# OD-XAI: Explainable AI-Based Semantic Object Detection for Autonomous Vehicles
## 4 The Proposed Approach
### Preprocessing
All other objects in the original image were filtered away; only the road and surroundings were kept. Figure 12a,b are the outputs generated on channels 39 and 114 of layer 34 of the ResNet-18 model. Although the segmentation is not accurate, most of the relevant traits were already retrieved. Figure 11: Model—ResNet-18, layer 7, channel 63 weights applied on the test image. (**a**) Test image. (**b**) Channel 63 output on the test image (**a**). Vertical poles and trunks of the trees obtained greater heat-map values denoting higher gradient scores. \[\frac{\partial t_{c}}{\partial F^{k}} \tag{3}\]0 Model—ResNet18, layer 7, total channels—64, layer type—Max Pooling. (**a**) Output of channel 0. (**b**) Output of channel 5. (**c**) Output of channel 33. (**d**) Output of channel 63. 3. Deep hidden layers: Deep hidden layers are the ones closer to the output layers of the model. Here, we studied the deep layers of ResNet-18 residing in the decoder area of the semantic segmentation architecture. We investigated the output of a few of these layers of the model. Comparing the outputs of layer 34 in Figure 12 to that of layer 94 in Figure 13, we can identify total altercations in the projections. Layer 94 is a deep network layer; near to the output layer, it performs a better job at creating road semantics compared to layer 34. The gradients in Figure 13 are not as uniform as the output layer. It should be noted that the gradients are still not smooth. In Figure 14b, specific stress is placed on the wheels of the vehicle. Moreover, the figure's null gradients on the road portion also depict that the surroundings in the image are appropriately segregated from the road part. It can be interpreted that the gradients in the output of Figure 14a,c are brighter near the boundary of the road region. In Figure 15, the ResNet-18 model outputs for deeper layers are obtained. These images are almost similar to the predictions obtained at the output layer. Figure 15a shows the predicted output map, which stresses on the background of the image and Figure 15b shows the predicted output map, which stresses on the road part of the image. \[\frac{\partial t_{c}}{\partial F^{k}} \tag{3}\]1 Model—ResNet-18, layer 102, total channels—64, type—convolutional layer. (**a**) Output of channel 4. (**b**) Output of channel 5. (**c**) Output of channel 7. Smooth semantic outputs are generated at this layer. \[\frac{\partial t_{c}}{\partial F^{k}} \tag{3}\]2 Model—ResNet-18, layer 34, total channels—64, type—convolutional layer. (**a**) Channel 39. (**b**) Channel 114. \[\frac{\partial t_{c}}{\partial F^{k}} \tag{3}\]3 Model—ResNet-18, layer 94, total channels—256, type—transpose convolutional layer. (**a**) Output of channel 16. (**b**) Output of channel 17. \[\frac{\partial t_{c}}{\partial F^{k}} \tag{3}\]4 Model—ResNet-18, layer 107, total channels—32, type—convolutional layer. (**a**) Output of channel 1. (**b**) Output of channel 2. Uniform and accurate gradients. The predictions at these layers are almost similar to those of the output layers. #### 5.2.4 Comparison of XAI Techniques

Table 7 contrasts the aforementioned XAI techniques. These strategies provide an understanding of the decision-making modules in an AV to its stakeholders. The expansion of total AVs worldwide will require people to comprehend the AV working mechanisms (to trust its judgments). The XAI techniques discussed above provide simple (yet detailed) depictions of AV judgments. As a result, these XAI techniques are appropriate for autonomous driving applications. ## 6 Results and Discussions

Results of the proposed DL models are discussed in this section. Evaluation metrics provide data on train and test set performances and play key parts in primary model testing and diagnoses. These parameters are critical to examine before deploying the DL models into AVs. Poor performance on measures suggests a poorly trained model that cannot be deployed in the actual world. A thorough description of evaluation methods is provided in the forthcoming section.