## 1 Introduction
In our view, such sublayers--compiled, e.g., from data gathered in car-based data collection campaigns--could give useful hints to road authorities and administration as to where to place additional TSs and auxiliary signs or remove unnecessary existing ones. Herein, however, we stick to the route-based sampling of the TSs of the urban area, the map-based processing touched upon above will be addressed in further research. In [25; 26], the urban road environments were categorized into three RETs, namely, into downtown (Dt), residential (Res), and industrial/commercial (Ind) areas. The ROs represented in the object-log were the TSs and CRs encountered along the route. In an advantageous implementation foreseen, both the TS and the CR data originate from their respective dedicated ADAS subsystems. While in case of the TS data, the corresponding subsystem, i.e., the TSR ADAS subsystem, is quite common in recent production cars, the CR detection ADAS function is fairly uncommon at this point of time. It is expected though that in the coming years, the LiDAR sensors developed for automotive applications will pave the way for the spread of such an ADAS subsystem. A good insight in ADAS system architectures, various ADAS subsystems and functions, as well as the respective methods and computations involved is given in [30]. A survey on TSR methods and systems is given in [31], while in [32], a mapping and navigation system developed for large-scale global positioning-denied sites is introduced. The system is capable of detecting CRs, intersections, and other road infrastructure. The static RO-based urban RET detection approach proposed in [25], and some further approaches make use of a variety of classification and change detection (CD) methods known from the statistical inference literature. In the cited paper, it is presumed that the static ROs in general, and the considered TSs and the CRs, in particular, occur along the route according to an inhomogeneous discrete-variable binomial process. The minimum description length (MDL) methodology is then applied to detect and locate change in the character of the road environment sweeping past the ego-car. The lane-keep assist ADAS and the lane following autonomous driving (AD) subsystems, which perforce continually identify the current and neighboring lanes, and estimate their widths, as well as the TSR ADAS and AD subsystems, which locate, identify, and track the TSs encountered by the ego-vehicle, are of particular interest in the context of RET detection. First, such ADAS subsystems are already available on-board many production cars, second, the categorical and spatial distribution of TSs, as well as, the lane-widths and the number of lanes--in the current cross-section of the road or in an aggregated form (e.g., average lane-width, average number of lanes)--carry information that can be useful in determining the RET of the given urban area. It should be emphasized that a timely feedback of the RET information to the above ADAS and AD subsystems could increase their effective processing speed and lower the rate of misclassifications via setting practical parameter constraints for the computations involved. Such constraints could be of geometrical nature and could take the forms of Boolean, probabilistic, and fuzzy regions-of-interest (ROIs), respectively, e.g., within image frames of video sequences [33]. While in case of point clouds, volumes-of-interest, again meant in a Boolean, in a probabilistic, and in a fuzzy way, respectively, could be marked and used [34]. As a further application of such reciprocal information, the characteristic size range of TSs--for a given RET--could be used for validating the detected TS candidates [35]. Similar processing benefit could be gained from the above outlined information feedback in case of other presently not so wide-spread driver assistance functions, such as the CR detection. Furthermore, information on the current RET is also important for suggesting/choosing appropriate vehicle speed and acceleration/deceleration for the ego-car.