## 4 Design of an Optimization Framework for the IMS
### Driver Workload Index  
The main objective of the IMS's Filter and Queue module is to schedule application requests in order to avoid driver cognitive overload. To achieve this goal, the truth tables used in the filtering process for the prioritization of actions must be derived to minimize the driver's level of workload associated with forwarding application requests to the driver. To optimize the filtering process, a methodology to assess driving workload in several conditions is essential. Many studies have focused on evaluating the driver's resources \begin{table}
\begin{tabular}{c c} \hline
**DVE, Queue,** & DVE 1 \\ \cline{2-3}
**Application** &... \\ \cline{2-3}
**Requests** & DVE \(n\) \\ \cline{2-3}  & LQue \\ \cline{2-3}  & App. 1 \\ \cline{2-3}
**Outcome of** &... \\ \cline{2-3}
**Requests** & App. \(m\) \\ \hline App. 1 & \(f_{1}(\text{DVE 1,..., DVE $n$, LQue, App. 1,..., App. $m$}):\mathbb{B}^{n+1+m}\rightarrow\mathbb{B}\) \\ \hline... &... \\ \hline App. \(m\) & \(f_{m}(\text{DVE 1,..., DVE $n$, LQue, App. 1,..., App. $m$}):\mathbb{B}^{n+1+m}\rightarrow\mathbb{B}\) \\ \hline \end{tabular}
\end{table} 
Table 1: Truth table for prioritization of actions. allocated to a driving task [61; 62]. These have shown that driver workload has three main features. First, drivers are capable of reporting the task demands on separate workload dimensions (perceptual, cognitive, and motor dimensions) [63]. Second, age differences significantly affect driver workload [64]. Third, performance measurements may estimate workload only partially because of the potential dissociation of performance and mental workload [65]. In accordance with these three properties, a workload assessment method is expected to capture the multi-dimensional property of the cognitive load and to account for driver age differences. Among several techniques to assess the level of the driver's workload, subjective methods are the most frequently used in practice [66]: they make use of driver's reports concerning subjective judgments of the effort and expenditure that was experienced during the task [67]. The most popular subjective method is the National Aeronautic and Space Administration Task Load indeX (NASA-TLX) [68], which was originally designed for aviation pilots. It assesses the workload through six rating scales, i.e., mental demand, physical demand, temporal demand, performance, effort, and frustration levels. The Driving Activity Load Index (DALI) [69] is a technique for workload estimation obtained by revising the NASA-TLX to specifically adapt it to the car driving task. Similarly to NASA-TLX, the DALI estimation is based on six rating scales, where, however, the main factors composing the workload score were chosen to be more adapted to the car driving context. In particular, workload dimensions of the DALI are effort of attention, visual demand, auditory demand, temporal demand, interference, and situational stress. In this study, the DALI method is used to evaluate offline the workload under various complexities of the driving context. More specifically, several driving conditions are set up to induce on purpose various levels of workload for the driver, i.e., with and without secondary activities, under several different states of the driver-vehicle-environment system, and by varying application requests. During each driving condition, subjective measures are executed to assess the magnitude of the six factors on a scale. Then, the levels of workloads are quantified by comparing the sets of six factors evaluated in the tested driving conditions. In doing so, relative weights on driver workload between various DVE states \(\big{(}c_{\text{DVE }j}\), with \(j\in\{1,\ \dots,\ n\}\big{)}\) and several applications requests \((c_{\text{App.\,}i}\), with \(i\in\{1,\ \dots,\ m\})\) are computed. Identification of these relative weights is a preliminary step to calculate the driver workload index \(I_{Dw}(\mathbf{x})\).