Connected and Automated Vehicles (CAVs) utilize a variety of onboard sensors to sense their surrounding environment. CAVs can improve their perception capabilities if vehicles exchange information about what they sense using V2X communications. This is known as cooperative or collective perception (or sensing). A frequent transmission of collective perception messages could improve the perception capabilities of CAVs. However, this improvement can be compromised if vehicles generate too many messages and saturate the communications channel. An important aspect is then when vehicles should generate the perception messages. ETSI has proposed the first set of message generation rules for collective perception. These rules define when vehicles should generate collective perception messages and what should be their content. We show that the current rules generate a high number of collective perception messages with information about a small number of detected objects. This results in an inefficient use of the communication channel that reduces the effectiveness of collective perception. We address this challenge and propose an improved algorithm that modifies the generation of collective perception messages. We demonstrate that the proposed solution improves the reliability of V2X communication and the perception of CAVs.

Automated vehicles make use of multiple sensors to detect their surroundings. Sensors have significantly improved over the years but still face challenges due to the presence of obstacles or adverse weather conditions, among others. Cooperative or collective perception has been proposed to help mitigate these challenges through the exchange of sensor data among vehicles using V2X (Vehicle-to-Everything) communications. Recent studies have shown that cooperative perception can complement on-board sensors and increase the vehicle’s awareness beyond its sensors field of view. However, cooperative perception significantly increases the amount of information exchanged by vehicles which can degrade the V2X communication performance and ultimately the effectiveness of cooperative perception. In this context, this study conducts first a dimensioning analysis to evaluate the impact of the sensors’ characteristics and the market penetration rate on the operation and performance of cooperative perception. The study then investigates the impact of congestion control on cooperative perception using the Decentralized Congestion Control (DCC) framework defined by ETSI. The study demonstrates that congestion control can negatively impact the perception and latency of cooperative perception if not adequately configured. In this context, this study demonstrates for the first time that the combination of congestion control functions at the Access and Facilities layers can improve the perception achieved with cooperative perception and ensure a timely transmission of the information. The results obtained demonstrate the importance of an adequate configuration of DCC for the development of connected and automated vehicles.

The emergence of connected automated vehicles and advanced V2X applications and services can challenge the scalability of vehicular networks in the future. This challenge requires solutions to reduce and control the communication channel load beyond the traditional congestion control protocols proposed to date. In this paper, we propose and evaluate the use of V2X message compression to reduce the channel load and improve the scalability and reliability of future vehicular networks. Data compression has the potential to reduce the channel load consumed by each vehicle without reducing the amount of information transmitted. To analyze its potential, this paper evaluates the compression gain of three compression algorithms using standardized V2X messages for basic awareness (CAMs), cooperative perception (CPMs) and maneuver coordination (MCMs) extracted from standard-compliant prototypes. We demonstrate through network simulations that V2X message compression can reduce the channel load. In particular, the tested compression algorithms can reduce the channel load by up to 27% without reducing the amount of information transmitted. Reducing the channel load and the consequent interferences significantly improves the reliability of V2X communications. However, this study also emphasizes the need for high-speed compression and decompression modules capable to compress and decompress V2X messages in real time, especially under highly loaded scenarios.

Automated Vehicle Marshalling (AVM) is an innovative technology poised to transform the automotive industry by enabling automated vehicles to be wirelessly controlled within geofenced areas while ensuring guaranteed Functional Safety (FuSa). Significant investments from major automakers and suppliers are driving the advancement of this SAE Level 4 driverless technology. Standardization is a crucial prerequisite for the widespread deployment of AVM, requiring collaboration among academia, international standardization bodies (e.g., ISO, ETSI, SAE), and industry consortia such as VDA and 5GAA. This paper outlines the current standardization efforts and deployment status of AVM, elaborates on core vehicle motion control mechanisms, FuSa principles, communication interfaces, message formats, and spectrum requirements. Through this comprehensive examination, the paper aims to address how AVM can be seamlessly integrated into future Intelligent Transportation System (ITS) ecosystems. As the automotive industry progresses toward greater automation and connectivity, AVM represents a major advancement in automated vehicle maneuvering and control for manufacturing plants, logistics depots, parking facilities, and charging stations.

Analysing shortest path for real time traffic environment is crucial with dynamic updating. VANET technology can be used for analysing traffic but generates a huge amount of data to be exchanged, which demands more processing power and resources. In this paper, a new histogram-based route guidance algorithm (HBA) has been proposed based on light weight processing. The proposed algorithm enables selecting the shortest path between any source and destination using the histogram models, which capture the higher order distribution function of the number vehicles in every lane. Furthermore, the histogram model is used to estimate the traffic delays at intersections and roundabouts. The data entity collection through sensors used for histogram modelling is presented in detail. The experimental results show that the proposed algorithm provides a good prediction of road traffic status and a better solution for the congestion problem in the urban areas.