# Redundancy Mitigation Mechanism for Collective Perception in Connected and Autonomous Vehicles
## 1 Introduction  
The operation of a connected and autonomous vehicle (CAV) is based on three layers, namely sensing, perception, and decision making [1, 2]. CAVs are equipped with an array of onboard sensors at the sensing layer, such as high-resolution cameras, RADAR, light detection and ranging (LIDAR), global positioning system (GPS), inertial measurement unit (IMU), and ultrasonic sensors [3, 4]. In this way, the sensing layer provides a vast amount of environmental and contextual data [5, 6]. This sensing capability is a prerequisite for a vehicle to perceive the surrounding environment and make proper and timely decisions to ensure the comfort and security of passengers [7, 8]. However, regardless of the technology used, onboard sensors are limited by the CAV field of view (FoV), and by obstacles from other moving cars or roadside objects. The perception layer is responsible for furnishing the vehicle with knowledge of the surrounding environment to allow it to identify and track pedestrians, animals, and other vehicles, as well as accidents and dangerous situations, among other tasks [9]. In summary, the perception layer must have the following characteristics: (i) provide precise driving environment information; (ii) work typically and not easily malfunction; and (iii) avoid processing delays that affect real-time applications (e.g., overtaking maneuvers) [10, 11]. However, the sensors' restricted FoV and potential inaccuracy results in an incomplete or erroneous perception of the surrounding environment [10, 12]. In this sense, the limitations and inherent challenges in the perception layer lead to malfunction for complex drivingapplications, such as collision avoidance systems, blind crossing, location prediction, and high definition maps [13; 14]. To cope with this issue, CAVs must not only depend on on-board sensors or mechanisms that operate locally. This is because the decision layer can have more precise decisions as soon as the perception layer also provides information from other vehicles, sensors, people, cities, etc. [15; 16]. In this sense, collective perception (CP) appears as an alternative to enhance the sensing layer information with data from other vehicles by allowing vehicles to share their sensing and perceived data via wireless communication to obtain a more accurate and comprehensive perception of their surroundings to guarantee safety for CAVs [17; 18]. CP services provide more information to detect safety-critical objects, avoid blind spots, detect non-connected road users or to better track objects [16; 19]. Hence, CP provides updated view of a larger environment by considering information from neighbor vehicles [15; 16]. Finally, the decision layer is responsible for continuously taking efficient and safe driving actions in real time, such as overtaking maneuvers, platoon formation, predictions, route and motion planning, and obstacle avoidance. These decisions are possible through the collective knowledge of the scenario provided by the perception layer [20; 21]. However, CP could lead to significant amount of information exchanged by vehicles, which decrease the performance of the vehicle-to-everything (V2X) network, and also the effectiveness of CP. One way to mitigate this problem is to reduce redundant shared data, which can be achieved by correlating different data sources or taking into account vehicle mobility patterns. In addition, redundant data indicate that many vehicles may execute a duplicate perception across the same region simultaneously [22]. It is important to mention that the processing delay of a given data should not be too long, as otherwise, the processing results would be out of date in relation to the real state of the environment. In this way, the CP may face increased transmission latency as soon as no technique is used to decrease transmission redundancy [23; 24; 25; 26]. Existing studies [27; 28; 29; 30] seek to reduce the overhead of the CP system and control the redundant information exchanged between vehicles to reduce the communications overhead.