### A-HMIs as a Solution to Reduce Driver Distraction
#### 1.2.1 Related Work  
Drawing from a literature review, eight strategies have been commonly employed to exploit adaptive functions in A-HMIs [26]. These strategies can be categorized into two groups, each addressing different aspects of attention management. The first focuses on optimizing attentional demands to prevent overload or underload. Within this category, there are five strategies, i.e., Limiting, Simplifying, Filtering, Delaying, and Activating. The first strategy involves limiting access to functions of non-driving tasks to prevent distractions, particularly during high-demand situations [13]. In the Simplifying approach, displays and alerts are simplified to reduce workload. This simplification may entail, for example, increasing text size or combining alerts into a single summary alert [27]. In contrast, the Filtering strategy is designed to allow only critical alerts and notifications to reach the driver during demanding scenarios [28]. The delaying strategy involves postponing alerts, notifications, etc. when drivers are experiencing high workload conditions [29]. On the other hand, the last strategy is employed in situations characterized by underload, typically by initiating a non-driving task to increase driver workload [30]. The remaining three strategies, i.e., Advancing, Supplementing, and Augmenting, fall under the second category that aims to redirect the driver's focus back to the driving task. The Advancing approach involves the adjustment of alert timing and issuing alerts prior to their standard timing, shifting the driver's attention back to the driving task [31]. In contrast, the Supplementing strategy serves to complement existing warnings by supplying extra warnings when the driver's attention is compromised [32]. Finally, within the Augmenting strategy, alerts are modified, for example, utilizing different sensory modalities, to increase awareness [33]. A-HMIs generally achieve their adaptability by employing one or a combination of these strategies. For instance, in [34], a sophisticated workload management system has been implemented to suppress non-essential messages during challenging driving scenarios, allowing the driver to concentrate on resolving the situation without unnecessary distractions. An additional innovative approach for information management has been developed in [35], focusing on the scheduling of driver warning messages. This method presents a compelling solution by formulating the message scheduling as a resource-constrained scheduling problem. This problem encompasses a dynamic set of presentation requests, contending for limited resources, and incorporates modifying actions as conflict resolution strategies. These strategies include postponing, preponing, shortening, canceling tasks, and switching resources. To effectively address the scheduling problem, a transformation into a tree search problem is executed. The work presented in [36], following the blackboard design pattern, introduces an A-HMI architecture that offers an integration of multiple strategies to manipulate and adapt in-vehicle information flow, depending on the driving context. An innovative fusion strategy for messages is a key highlight of this architecture, built on a taxonomic message model. This sophisticated fusion strategyfacilitates the combination of two or more low-level messages, resulting in the creation of a higher-level message. Noteworthy research on information management can be found in [37]. It presents the Interaction and Communication Assistant (ICA) system, which is a highly advanced approach to efficiently manage information through continuous monitoring of the driver, vehicle, and environment. In the ICA system, access to Input/Output (I/O) devices is granted through a request-response mechanism. Applications interact with the ICA by submitting access requests, and in response, the ICA provides an assigned channel through a suitable reply.