In 2022 most fatalities involving heavy-duty trucks occurred on rural roads, where the available space is typically narrower. The increased size and the resulting larger blind spots of heavy-duty trucks present new challenges for truck drivers in comparison with passenger cars. For this reason, the detection of free space around the vehicle is of critical importance, as it has the potential to save lives and reduce operating costs due to maintenance and downtime. Despite the existence of numerous approaches to free space detection in the literature, few of these have been applied to the trucking sector. Therefore, the objective of this paper is to develop a methodology for defining the characteristics of a free space detection function, especially when applied to trucks. The initial task of this paper is to identify relevant use cases based on an analysis of the most critical accidents and common driving situations that truck drivers encounter, with a particular focus on a potential contribution of free space detection to assist the driver. Once the use cases are identified, they are then grouped together based on their similarity. Subsequently, relevant features from each cluster are extracted and afterwards combined and examined in order to derive different scenarios. By analyzing these, the requirements to be fulfilled by a free space detection function are defined, including an application to trucks. To verify these requirements, a Mercedes Actros equipped with different ground truth sensors is used as a research vehicle. Finally, the initial results of our research, a Not Free Space Warner, a driving assistance function with the goal of its implementation in trucks, are presented, along with the limitations and challenges associated with its realization in the context of trucks.