## 1 Introduction
The collected data were organized into a publicly accessible dataset that includes the measured ego-car trajectories, the raw and processed point cloud data from the LiDAR sensors, as well as the ego-car trajectories with improved precision computed via SLAM. Other approaches, e.g., the ones presented in [25; 26], rely object-level data as inputs to the urban RET detection function, i.e., they follow an indirect processing approach. In a feasible realization, the raw data streams originate from the very same sensors as in the direct case, but the respective data streams reach the RET detection subsystem only after having been processed and considerably compressed by one or more ADAS subsystem. The resulting data are an object-level description of the road environment, i.e., an RO log. This log serves as an input to the indirect RET detection function. The on-board data processing described above, as well as other road, traffic, and vehicular data processing carried out in various ADAS subsystems (e.g., lane detection, TSR, detection of nearby vehicles) can also be looked at from, analyzed with respect to, and formulated using a static reference point. Setting up and using a local dynamic map (LDM) [27] could serve these purposes, and provide additional conceptual support for the developers of ADAS functions. LDM is a widely used model for representing, and a standardized technology for integrating static, temporary, and dynamic road, traffic, and vehicular information into a static geographical context by means of a common coordinate reference. Customarily, it has four object layers describing and managing ROs that are subject to change and exhibit dynamics at different time scales. More concretely, these layers store and handle data on permanent static, transient static, transient dynamic, and highly dynamic ROs, respectively. For instance, when framing the static RO-based urban RET detection task in LDM, the ego-car is seen as a highly dynamic RO. A crossroads (CRs) intersection of streets is a permanent static RO in this model. The intersection can be associated with other ROs (e.g., with fixed traffic lights located there, which are transient static ROs). The lanes, lane markings, pedestrian crossings, and the conventional TSs are transient static ROs, while the TSs displayed by variable message sign boards can be classified as transient dynamic ROs. The RETs can be treated as permanent static features of an area or of a sub-network of roads. One could look at the ROs in an urban settlement and collect and compile their location and categorical data into a map layer, e.g., in the way data contributors of OpenStreetMap maps do with roads, railways, rivers, and various locations of importance [28]. By selecting appropriate subsets of TSs--i.e., TS subsets that are characteristic to certain RETs--various sublayers of the RO layer can be created, displayed, and analyzed. The analysis could include a Delaunay triangulation of the TS locations within a sublayer, and then one could look for dense clusters of triangles in the generated structure. By carrying out similar processing for a number of sublayers, a TS-based RET categorization of the urban area can be created. By further processing the map-based representation of TSs and other ROs, one could derive other interesting sublayers that relate to seasonal, weekly, or daily validity of the TSs and could derive a sublayer representing weather-related TSs (e.g., TSs applicable for wet, snowy and icy road conditions). For instance, the sublayer representing the within-the-day validity of TSs--indicated by auxiliary signs or time intervals attached to the TSs--should reflect the daily dynamics of traffic source and sink structure of the area [29]. Clearly, the mentioned dynamics are closely related to the RET categorization used herein.