# OD-XAI: Explainable AI-Based Semantic Object Detection for Autonomous Vehicles
## 1 Introduction
### Research Contributions  
The significant contributions of the paper are as follows:  
* We propose an XAI integrated AV system that improves the explainability of semantic segmentation models, which are considered black box models and are difficult to analyze and comprehend. To conduct semantic segmentation on road images in AVs, the proposed system was custom-trained on the KITTI road dataset.
* To comprehend the model's internal workings and which areas it concentrates on when input images are processed through the trained model, XAI methods, such as Grad-Cam and saliency maps, were used. Logical connections are derived and are mapped with the application, thereby producing easy-to-understand explanations for real world scenarios.
* We present a novel approach to explain the intermediate model workings. It analyzes the intermediate layers of the DL model by visualizing their outputs and deriving accurate interpretations. The analysis is extensive; all the conclusions are expanded upon by offering logical links and reasons.  
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline
**Author** & **Year** & **Objective** & **Research Findings** \\ \hline [PERSON] et al. [10] & 2019 & A fully connected network (FCN) is used to perform semantic segmentations on road images. & Does not offer information on the explainability of the model’s judgments. \\ \hline [PERSON] et al. [11] & 2020 & Imitation learning-based agent equipped with attention maps is proposed. The attention model is used to determine which section of the picture is most significant. & There are no intermediate model explanations that explain how the model works within. \\ \hline [PERSON] et al. [12] & 2021 & Proposed a method for a human driver to use intentional gazing to deliver scene forecasts to an intelligent driving system. & If the driver is not paying attention while driving, the system may not work correctly because it relies significantly on the driver’s gaze when making decisions. The application (being sensitive) requires use of XAI. However no explainability principles are employed. \\ \hline [PERSON] et al. [13] & 2021 & Introduced AUTO-DISCERN, an automated explainable decision-making system for AVs that combines common sense reasoning with answer set programming. & There is no visual depiction of the model’s decision-making. \\ \hline [PERSON] et al. [7] & 2021 & Proposes a system to detect malicious vehicles in vehicular ad hoc networks (VANETs) and incorporates XAI to provide interpretability of ML models. & This technology only works in VANETs, requiring the presence of two or more AVs. The proposed system is based solely on numerical data and does not consider road images. It does not provide insight into the explainability of the model’s decision. \\ \hline [PERSON] et al. [14] & 2022 & CNNs, autoencoders, pyramid networks, and bottleneck residual networks are used to create intelligent semantic segmentations for self-driving vehicles. & The proposed approach does not provide information on the models interpretability. \\ \hline The proposed approach & 2022 & Integration of XAI with semantic segmentation for AI systems in AVs. & The proposed approach produces visual explanations for the segmentation maps produced by the semantic segmentation models using various XAI techniques. \\ \hline \end{tabular}
\end{table}
Table 1: Comparative analysis of various approaches for XAI-based autonomous driving.