###### Abstract  
The development of intelligent transportation systems (ITS), vehicular ad hoc networks (VANETs), and autonomous driving (AD) has progressed rapidly in recent years, driven by artificial intelligence (AI), the internet of things (IoT), and their integration with dedicated short-range communications (DSRC) systems and fifth-generation (5G) networks. This has led to improved mobility conditions in different road propagation environments: urban, suburban, rural, and highway. The use of these communication technologies has enabled drivers and pedestrians to be more aware of the need to improve their behavior and decision making in adverse traffic conditions by sharing information from cameras, radars, and sensors widely deployed in vehicles and road infrastructure. However, wireless data transmission in VANETs is affected by the specific conditions of the propagation environment, weather, terrain, traffic density, and frequency bands used. In this paper, we characterize the path loss based on the extensive measurement campaign carrier out in vehicular environments at 700 MHz and 5.9 GHz under realistic road traffic conditions. From a linear dual-slope path loss propagation model, the results of the path loss exponents and the standard deviations of the shadowing are reported. This study focused on three different environments, i.e., urban with high traffic density (U-HD), urban with moderate/low traffic density (U-LD), and suburban (SU). The results presented here can be easily incorporated into VANET simulators to develop, evaluate, and validate new protocols and system architecture configurations under more realistic propagation conditions. v 2024244334  
urban, suburban, rural, and highway, among others. The integration of ITS, VANETs, AD, AV, and V2X with the URLLC scenario has rendered drivers and pedestrians more aware of the need to improve their behavior and decision making in adverse traffic conditions by enabling them to exchange information from different types of sensors widely deployed in vehicles and road infrastructure. However, the wireless data transmission in these systems is affected by the specific conditions of the propagation environment, including the weather, orography, traffic density, and frequency bands used. The future design of vehicle communication systems has received considerable attention in recent years from the automotive industry, government, and the scientific community as the integration of vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I). These are aimed at proposing better vehicle-reliable safety applications based on DSRC [2], which require high reliability in the connectivity between V2V and V2I communications. In this sense, various types of sensors such as cameras, radars, and speed detectors, among others, have been implemented to support the development of AD and improve mobility conditions in cities and on the road network [4; 7; 8]. For example, in [4], an integrated sensing and communication system (ISAC) was proposed based on a path loss prediction approach to improve the knowledge of wireless data transmission in vehicular networks. The approach predicts the end-to-end path loss distribution using multimodal data collected by millimeter wave (mmWave) radars, laser radars, and cameras. The important role of sensor fusion in intelligent transportation systems was described in [7]. This paper provides a comprehensive overview of the capabilities, the impacts, the planning, and the technological challenges of AVs. In [8], the author introduced several V2X use cases for autonomous driving, where cooperative autonomous driving is categorized into two types: cooperative sensing and cooperative decision. Cooperative sensing focuses on the exchange of sensor information between V2V and V2I. This will further enhance road safety, reduce traffic congestion, and improve travel comfort in urban environments [9; 10].