# Networked Roadside Perception Units for Autonomous Driving
## 2 State of the Art  
Sensor data sharing can be classified into three categories depending on the occurrence of sensor fusion [7]: (1) In raw/low-level data sharing, raw data from the sensors is shared - such data includes point clouds from LiDAR and camera images; (2) in feature/middle-level data sharing, pre-processed data (such as a bounding box from a vision-based object detector) is shared; and (3) in object/track-level data sharing, the position information of the objects in the global coordinate system is shared. Augmented vehicular reality (AVR) [8] systems share raw-level sensor data (point cloud) of the 3D camera depth perception sensors. Some researchers have also evaluated the realization of feature-level sensor data sharing in real vehicles [9]. In [10], the authors discussed the use of feature-level sensor data sharing to address the limited network bandwidth and stringent real-time constraints. Object(track)-level cooperative perception has two advantages over raw- and feature-based approaches: Low wireless resource requirement and sensor agnosticism. Object(track)-level approaches such as collective perception messages (CPMs) [5; 6] have received the most attention from researchers and developers because of their suitability for deployment in cooperative ITS. The bandwidth consumption in CPM was analyzed in [11; 12]. A few researchers have realized object-level cooperative perception with multiple infrastructure-based sensors and transmitters [13; 14]. Our previous work [15] proposed CAM-encoded infrastructure-based cooperative perception. The receivers can process the message using the same reception procedure as that of CAM. Consequently, the solution is compatible with all the products currently available in the market. When multiple vehicles detect the same object, they all transmit the object's information and hence introduce redundancy. A reasonable level of redundancy is useful to confirm the existence of the object. However, there is a risk of message flooding under heavy traffic. [16; 17] proposed generation rules for mitigation. Some message filtering algorithms [18; 19] reduce the number of messages while maintaining perception performance. [20] leveraged the trade-off between optional information and  
Figure 1: The roadside perception unit (RSPU) system and networked RSPUs. message size to save network resources. In [21], the authors introduce deep reinforcement learning that addresses the network load problem. Security in cooperative perception is essential for deployment. In [22], the researchers attempted to examine the trustworthiness of cooperative perception by quantifying the confidence in the correctness of data by using Bayes' theory. TruPercept [23] achieves cooperative perception through trust modeling; its dataset is openly available and includes unreliable and malicious behavior scenarios. Researchers often evaluate the network performance of vehicular ad-hoc networks (VANETs) in the simulator. Artery [24] is a popular VANET simulation framework that combines OMNet++ (network simulator) and SUMO (traffic simulator). [25, 26] extended the Artery framework for cooperative perception by adding the sensor model. However, field testing is essential to understand the real effect of sensor communication in the environment. There are only a few field tests available in the literature [27]. In [28], the authors tested V2X cooperative perception and its application to ITS by considering basic safety message (BSM) and conducting a field test involving three vehicles. Reference [29] developed a system based on robot operating system (ROS) to visualize sensor perception and CAM and create test environments using a combination of real and virtual (simulated) objects. The cooperative automation research mobility application (CARMA) platform [30] is an open-source software that connects Autoware-based autonomous vehicles [31, 32] by using US standards [33], including BSM, signal phase and timing (SPaT), and MAP.