## 1 Introduction  
Despite of the on-going research on self-explaining road layouts and designs [1, 2], and on the computerized recognition methods of such designs and layouts, e.g., on methods that apply artificial intelligence methodology [3], setting up traffic signs (TSs) along the roads and traffic lights in road junctions and near pedestrian crossings by the transport authorities still remains a customary measure for reducing traffic safety risks in urban areas [4]. Clearly, there are other viable alternative measures, as well as supplementary onesfor the purpose. These include--among many others--the installation of speed reduction markings onto the road-surface [5] and the installation of vehicle- to-infrastructure (V2I) communication facilities, e.g., to succor the TS recognition (TSR) function offered by advanced driver assistance systems (ADAS) [6] and self-driving cars [7]. In a wider sense, V2I communication succors the road, traffic and vehicle data gathering, fusion, and dissemination, and through these data processes, it is expected to have a significant beneficial impact on traffic safety [8]. More specifically, V2I communication can be used for raising the road-awareness of car-drivers, as well as that of the intelligent and the self-driving road vehicles. Furthermore, it can be used for providing the human drivers and the smart vehicular systems with current traffic information with respect to the region, town, and area, on the one hand, and with some very specific dynamic information on individual vehicles in the vicinity, on the other [9]. When speaking about raising road awareness of drivers, one is obliged to speak about the Global Navigation Satellite System (GNSS), a system that is used by masses of people around the world. According to [10], the GNSS devices per capita averaged out at 0.8 across the countries of world in 2019. The GNSS is used with wide variety of devices running map-based applications, a significant percentage of these devices are installed on-board cars. The brief history of the navigational systems and their respective precisions are presented in [11]. The paper provides a fresh outlook on the navigational needs of and the available navigational solutions for autonomous vehicles and systems. As it often happens to popular services, devices, and applications, threats against these surface from time to time. Such threats have surfaced also against the GNSS service [12]. Although the number of successful navigational spoofing attacks is still negligible, the navigational signal deteriorations due to other--i.e., non-hostile--factors are clearly not. For instance, the signal reception is often brought down, or even blocked by the high-rise buildings in densely built urban areas. Some examples in this context are presented in [13]. The speed reduction measures implemented in urban areas are motivated by the traffic safety concerns associated with the intense road traffic and the limited space available there for the driving maneuvers [14]. While driving, and particularly while driving in urban areas, drivers need to perform numerous mental and control tasks--ranging from those associated with limb-movement to those required for complex driving maneuver planning and execution--within stringent time and spatial constraints and with high reliability [15]. Furthermore, these tasks must be performed in presence of disturbances, such as unfavorable lighting, adverse weather, and traffic conditions [16]. In addition, the older age of the driver may contribute to the perceived difficulty of these tasks [17]. A system, which pays attention to the driver's activity within the car and also to aspects of the urban road environment, was developed as part of the Urban Intelligent Assist Research Initiative some years ago [18], and since then, other systems with similar, or enhanced capabilities followed suit [19; 20].