# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 2 State of Art about Collective Perception for Connected and Autonomous Vehicle
### Data Selection and Fusion Aspect  
CP over wireless networks cannot depend on the exchange of raw sensor data. This is because the limited availability of communication resources, which is a fundamental factor to justify the need to select or fuse data before transmission. Therefore, raw sensor data must be effectively combined to save storage space and transmission resources. Previously, some strategies [36; 37; 38] were presented for handling and processing such raw sensor data. In this sense, data fusion approaches may integrate shared perceptual information provided by CP applications to increase the accuracy of discovered items. In addition to minimizing resource consumption, these strategies enhance the identification of impediments in the driving environment via integrating multi-sensor data and raising the navigational safety of autonomous cars [17; 39].  
Data fusion using a large set of redundant information leads to an overconfident estimate, and thus it is important to use adaptive methods to select the information to be fused [36; 38]. As CP techniques rely on broadcast data without universally interpretable object identifiers, participating CAVs must use data binding to sort and organize the received data before merging computations. In addition, it is crucial to examine the geographical and temporal significance of the item that will be used to integrate the data and its effect on the produced findings [10].  
[PERSON] et al. [36] studied the joint problem of associating vehicles, allocating resource blocks (RBs), and selecting the content of the exchanged CPMs to maximize the vehicles' satisfaction in terms of the received sensory information. The authors consider a quadtree-based point cloud compression mechanism to analyze a reinforcement learnings (RLs)-based vehicular association, RB allocation, and content selection of CPMs.  
[PERSON] et al. [40] proposed and evaluated a unified CPs framework based on vehicle-to-vehicles (V2 Vs) connectivity. The authors presented a generalized framework for decentralized multi-vehicle CPs, as well as a systematic analysis of the framework's inherent limitations and opportunities. The framework must take into account the ad hoc and random nature of traffic and connectivity, providing scalability and robustness to work at various traffic densities and scenarios.  
[PERSON] et al. [37] studied the split covariance intersection filter, which is a method capable of handling both independent and arbitrarily correlated estimates and observation errors. Various filtering solutions based on the covariance intersection filter or split covariance intersection filter were presented.  
[PERSON] et al. [38] created a perception and localization system to allow a vehicle with low-fidelity sensors to incorporate high-fidelity observations from a vehicle in front of it, allowing both vehicles to operate autonomously. The system shares observed vehicle tracks and their associated uncertainties, as well as localization estimates and uncertainties. The associated measurements are then fused in an extended Kalman filter to provide high-fidelity estimates of tracked vehicle states at the current time.  
[PERSON] et al. [41] presented a CPs scheme with deep reinforcement learning to improve detection accuracy for surrounding objects. The presented scheme reduces network load in vehicular communication networks and improves communication reliability by using deep reinforcement learning to select the data to transmit. The authors also demonstrated the collective and intelligent vehicle simulation platform, which was used to design, test, and validate the CPs scheme.  
[PERSON] et al. [10] presented a dedicated system design with a custom data structure and light-weight routing protocol for easy data encapsulation, fast interpretation, and transmission. The authors also proposed a comprehensive problem formulation and an efficient fitness-based sorting algorithm to choose the most valuable data to display at the application layer.