## 4 Experiment and Result Analysis
### Experimental Results  
For quantitative comparison with competing methods, we evaluated the performance of the proposed system using two metrics: mean intersection over union (mIoU) for both lane line segmentation and drivable area estimation, and accuracy (Acc.) for the scene classification. The mIoU is defined as the mean of a single intersection over a union per class, \(IoU=TP/(FP+TP+FN)\), where \(TP\), \(FP\), and \(FN\) denote _true positive_, \(false\)_positive_, and \(false\)_negative_, respectively, for a single class. The accuracy was calculated using the ratio of the correctly classified road to the total number of test images, \((TP+TN)/(TP+FP+TN+FN)\). We also measured the frame per second (fps), which denotes how many frames can be processed in a second, to show the running time. The performance comparison with that of the competing drivable area estimation, lane line segmentation, and scene classification methods on the BDD dataset is listed in Table 2. We compared our method with the winners of the Autonomous Driving challenge for drivable area detection [26; 27] and others [9; 28]. We also compared the lane line segmentation task with [13; 14; 29], which are single task approaches for lane line detection or segmentation. The comparison with multi-task learning approaches [1; 2; 3] are also provided in the table. Even though competing methods shows good results, most of them are not suitable for real-time data processing, except VisLabs [2], SCNN [14], and Asgarian [9]. In particular, IBN_PSA/P is based on IBN-Net [26], and both Mapillary Research [27] and DiDi AI Labs [28] use a modified version of ResNet [16]. MultiNet [1] is a well known multi-task learning approach that performed street classification, vehicle detection and road segmentation on the KITTI dataset. DLT-Net [3] performed drivable area estimation and traffic object detection, simultaneously. To the best of our knowledge, this work is the first real-time multi-task learning approach consists of drivable area estimation, lane line segmentation, and scene classification on the BDD dataset. Here, note that the performances of competing methods were adapted from by [2; 13], which might slightly vary as the implementation details are only partially public. As shown in Table 2, compared to competing multi-task learning approaches [1; 2; 3], the proposed methods show improved performance in all three tasks. More accurate drivable area estimation is achieved with a mIoU of 84.41%, and the accuracy of scene classification reaches 78.4%, which is higher than that of the competing method. Compare to other multi-task learning approaches, our approach significantly outperformed competing methods in speed and accuracy. Specifically, the slowest version of our method, which performs three tasks (drivable area estimation, lane line segmentation, and scene classification) simultaneously executes at 93.81 fps. That is more than four times faster than the VisLabs [2]. This is a remarkable advantage for practical applications that require a short time performance, such as in autonomous vehicles. Compared to the prevision single task drivable area estimation methods [26; 27], our method shows a slightly lower mIoU. However, the competing method is not suitable for real-time application with 3.81 fps; the proposed method exhibits more than 20 times faster inference time compared to the competing methods while providing lane line segmentation and scene classification simultaneously. This implies that the proposed method has better computational efficiency, which is also one of the advantages for mobility devices considering the limited resources. We believe that it is competitive results considering that the multi-task approach can provide more rich information that can be utilized in other modules of autonomous driving than a single-task approach.