# OD-XAI: Explainable AI-Based Semantic Object Detection for Autonomous Vehicles
## 4 The Proposed Approach
### Preprocessing
\begin{table}
\begin{tabular}{c c c c} \hline \hline  & **ResNet-18** & **ResNet-50** & **SegNet** \\ \hline Train set Mean IOU & 0.9459 & 0.8665 & 0.9038 \\ \hline Test set Mean IOU & 0.9621 & 0.9190 & 0.9343 \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Comparison of IOU results of the trained models. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **ResNet-18** & **ResNet-50** & **SegNet** \\ \hline \hline \multirow{2}{*}{Train IOU} & **Class-Road** & **Class-Surroundings** & **Class-Road** & **Class-Surroundings** \\ \cline{2-5}  & 0.9459 & 0.8665 & 0.8665 & 0.9038 & 0.9038 \\ \hline Test IOU & 0.9622 & 0.9621 & 0.9195 & 0.9184 & 0.9344 \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Class-wise IOU score for the proposed model architectures. In this section, we presented the output and results of the trained models on the KITTI road dataset. We evaluated the model using the mean IOU metric and observed its value for all individual classes. Figure 16: Comparison of various semantic segmentation outputs of trained deep learning architectures with the original and ground truth images. The above outputs are obtained for four different original test images belonging to the KITTI dataset. (**a**–**d**) Test images, (**e**–**h**) ground truth, (**i**–**l**) ResNet-18, (**m**–**p**) ResNet-50, and (**q**–**t**) SegNet. Figure 17: Inference time analysis of the trained models. ## 7 Conclusions

This paper trained and assessed three current DL semantic segmentation models for road detection in AVs. It is necessary for AI in AVs to effectively recognize roads and other surrounding objects to securely drive a car. It is vital to implement these architectures correctly, but it is also necessary to generate explanations for the decisions and the complicated inner workings of the model. Hence, to handle a similar challenge, we attempted to integrate XAI with the trained models in this paper. We applied Grad-CAM and saliency maps to the models and described the relevance of their outcomes. We observed that the areas of an image that had more significance had brighter pixel values in the heat map. We also studied a few model sublayers to understand which layer focused on what part of the image. Although the explanations offered in this research reveal specific insights into the complex model and predictions, they do not entirely solve the black box issues of the ML and DL algorithms. In the future, natural language processing will be integrated in the XAI system to provide explanations to the users in textual form. Conceptualization: W.-C.H. and S.T. ; writing--original draft preparation: H.M., D.J. and R.G. methodology: R.G. and S.T. ; writing--review and editing: R.S. and S.T. All authors have read and agreed to the published version of the manuscript. This research is supported by the Ministry of Science and Technology, Taiwan (MOST 110-2410-H-161-001). Not applicable. The authors declare no conflict of interest. ## References

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