# OD-XAI: Explainable AI-Based Semantic Object Detection for Autonomous Vehicles
## 3 System Model and Problem Formulation  
Figure 3 displays the system model of semantic segmentation of an input frame along with its integration of XAI for the model explanation. Firstly, AV records the frame with the help of a camera positioned on it. The input frame is then scaled, preprocessed, and made suitable for predictions. During data collection, frames are stored in a database that  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Naive User** & **Developer** & **Naive User** & **Developer** \\ \hline \multirow{6}{*}{When the AV is stationary [19]} & **Without XAI Integration** & **Without XAI Integration** & **With XAI Integration** & **With XAI Integration** \\ \cline{3-6}  & & & Developers cannot visualize what the model sees when the AV is motionless. Few decisions, such as turning the engine on and setting the AV in motion, moving uphill from a static state, taking a AV senses while stationary. & & When an AV is stationary, developers may readily inspect the attention maps created by XAI modules based on the input stream. Attention maps for different scenarios can be seen, which permit better delivery of results. \\ \hline \multirow{6}{*}{In the event of a traffic accident} & Reasoning for explicit decision-making based on what the AV perceives cannot be obtained by the user. & Developers cannot pre-mitigate possible threats due to the lack of the black box of the DL algorithm. & The user can be presented with real-time output generated by XAI modules and can readily acquire control of the vehicle if necessary. & Using XAI, developers may predict the cause of the AI model’s decision-making in AVs and rectify any discovered flaws. \\ \hline \multirow{6}{*}{Software maintenance} & Comparative findings for a fresh software release cannot be acquired, remaining as a black box to the naive user. & The deterioration of software and models caused due to data drift [20] is a tough problem to identify without XAI integrated. & New software patches can be distinctly analyzed and reviewed by naive users making transparent for the users. & Because XAI modules are independent of training data, they enable effective model monitoring over time to monitor over time to avoid and address software degradation. \\ \cline{3-6}  & Comparative findings for a fresh software release cannot be acquired, remaining as a black box to the naive user. & The deterioration of software and models caused due to data drift [20] is a tough problem to identify without XAI integrated. & New software patches can be distinctly analyzed and reviewed by naive users making transparent for the users. & Because XAI modules are independent of training data, they enable effective model monitoring over time to avoid and address software degradation. \\ \hline \multirow{6}{*}{When AV is motion [21]} & & & Real-time semantic map predictions and analyses can be provided with XAI integration. & Real-time semantic map predictions and analyses can be provided with XAI integration. \\ \cline{3-6}  & Although real-time prediction are easily available, the motion of an AV is heavily influenced by physical physical characteristics that may go may go unnoticed during conventional output scrutiny and analysis. & Although real-time semantic heat maps can be generated at various instances corresponding to various physical phenomena. Physical context refer to situations, such as the sudden emergence of speed breakers, signboards, the existence of potholes on the road, and so on. \\ \hline \hline \end{tabular}
\end{table}
Table 3: A few scenarios with and without XAI-integrated modules in AVs are compared.  
can subsequently be used to train the model. After the model is trained, its performance is evaluated using multiple measures. For evaluating the model and preprocessed frames are sent through the model to acquire the predicted outputs. These predictions can then be explained with the help of XAI to comprehend the model's inner workings and assess its credibility.