Enhancing Vehicular Network Efficiency: The Impact of Object Data Inclusion in the Collective Perception Service

Abstract

As the automotive industry evolves, integrating intelligent technologies and cooperative services in vehicular networks has become crucial to enhance road safety and autonomous driving capabilities. However, this integration can strain networks, particularly when exchanging a high volume of object information. This work studies the impact of the Collective Perception Messages (CPMs) size on the vehicular network performance. We introduce an algorithm aimed at optimizing the efficiency of extra object data inclusion in CPMs. The focus is on evaluating the vehicular network efficiency by selectively including extra objects within the available message space, strategically enhancing the transmission of more objects. This optimization not only reduces the need for constant CPM generation, but also maximizes the efficiency of information exchange. Using real-world vehicular data, this approach's effectiveness in improving the Collective Perception Service (CPS) is demonstrated, showing a significant improvement when compared to traditional CPS standard: the proposed algorithm is capable of transmitting 14% more object information while using 2.6% fewer bytes. In addition, if we were to maintain the same number of bytes used in transmission as the CPS standard, our algorithm would result in a 23% increase in transmitted object information. Furthermore, the additional delay incurred by the algorithm is minimal, with an average of just 3 ms.