# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 2 State of Art about Collective Perception for Connected and Autonomous Vehicle
### Privacy and Security Aspect  
As soon as the data leave the vehicle, it is important to guarantee that any personal data are processed lawfully. Some works [42; 43; 44] are still evaluating how to comply with data protection regulations in CP applications. CP deals with mobility and sensor data, and thus, monitoring CAVs might breach users' privacy. Furthermore, relying on CP applications for continuous and comprehensive environmental data may generate privacy problems [17; 32].  
Perception data would show the movements and actions of the driver, who is not necessarily the vehicle owner. In this sense, broadcast CPMs with user location information may lead to a privacy risk for both the car owner and the driver [17; 32]. However, privacy issues may be addressed if the information is filtered, anonymized, and aggregated (for anonymity and data quality reasons) by trustworthy back-end systems before being shared with other parties. [PERSON] et al. [42] introduced a safety metric for object perception that incorporates all of these parameters and returns a single easily interpretable safety assessment score. This new metric is compared to state-of-the-art metrics using real-world and virtual data sets. The proposed metric uses responsible-sensitive safety (RSS) definitions to identify collision-relevant zones and penalizes hidden collision-relevant objects. The RSS model consists of 34 definitions of various safety distances, timings, and procedural rules that mathematically codify human judgment in various traffic conditions. RSS rules specify the proper behavior of an autonomous vehicle and provide a mathematical description of safe conduct.  
[PERSON] et al. [45] investigated and demonstrated, using representative experiments, how a CAVs achieves improved safety and robustness when perceiving and interacting with RSUs, using CPMs information from an intelligent infrastructure in various traffic environments and with different setups. The authors showed how CAVs can interact with walking and running pedestrians autonomously and safely using only CPMs information from the RSUs via vehicle-to-infrastructures (V2 Is) communication.  
[PERSON] et al. [43] provided a trustworthy information-sharing framework for CAVs, in which vehicles measure each other's trust using the Dirichlet-categorical model. The authors apply trust modeling and trust management techniques developed for vehicular networks to CAVs in order to achieve a trustworthy perception of information sharing on CAVs. Enhanced super-resolution generative adversarial networks are used to improve the resolution of images containing distant objects in order to allow a vehicle to assess the trustworthiness of more data transmitted from others. Based on the results of sharedobject detection, the work first crops out all distant objects from captured images. It detects potential objects by applying our super-resolution model to these blurry images.  
[PERSON] et al. [46] examined a novel approach for CAVs to communicate perceptual observations, which is tempered by trust modeling of peers providing reports. Based on the accuracy of reported object detections as verified locally, transmitted messages can be fused to improve perception performance beyond sight and a long distance from the ego vehicle. The authors proposed a method for integrating trust modeling and collective perception into an end-to-end distributed perception model, as well as the TruPercept dataset, a multi-vehicle perspective perception dataset for CAVs collected in a realistic environment.