A journey towards optimized collective perception service for vehicular networks

The rising number of vehicles on the road has become a growing concern, as it has led to an increase in fatal accidents, resulting in both the loss of human life and significant financial losses. A major contributing factor to many of these accidents is the lack of driver awareness. To address this issue, many modern production vehicles are now equipped with multiple sensors that support Advanced Driver Assistance Systems (ADAS). However, the effectiveness of these sensors is limited under certain conditions, such as severe weather, short detection ranges, and reduced accuracy, which means that fatal accidents can still occur. To overcome these limitations, Vehicular Ad Hoc Network (VANET)s have been proposed. In Europe, the European Telecommunications Standards Institute (ETSI) standardizes VANET-related safety applications. Among these, Collective Perception (CP) service is considered the most significant for enhancing driver awareness during the transition period until full market penetration of Vehicle-to-Everything (V2X) vehicles. This thesis embarks on a journey to optimize CP services by identifying key limitations in current standard implementations, ultimately reaching the destination of a fully optimized CP service designed to meet the demands of future Intelligent Transportation System (ITS) applications. To achieve this, the thesis proposes three novel optimization mechanisms, each targeting a critical aspect of the CP architecture: Collective Perception Message (CPM) dissemination infrastructure, CPM processing at the receiver, and CPM generation at the transmitter. The first proposed optimization focuses on using cost-effective mobile Road Side Unit (RSU)s with adaptive CPM dissemination to provide better performance compared to traditional static RSUs. Simulation and modeling results demonstrate that mobile RSUs significantly enhance CPM dissemination performance, including extended CPM coverage. However, as the coverage area increases, so does the number of CPM objects received at each V2X vehicle, resulting in higher processing demands. Therefore, the second optimization targets efficient processing of received CPM objects by smart filtering while maintaining situational awareness. Results show that the proposed method can filter out the majority of received CPM objects, thereby reducing processing load without compromising safety. The third optimization introduces a mechanism to prioritize V2X vehicles in CPM generation, aiming to mitigate channel congestion. This adaptive approach enables each vehicle to dynamically adjust its transmission frequency based on situational context, therefore maintaining awareness while improving network performance. Finally, this thesis integrates all three optimizations into a cohesive and fully optimized CP service, reaching the intended destination of the journey.