# A LiDAR Multi-Object Detection Algorithm for Autonomous Driving
## 3 Results and Discussion
### Experimental Results and Analysis
However, there are some issues that may have an impact on the detection results of small objects, for example, pedestrians may be confused with other slender objects pedestrians such as poles, tree trunks, and the like, and the presence of certain vertical objects may also affect the number of point clouds. Real-world environments with different lighting, shooting angles, and complex backgrounds also affect detection effects, etc. In order to solve these problems, a combination of image-based methods [38] may be a viable solution. In addition to list the official KITTI evaluation protocols, i.e., an IoU threshold of 0.7 for cars and 0.5 for pedestrians and cyclists, it also lists the IoU thresholds of 0.7, 0.5, and 0.5 for the bbox AP, BEV AP, 3D AP, and AOS values for car detection and the IoU thresholds of 0.5, 0.25, and 0.25 for bbox AP, BEV AP, 3D AP, and AOS values for pedestrian and cyclist detection. According to the different methods of taking the equal interval of the recall rate, the results of the interpolation precision of AP11 (indicating the interpolation precision of 11 points, taking the equal interval of the recall rate, calculating the average value of the relative exact rate, and obtaining the average precision; R11 = {0, 0.1, 0.2,...., 1}) and AP40 (indicating the interpolation precision of 40 points; R40 = {1/40, 2/40, 3/40,...., 1}) are shown in Tables 5-7. \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 SECOND [15] & 54.53 & 87.84 & 81.31 & 71.95 & 51.56 & 43.51 & 38.78 & 80.97 & 57.20 & 55.14 \\ PointPillars [16] & 68.86 & 90.19 & 88.76 & 86.38 & 58.05 & 49.66 & 47.88 & 82.43 & 68.16 & 61.96 \\ This Paper & 66.84 & 90.76 & 89.39 & 88.20 & 42.07 & 39.73 & 37.55 & 84.47 & 71.39 & 66.98 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Experimental results of the AOS model. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detection Type** & **Cars–Easy** & **Cars–Moderate** & **Cars–Hard** \\ \hline bbox AP11 & 90.82 & 89.62 & 88.60 \\ bbox AP40 & 95.52 & 92.03 & 90.99 \\ BEV AP11 & 90.82 & 90.03 & 89.37 \\ BEV AP40 & 95.59 & 94.62 & 93.75 \\
3D AP11 & 90.82 & 89.95 & 89.22 \\
3D AP40 & 95.56 & 94.43 & 93.30 \\ AOS AP11 & 90.76 & 89.39 & 88.20 \\ AOS AP40 & 95.45 & 91.78 & 90.56 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Car detection results by ECA Modules–PointPillars on the KITTI datasets. Theoretically, the ECA attention module has the property of supporting plug-and-play and does not bring too much extra burden to the training, running, computation, and storage of the algorithm. However, in order to verify whether the complexity it brings is reasonable or not, and whether it will limit real-time processing in autonomous driving scenarios and affect the scalability of the algorithm, in this paper, the speed comparison of the algorithm before and after the improvement is conducted on NVIDIA GeForce RTX 3080 Ti graphics card, and the comparison results are shown in Table 8.  
Where FPS denotes the number of detectable scenes per second, it can be seen that the inference speed of the improved algorithm is basically kept at the same level as that of the PointPillars algorithm. It is shown that the additional introduction of the ECA module does not affect the real-time performance of the algorithm too much, and the improved algorithm is able to improve the detection accuracy while still maintaining the computational efficiency, which preliminarily verifies the effectiveness and scalability of the algorithm. However, the actual scalability is still affected by factors such as hardware platform, datasets size, and computational resources. Therefore, further tuning and adaptation are still necessary to meet the needs of specific application scenarios.