## 4 Experiment and Result Analysis
### Experimental Results  
Average Precision (AP) was adopted as the evaluation metric for this study. As demonstrated in Table 3, ASCA-PointPillars outperforms other algorithms in terms of pedestrian recognition accuracy. This highlights the effectiveness of the ASP module and RS-Aug in improving the recognition performance of small objects. Although the recognition accuracy for cars and cyclists may not exceed that of other algorithms, ASCA-PointPillars achieves a higher Frames Per Second (FPS) than all other algorithms (except PointPillars), ensuring real-time recognition. Moreover, ASCA-PointPillars exhibits superior recognition accuracy across all three categories compared to PointPillars, demonstrating its ability to maintain high accuracy in real-time scenarios. ASCA-PointPillars was also implemented on NVIDIA Xavier AGX, an edge computing device, and achieved a frame rate of 21.82 FPS. This demonstrates that the proposed algorithm can provide real-time recognition capabilities in vehicle terminals. Table 4 shows the recognition results of PointPillars and the proposed ASCA-PointPillars on objects at two distance ranges in the KITTI dataset, using mean Average Precision (mAP) as the evaluation metric. As can be seen from the table, the recognition accuracy of both algorithms inevitably decreases as the distance increases. However, compared to PointPillars, the proposed ASCA-PointPillars exhibits a higher recognition accuracy for objects at longer distances. Specifically, it achieves accuracy improvements of 2.94% and 3.02% in the ranges of 0-40 m and 40-80 m, respectively. This demonstrates that the proposed ASP module can effectively enhance the recognition accuracy of objects at long distances. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multirow{2}{*}{**FPS**} & \multicolumn{3}{c}{**Car**} & \multicolumn{3}{c}{**Pedestrian**} & \multicolumn{3}{c}{**Cyclist**} \\ \cline{3-10}  & & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** & **Easy** & **Mod.** & **Hard** \\ \hline ASCA-PointPillars & **31** & 86.51 & 75.77 & 69.24 & **55.24** & **46.04** & **43.58** & 75.63 & 59.91 & 55.34 \\ PointPillars & **62.5** & 82.58 & 74.31 & 68.99 & 51.45 & 41.92 & 38.89 & 77.10 & 58.65 & 51.92 \\
3 DSSD & 25 & 88.36 & 79.57 & 74.55 & 54.64 & 44.27 & 40.23 & 82.48 & 64.10 & 56.90 \\ Point-GNN & 1.7 & 88.33 & 79.47 & 72.29 & 51.92 & 43.77 & 40.14 & 78.60 & 63.48 & 57.08 \\ Voxel R-CNN & 25 & **90.90** & 81.62 & 77.06 & NaN & NaN & NaN & NaN & NaN \\ PV-RCNN & 12.5 & 90.25 & 81.43 & 76.82 & 52.17 & 43.29 & 40.29 & 78.60 & 63.71 & 57.65 \\ PDV & 10 & 90.43 & **81.86** & **77.36** & 47.80 & 40.56 & 38.46 & **83.04** & **67.81** & **60.46** \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Results on the KITTI 3D object dataset (%). NaN indicates that there are no relevant data in the KITTI test benchmark. Figure 8 offers a qualitative analysis of the recognition results, with cars represented by red bounding boxes, cyclists by blue bounding boxes, and pedestrians by green bounding boxes. As shown in Figure 8c,d, the recognition results clearly show that while PointPillars may fail to detect distant objects, ASCA-PointPillars can accurately recognize certain severely occluded or sparse objects at a distance. Only a few distant objects and those with excessively sparse point clouds remain undetected. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **0–40 m** & **40–80 m** \\ \hline PointPillars & 71.48 & 51.33 \\ ASCA-PointPillars & 74.42 & 54.35 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Distance-wise recognition results on the KITTI 3D object validation dataset (%). Figure 8: Visualization of recognition results for PointPillars and ASCA-PointPillars: (**a**) scene; (**b**) ground truth; (**c**) prediction of PointPillars; (**d**) prediction of ASCA-PointPillars. Cars are represented by red bounding boxes, cyclists by blue bounding boxes, and pedestrians by green bounding boxes.