## 2 Cooperative Perception Information Fusion
### Image-Point Fusion  
Single-modal multi-sensor data fusion is always limited by intrinsic and extrinsic conditions. For example, the camera has a difficult time detecting the target depth information and is greatly affected by adverse weather and light. LiDAR has poor perception effects, low resolution, and a high cost for long-distance small targets. Millimeter-wave radars have strong antijamming capabilities but low resolution and a limited field of view. This subsection summarizes and analyzes the current sensor data fusion methods and challenges for single-vehicle autonomous driving [35; 36] and points out that multimodal and multilevel sensor information fusion can greatly improve the effectiveness of cooperative perception. Figure 5 shows a comparison between image and LiDAR data characteristics. Image-point cloud information fusion is the mainstream method of autonomous driving cooperative perception in the current IoV environment. Common fusion methods include data fusion estimation methods, based on the Kalman filter; the Bayesian-based distributed fusion method; and the neural network-based method [37]. [PERSON] et al. [38] proposed a robust target recognition algorithm based on the fusion of millimeter-wave radar and a camera based on the antijamming capability of millimeter-wave radar for foggy weather. The captured target is filtered by millimeter-wave radar and mapped onto the image to obtain an ROI. The detection results identified by the camera vision network are fused with the radar target estimation value using the weighted technique, resulting in perception results with greater detection accuracy. However, the method of fusing the perceptual information of radar and camera using convolutional neural networks has proved to be more promising. [PERSON] et al. [39] used radar detection to create ROIs on images and utilized neural networks to classify the ROI, and similar object detection methods are also represented in the literature [40; 41; 42; 43]. By fusing the results of the separate detection of the camera and the radar, [PERSON] et al. [44] realized an effective correlation between the distance measurement of the radar and the image information. Deep learning methods based on generative adversarial networks used to achieve the fusion of camera and radar data have been widely studied [45]. [PERSON] et al. [46] designed a radar and camera cooperative detection algorithm, projecting the radar signal to the image coordinate system through coordinate transformation and using the symmetry of the vehicle to identify the target vehicle, and they achieved good detection results. With the continuous deepening of research, some scholars have tried to deploy radar and camera perception equipment on the roadside to perceive the surrounding environment and provide more complete perception information for autonomous vehicles through wireless communication technology. [PERSON] et al. [47] utilized edge computing to localize the environmental perception data to effectively reduce latency. Figure 5: A comparison between image data and point cloud data. Meanwhile, the YOLOv3 [48] and DBSCAN clustering algorithms [49], often deployed in roadside equipment, are used to preprocess camera and radar data, respectively, to obtain information such as the position, speed, and category of targets. Then, the method of joint calibration and direct update is used to synchronize the two types of sensors in space and time, and the Munkres algorithm and Kalman filter are used to achieve the association and tracking of multiple targets, which effectively improves the horizontal and vertical detection performance of the sensing system but lacks real-world verification. [PERSON] et al. [50] realized the fusion perception of radar-camera sensors in a real road environment, which reduced the delay of actively perceiving the target and accelerated the actual deployment of cooperative perception technology.