aging. Indeed, age differences represent one of the most significant factors influencing driver workload. The aging process induces a deceleration in information processing across perceptual, cognitive, and psychomotor domains for older drivers [64]. Consequently, when processing an equivalent amount of information within a defined time period, older drivers typically experience higher levels of mental workload in contrast to their younger counterparts.