## 4 Experiments and Results
### Visualization of Samples  
The nuScenes dataset, with its diverse collection of 1000 driving scenes, captures various scenarios that provide a platform to evaluate, benchmark, and compare 3D object detection algorithms. Each scene in this dataset is roughly 20 s long and provides information, including camera images, radar data, and meticulously labeled objects. Although LiDAR data are also represented as a point-cloud representation, similar to radar data in this dataset, LiDAR and radar point-cloud data are fundamentally different despite both appearing as point clouds. LiDAR provides highly detailed 3D representations while radar excels at long-range detection, and therefore, the work presented within this paper focuses on camera-radar sensor fusion. Hence, LiDAR data are not utilized for experiments outlined in this section. \begin{table}
\begin{tabular}{l c c c c c c c c c} \hline \hline
**Network** & **C** & **R** & **NM** & **FAM** & **mAP**\(\uparrow\) & **mATE**\(\downarrow\) & **mASE**\(\downarrow\) & **mAAOE**\(\downarrow\) & **mAVE**\(\downarrow\) & **mAAE**\(\downarrow\) \\ \hline MonoDIS & \(\bigvee\) & - & - & - & 0.301 & 0.738 & 0.269 & 0.544 & 1.532 & 0.141 \\ \hline NeXtDet & - & - & - & - & 0.351 & 0.635 & 0.271 & 0.550 & 1.346 & 0.142 \\ \hline NeXtFusion & \(\bigvee\) & \(\bigvee\) & - & 0.427 & 0.499 & 0.268 & 0.541 & 0.846 & 0.140 \\ \hline NeXtFusion & \(\bigvee\) & - & \(\bigvee\) & 0.474 & 0.448 & 0.262 & 0.534 & 0.536 & 0.138 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results of the ablation studies on nuScenes validation dataset (C: camera, R: radar, NM: naïve method, and FAM: frustum association method). This section delves into visualizing the proposed network's validation performance in terms of 3D object detection on the nuScenes dataset. In this dataset, the scenes are annotated every half a second (i.e., at 2 Hz). A sample is defined as an annotated keyframe of a scene at a specific timestamp, where the timestamps of data from all sensors closely align with the sample's timestamp. For illustration, let us examine the first annotated sample in a scene described as 'Night, pedestrians on sidewalk, pedestrians crossing crosswalk, scooter, with difficult lighting conditions' by the annotators of the dataset. This scene was captured in Holland Village, Singapore, using a comprehensive sensor suite mounted on a vehicle. Each snapshot of a scene references a collection of data from these sensors, accessible through a data key in the dataset. These sensors, which are mounted on the vehicle, include the following:  
* One LIDAR (light detection and ranging) sensor:
* LIDAR_TOP
* Five RADAR (radio detection and ranging) sensors:
* RADAR_FRONT
* RADAR_FRONT_LEFT
* RADAR_FRONT_RIGHT
* RADAR_BACK_LEFT
* RADAR_BACK_RIGHT
* CAM_FRONT_LEFT
* CAM_FRONT_RIGHT
* CAM_BACK_RACK_LEFT
* CAM_BACK_RIGHT  
Figure 11 provides a visual representation of 3D object detection performed by the proposed NeXtFusion network on a scene with occlusion and poor lighting conditions, and Figure 12 provides an example of multi-modal 3D object detection involving multiple objects at an intersection, such as humans (blue bounding box), cars (yellow bounding box), and trucks (red bounding box). It also demonstrates the object detection performance of distant, tiny objects in the shadow of the building. Figure 11: An example of Camera-Radar fusion-based 3D object detection involving occlusion at night from the nuScenes dataset using the proposed NeXtFusion network. Different colors of the bounding boxes indicate different objects detected. Figure 13 provides an example that plots radar point-cloud data for the same image from Figure 12. Unlike LiDAR, which excels in dense, close-range measurements, radar boosts a significantly larger operational range. While this extended reach comes at the expense of point density, it enables the detection of distant objects that might be invisible to LiDAR. Consider a scenario where a car is approaching a sharp bend on a highway.