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.
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