# A LiDAR Multi-Object Detection Algorithm for Autonomous Driving
## 3 Results and Discussion
### Object Detection Results and Analysis  
In order to facilitate the study, observation and explanation of the illustration, the RGB image and 3D frame prediction results in BEV mode are shown, and the object detection results are shown in Figure 6 (green for cars, yellow for cyclists, and blue for pedestrians).  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detection Type** & **Pedestrians–Easy** & **Pedestrians–Moderate** & **Pedestrians–Hard** \\ \hline bbox AP11 & 64.78 & 60.99 & 57.57 \\ bbox AP40 & 64.75 & 60.72 & 57.06 \\ BEV AP11 & 69.74 & 66.77 & 63.48 \\ BEV AP40 & 71.06 & 67.33 & 63.56 \\
3D AP11 & 69.68 & 66.59 & 63.12 \\
3D AP40 & 70.99 & 66.95 & 63.18 \\ AOS AP11 & 42.07 & 39.73 & 37.55 \\ AOS AP40 & 41.69 & 39.23 & 36.75 \\ \hline \hline \end{tabular}
\end{table}
Table 6: Pedestrian detection results by ECA Modules–PointPillars on the KITTI datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Detection Type** & **Cyclists–Easy** & **Cyclists–Moderate** & **Cyclists–Hard** \\ \hline bbox AP11 & 85.49 & 73.92 & 69.51 \\ bbox AP40 & 88.74 & 74.71 & 70.74 \\ BEV AP11 & 84.88 & 70.85 & 66.63 \\ BEV AP40 & 87.95 & 71.55 & 67.57 \\
3D AP11 & 84.88 & 70.85 & 66.62 \\
3D AP40 & 87.95 & 71.54 & 67.47 \\ AOS AP11 & 84.47 & 71.39 & 66.98 \\ AOS AP40 & 87.49 & 71.94 & 67.88 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Cyclist detection results by ECA Modules–PointPillars on the KITTI datasets.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Average Inference Time** & **Average Inference Speed** & **Bash\_Size** \\ \hline PointPillars & 0.116 s & 34.483 FPS & \\ This paper & 0.133 s & 30.075 FPS & 4 \\ \hline \hline \end{tabular}
\end{table}
Table 8: Algorithmic inference speed comparison.  
Figure 6 shows the RGB original images and their corresponding point cloud detection result maps for three different road scenes. Among them, the left side, Figure 6a,c,e, show the RGB original images under the highway driving scenario, the rural road driving scenario, and the urban road driving scenario, respectively, and the right side, Figure 6b,d,f, show their corresponding point cloud prediction result maps. Through the point cloud detection, the shape and position information of target objects such as cars, pedestrians, cyclists, etc. are obtained. The point cloud detection result graphs demonstrate the accurate detection and recognition capability of the target objects of these original maps. As shown in the original RGB image and the point cloud detection image, the point cloud data are acquired by the LiDAR sensor, which contains the 3D information of the whole environment, including the target, the ground, the background, etc., covering the spatial area from the sensor to the farthest reachable distance in the scene. However, only the point cloud within the camera's field of view has significance for the vision task, so filtering out the point cloud that is not within the camera's field of view and then detecting is the next step in the endeavor.  
Figure 6: Object detection results. (**a**) Highway driving scenario; (**b**) object detection results corresponding to figure (**a**); (**c**) in rural road driving scenarios; (**d**) object detection results corresponding to figure (**c**); (**e**) in urban driving scenarios; (**f**) object detection results corresponding to figure (**e**).