# A LiDAR Multi-Object Detection Algorithm for Autonomous Driving
## 3 Results and Discussion
### Experimental Results and Analysis  
Average precision (AP) was used as an evaluation metric for 3D object detection algorithms in order to evaluate 2D, bird's eye view, 3D, and Average Orientation Similarity (AOS) ([PERSON] et al., 2017). Based on the degree of occlusion of the target, the KITTI datasets were categorized into three levels of difficulty: easy, moderate, and hard. The easy, moderate, and hard overlap thresholds were set to 0.7, 0.7, and 0.7 times IoU for the car category, and for the cyclist and pedestrian category the easy, moderate, and hard overlap thresholds are set to 0.5, 0.5, and 0.5 times IoU, respectively. Table 2 shows the comparison of the detection performance results with the literature ([PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017) algorithms based on the KITTI datasets cars, pedestrians, and cyclists in BEV mode at three difficulty levels. Table 3 shows the comparison of the detection performance results with literature ([PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017; [PERSON] et al., 2017) algorithms based on the KITTI datasets cars, pedestrians, and cyclists in 3D mode at three difficulty levels. Table 4 demonstrates the comparison of detection performance results with literature ([PERSON] et al., 2017; [PERSON] et al., 2017) algorithms based on the KITTI datasets cars, pedestrians, and cyclists in AOS mode at three difficulty levels. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Difficulty Level** & **Easy** & **Moderate** & **Hard** \\ \hline Shelter & Fully visible & Partial shade, mostly sheltered & Hard see \\ Truncation value & \(\leq\)15\% & 15–50\% & \(\geq\)50\% \\ Number of pixels & \(\geq\)40 & & \(\geq\)25 & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Classification of difficulty levels. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**Method**} & \multicolumn{4}{c}{**Cars**} & \multicolumn{4}{c}{**Pedestrians**} & \multicolumn{4}{c}{**Cyclists**} \\ \cline{2-10}  & **mAP** & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** & **Easy** & **Moderate** & **Hard** \\ \hline F-PointNet ([PERSON] et al., 2017) & 57.35 & 81.20 & 70.39 & 62.19 & 51.21 & 44.89 & 40.23 & 71.96 & 56.77 & 50.39 \\ VoxelNet ([PERSON] et al., 2017) & 49.05 & 77.47 & 65.11 & 57.73 & 39.48 & 33.69 & 31.50 & 61.22 & 48.36 & 44.37 \\ SECOND ([PERSON] et al., 2017) & 56.69 & 83.13 & 73.66 & 66.20 & 51.07 & 42.56 & 37.29 & 70.51 & 53.85 & 46.90 \\ PointPillars ([PERSON] et al., 2017) & 59.20 & 79.05 & 74.99 & 68.30 & 52.08 & 43.53 & 41.49 & 75.78 & 59.07 & 52.92 \\ Complex-YOLO ([PERSON] et al., 2017) & 54.01 & 67.72 & 64.00 & 63.01 & 41.79 & 39.70 & 35.92 & 68.17 & 58.32 & 54.30 \\ Voxel R-CNN ([PERSON] et al., 2017) & - & 89.41 & 84.52 & 78.78 & - & - & - & - & - \\
3 DSSD ([PERSON] et al., 2017) & - & 89.71 & 79.45 & 78.67 & - & - & - & - & - \\ This Paper & 63.24 & 86.76 & 76.96 & 74.07 & 53.83 & 48.60 & 45.21 & 81.14 & 64.15 & 60.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Experimental results of the 3D model. The experimental results show that compared with the PointPillars algorithm in moderate difficulty, the mAP of the improved algorithm is improved by 3.84% in the BEV mode, 4.04% in the 3D mode, and the mAOS is reduced by 2.02% in the AOS mode. In addition, the AP of cars in the BEV mode improves to 87.39% in the moderate difficulty, 76.96% in the 3D mode, and 89.39% in the AOS mode; the AP of cyclists in the BEV mode improves to 67.84% in the moderate difficulty, 64.15% in the 3D mode, and 71.39% in the AOS mode; except for the pedestrian category, the improved algorithm outperforms the other detection models in the table under the other two categories. From Tables 2 and 4, it can be seen that the detection results under the AOS mode for pedestrians do not present optimal results. The attention mechanism can help the network to focus on the key information in the input data and suppress the unimportant information.