# Joint Semantic Understanding with a Multilevel Branch for Driving Perception
## 4 Experiments
### Experimental Result Analysis  
We showed the effectiveness of the proposed framework by comparing the performances with the most representative multi-task networks and single-task networks for intelligent vehicles. DTL-Net [9], YOLOP [10], MultiNet [8], FDAE [27], and VisLab [26] are multi-task learning based networks. IBN_PSA/P [36], Faster R-CNN [1], YOLOv5s [16], Asgarian et al. [21], PSPNet [5], ENet [37], ENet-SAD [4], and SCNN [3] are single-task based networks. In particular, DTL-Net and MultiNet are the most representative multi-task learning methods that handle driving perception tasks. We used the following metrics for the evaluation and quantitative comparison with competing methods; Recall(%) and mAP50(%) for the traffic object detection; mIoU(%) for the drivable area segmentation; accuracy(%) and IoU(%) for the lane line segmentation. We also compared the processing speed using frames per second (fps). Compared to competing methods (Table 1), the proposed network shows the state-of-the-art drivable area segmentation performance. The proposed method achieved 92.68% of mIoU, which is higher than all competing methods. More importantly, the proposedmethods can be executed in real time with 37 fps. This is a significant advantage for driving assistance application that is safety-critical and has limited resources. Figure 3 shows the results of the proposed drivable area segmentation. We can see that the drivable areas are sufficiently well segmented in various environments. It can be observed that the opposite lane, lane line, and other vehicles are successfully excluded from the drivable area, while the free space on the road is segmented as the drivable area. Table 2 compares our lane line segmentation result with other state-of-the-art approaches. The proposed method also yields the highest performance in the lane line segmentation task with 72.13% and 26.92% of accuracy and IoU score, respectively. The superior performances in drivable area segmentation and lane line segmentation show that the shared layer at the second branch in our proposed network effectively represents the common area where the road is placed. Compared to FDAE, the proposed method shows lower fps but achieves 8.12% higher mIoU in the drivable area, and 8.67% higher accuracy in the lane line segmentation task, while maintaining real-time execution. This is because the FDAE mainly focused on fast inference through global context understanding with simple scene classification using a light backbone network, whereas the proposed network enables the more elaborate prediction of the driving environment incorporating with traffic object detection. In addition, traffic object detection is a very necessary task to avoid a collision. Considering that the intelligent vehicle is a very safety-critical application, a comprehensive understanding of road areas enables the model to achieve better driving perception. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Method** & **mIoU (\%)** & **Speed (fps)** \\ \hline MultiNet [8] & 71.60 & 8.6 \\ DLT-Net [9] & 72.10 & 9.3 \\ IBN\_PSA/P [36] & 86.18 & 3.81 \\ Asgarian et al. [21] & 83.50 & 322 \\ PSPNet [5] & 89.60 & 11.1 \\ VisLabs [26] & 83.35 & 23.8 \\ FDAE [27] & 84.56 & 93.8 \\ YOLOP [10] & 91.50 & 41 \\ Ours & 92.68 & 37 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Drivable area segmentation results on the BDD100K dataset. Figure 3: Examples of the drivable area segmentation results. Figure 4 shows the qualitative results of lane line segmentation. The green marks represent the lane lines on the road. From the first image (left top) of Figures 3 and 4, the wide space is well separated by distinct regions where the lane lines are placed. Here, note that, following the ground truth of the BDD100K dataset, the left bottom image of Figure 4 also shows that the crosswalk is well segmented as lane lines. Table 3 lists the traffic object detection performances on the BDD100K dataset. We only consider the vehicle detection, the same as previous research [8; 9; 10].