## 4 Experiments and Results
### Experiment Results  
To assess the performance of the proposed multi-modal 3D object detection network and understand the benefit of multi-modal over single-modal object detection networks, NeXtFusion has been compared to the camera-based 3D object detection networks, orthographic feature transform (OFT) [40] and monocular 3D object detection (MonoDIS) [41], as well as InfoFocus [42], which is a LiDAR-based 3D object detection neural network, and benchmarked on the nuScenes dataset.  
The effectiveness of any object detection network relies heavily on its training process. Table 1 presents the training parameters used for both the existing and the proposed object detection networks analyzed in this study. As is evident from this table, all network models employ the Adam optimizer, a learning rate of 0.0001, batch size of 64, weight decay of 0.0005, momentum of 0.85, and trained for 400 epochs.  
Table 2 presents a comparison of several 3D object detection methods on the nuScenes validation dataset, indicating performance metrics such as mAP, mATE, mASE, mAOE, mAVE, and mAAE described in Section 4.2 of this paper. The models compared are 3D object detection networks that utilize data primarily from camera or lidar sensors such as OFT, MonoDIS, and InfoFocus. As is evident in this table, the proposed NeXtFusion network achieves a remarkable performance, securing the highest mAP score among all compared 3D object detection networks. This metric signifies the overall accuracy of the proposed multi-modal object detection model across different confidence levels.  
Compared to its single sensor-based 3D object detection competitors, the proposed NeXtFusion network exhibits significant improvements in mAP. Notably, it outperforms MonoDIS by 9.5% and surpasses OFT by a margin of 35.1%. While InfoFocus exhibits a very strong mAP score, its reliance solely on LiDAR data limits its capabilities compared to NeXtFusion's fusion of multiple sensor modalities. This multi-sensor (multi-modal) approach enables NeXtFusion to achieve a significant improvement in velocity error compared to camera and lidar-based methods, demonstrating the added value of utilizing  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Object Detection Network** & **Training Dataset** & **Optimizer** & **Learning Rate** & **Batch Size** & **Weight Decay** & **Momentum** & **\# of Epochs** \\ \hline OFT & Y & Adam & 0.0001 & 64 & 0.0005 & 0.85 & 400 \\ \hline MonoDIS & Y & Adam & 0.0001 & 64 & 0.0005 & 0.85 & 400 \\ \hline InfoFocus & Y & Adam & 0.0001 & 64 & 0.0005 & 0.85 & 400 \\ \hline NeXtFusion & Y & Adam & 0.0001 & 64 & 0.0005 & 0.85 & 400 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Training parameters for experimental analysis on the nuScenes training dataset.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Object Detection Network** & **Validation Dataset** & **mAP**\(\uparrow\) & **mATE**\(\downarrow\) & **mASE**\(\downarrow\) & **mAOE**\(\downarrow\) & **mAVE**\(\downarrow\) & **mAAE**\(\downarrow\) \\ \hline OFT & Y & 0.122 & 0.819 & 0.359 & 0.848 & 1.727 & 0.479 \\ \hline MonoDIS & Y & 0.301 & 0.737 & 0.266 & 0.544 & 1.531 & 0.140 \\ \hline InfoFocus & Y & 0.396 & 0.361 & 0.263 & 1.130 & 0.617 & 0.394 \\ \hline NeXtFusion & Y & 0.473 & 0.449 & 0.261 & 0.534 & 0.538 & 0.139 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Comparison of 3D Object Detection Methods on the nuScenes Validation Dataset (\(\uparrow\) indicates Higher = Better, \(\downarrow\) indicates Lower = Better, and Y indicates the use of nuScenes validation dataset).  
diverse sensor information for a more comprehensive understanding of the environment in adverse external environmental conditions.