# Vulnerable Road User Detection for Roadside-Assisted Safety Protection: A Comprehensive Survey
## 3 VRU Detection Models
### Multi-Sensor
#### 3.2.1 Classification by Modality
[4] designed an infrastructure-based roadside unit (IRSU) with two cameras, one LiDAR, and a Cohda Wireless RSU to detect pedestrians in the blind spots of connected and autonomous vehicles (CAVs), providing the CAVs with collision avoidance data. In this setup, the cameras were used for object detection and classification, while LiDAR was used to generate 3D dimensions and orientations. InfraDet3D [111] proposed a camera-LiDAR fusion method where LiDAR detection results were first transformed into the camera coordinate system, and then the detections from both sensors were synchronized based on the distance between the center points of the detected objects. In this method, the camera's detection served as a supplement to LiDAR detection, employing a late fusion strategy. In CoopDet3D [118], [PERSON] et al. extracted features with roadside data from a camera and LiDAR. The features were transformed into a bird's eye view (BEV) representation. This method outperformed InfraDet3D on the TUMTraf dataset. VBRFusion [119] was proposed for VRU detection at medium to long ranges, based on the DAIR-V2X dataset [107]. VBRFusion enhanced the encoding of point and image feature mapping, as well as the fusion of voxel features, to generate high-dimensional voxel representations. In general, roadside fusion methods are gradually shifting from conventional function fusion approaches to feature fusion methods, aligning with generic LiDAR and camera sensor fusion techniques. Camera and RADAR: The combination of a camera and RADAR as a sensing unit is a cost-effective solution for detection. Similar to LiDAR-based detection models, this category includes both conventional methods and deep learning-based methods for fusing data from cameras and RADAR. Conventional methods often employ weighted averaging and clustering techniques, while deep learning approaches leverage neural networks to improve detection accuracy and robustness [120]. [PERSON] et al. [121] developed a fusion method that capitalizes on the strengths of both cameras and RADAR to detect road users, including VRUs. RADAR detection results provide the distance and velocity of the target by measuring the time delay and phase shift of the echo signal. The camera detects the target's angle and classification using YOLO V4 [22]. After calibrating the coordinates of both sensors, the results from the two sensors \begin{table}
\begin{tabular}{c c c} \hline \hline
**Category Based on Feature Extraction Method** & **Methods** & **Comments** \\ \hline Point- and feature map-based & PointFusion [59], PI-RCNN [60], EPNet [61], MSMDFusion [62] & Precise 3D spatial data from LiDAR combined with camera’s semantic information helps detect VRUs, even in occlusions or complex environments. \\ Point- and mask-based & PointFainting [63] & accurate VRU detection, especially in occlusions or partial visibility scenarios. \\ Deep feature-based & DeepFusion [64], TransFusion [65], SupFusion [66] & Advanced fusion of high-level features from both sensors leads to the best detection of VRUs, especially in dynamic or complex scenes. \\ \hline \hline \end{tabular}
\end{table} 
Table 4: _Cont._are fused using the nearest-neighbor correlation method. Similarly, in RODNet, RGB images are used to classify and localize objects, while transformed radio frequency images are utilized only for object localization [122]. [PERSON] et al. [123] proposed methods for both single-scan and multi-scan fusion using Dempster's combination rule to enhance evidence fusion performance. They also employed pre-calibration and the nearest-neighbor correlation method to associate detections from the camera and RADAR. Furthermore, the authors fused the detection results from multiple scans by associating detections from adjacent scans, which improved overall performance. With camera and RADAR fusion, challenging scenarios, particularly those captured under poor lighting conditions, can be detected more accurately.