# OD-XAI: Explainable AI-Based Semantic Object Detection for Autonomous Vehicles
## 2 Preliminaries
### Explainable Artificial Intelligence (XAI)  
The popularity of AI has soared in the past several decades. The number of articles (globally) on AI has increased more than three-fold since 2000 [15]. AVs have considerably improved in the last few years. With ML and DL approaches, AVs have become more precise and have expanded tremendously. This increasing popularity of AI has encouraged developers to design larger and more sophisticated models, which, in hindsight, produces better results, but it is increasingly becoming harder to analyze and interpret. The human mind will sometimes not accept concepts that it cannot grasp. For example, with AI, if people cannot understand the \"why?\" and \"why not?\" of the AI decision-making, trust management will become a major concern. There are six types of explanations in XAI that assist in increasing the understanding of the model. 1. _Global explanation:_ This type of explanation attempts to elaborate how models arrive at their predictions via visual charts, graphs, or images. Holistic explanations are generally obtained in techniques that perform global explanations. 2. _Contrastive explanation:_ Such type of explanation tends to pinpoint the \"why\" and the \"why not\" of the model prediction and behaviour. Contrastive explanations are generally used in determining the minimal changes in input that can affect the final predictions. 3. _Local explanation:_ This approach is totally opposite from the global explanation. While global explanations are used to obtain holistic representation, local explanations tend to focus more on specific features and attributes of the model. 4. _What-if explanation:_ This type of XAI technique is used to understand model attributes and predictions. 5. _Counterfactual explanation:_ This type of explanation answers \"How to\" arrive at the desired outcome. 6. _Example-based explanation:_ Such a technique uses model prediction on a sample dataset to explain the general output or the underlying data distribution of the whole dataset. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Research Contribution No.** & **Mapped Sections** & **Section Name** \\ \hline
1 & 3, 4, 6 & System Model and Problem Formulation, Proposed Approach, Results and Discussion \\ \hline
2 & 5.2.1, 5.2.2 & GradCAM, Saliency Maps \\ \hline
3 & 5.2.3 & Observational Explanations \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Contribution mapping with respective sections in the paper. This explanation technique is based on the hypothesis that \"similar inputs\" will generate \"similar outputs\". To overcome the aforementioned issues, the notion of XAI was established. The sole purpose of XAI is to make it easy for everybody, including engineers and general users, to grasp the model and its predictions. XAI attempts to decode the exact model working and decision-making. As the parameters and layers expand inside a model, it becomes more complex. These types of models are black box models [16; 17]. A black box model can be viewed as a black box, one in which an input is processed to obtain an output where the internal process itself is abstract and unknown. To understand the backends of such models, we need to open the black box and delve inside its working. This is exactly what XAI seeks to do. Some AI methods that are considered black box models include the artificial neural network (ANN), tree ensemble (TE), support vector machine (SVM), and deep neural network (DNN). XAI is increasing in popularity because it solves the following three questions [18]: What drives the model predictions? Why did the model take a specific decision? How can we trust the model? The first question verifies the fairness of the model, which implies that the model is driven by features that are relevant in decision-making [18]. The second question assures accountability and dependability of the model. The model should be able to justify and validate why some of the characteristics are more influential in the model's decision-making [18].