# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 5 Open Issues about Collective Perception  
We identify four research issues (i.e., redundant information, perception fusion and dissemination, privacy and security) from the suggested CP architecture for CAVs based on our analysis of the relevant literature. In this way, application-level control of the detected objects transmission often lacks modeling utilizing sophisticated V2X communication models. In the case of multiple CAVs perceiving the same object \(o_{j}\), redundant and unnecessary frequent updates about that object will be sent in the CPM, thereby increasing the network channel load. The high channel load may lead to frequent losses of CPMs, which may in turn degrade the performance of the CP service. In order to reduce the message size, the CP service may optionally omit a subset of the perceived objects that meet pre-defined redundancy mitigation rules. However, studies rarely use a precise V2X communication simulation model to evaluate redundant information from several CAVs. Consequently, V2X communication modeling must be performed with care, as redundancy mitigation techniques may be susceptible to the loss of CPM due to radio propagation or interference effects, and locally perceived objects may be omitted from the new CPM, even if a remote CAV fails to receive a particular historical CPM containing object information. The data load created by CP is primarily governed by the frequency with which messages are generated and the number of objects they convey. The former affects the delay between two consecutive CPM, while the latter affects the vehicle's environmental awareness. When messages are transmitted excessively often, they increase the channel's strain while enhancing perception only modestly. However, if they are created rarely, they have little effect on local perceptive skills. Setting a lower or higher limit for the minimum or maximum value is an option for specifying a preset CPM generating frequency. Furthermore, most techniques presume the correctness of the data linkage between local perception objects and those received through CPMs. Consequently, improper object information or the wrong omission of object information might reduce the performance of the CP service. Another future research should compare the different factors that affect low latency and true fusion to find a good balance between the two. Due to sensor preprocessing and communication flaws, direct data matching is impossible due to spatial and temporal deviations or false positives or negatives in sensor data. It must be fused so the CAV planning component can use it. There are two major types of sensors in CP for CAVs: (i) onboard sensors, which are mounted directly to the ego vehicle, and (ii) shared sensors, which are sensor information shared from other vehicles or RSUs. The fusion time is proportional to the input format of the data to fuse, the fusion method used, the output format, and the accompanied object detection and tracking method. It should be noted that incorporating tracking mechanisms into fusion can improve object detection and fusion but slows down the fusion process itself. An essential task of the data fusion mechanism is to add state spaces to the list of objects currently perceived by a CAV in case a sensor detects a new object. In addition,  
Figure 7: Absolute number of objects detected with collective perception. the data fusion system must develop a data structure for storing detected objects and updating previously tracked objects with new measurements associated with a previously monitored object. Finally, if new measurements are not related to previously monitored items, the system must delete those objects from the list of tracked objects. The data fusion mechanism predicts each object to timestamps at which no measurement is available from sensors. In addition, the data fusion mechanism associates objects from other potential sensors mounted to the station or received from other CAVs with objects in the tracking list.