# OD-XAI: Explainable AI-Based Semantic Object Detection for Autonomous Vehicles
## 1 Introduction  
In the early modern era, driving was an arduous task, as cars were essentially bigger and heavier motor-driven cycles. With advancements in technology, driving has become highly efficient and pleasant. However, with time, the frequency of accidents increased as the number of car buyers increased. Figure 1 shows a world map, highlighting the road traffic death rate, per country, per 100,000 people. Countries in Africa have the highest number of deaths, of which, Zimbabwe has a death rate of 61.90%. Countries such as India, Brazil, Mongolia, etc., highlighted in green, have death rates between 29.04% and 18.30%. The death rates in the United States, Mexico, and China, highlighted in purple, range from 18.01% to 8.17%. Countries highlighted in grey have the lowest death rates [1]. It can be inferred from these statistics that countries on the African continent have very substantial death rates due to the lack of technological advancements in those countries. The majority of these accidents occur due to the lack of attention by drivers and their highly unpredictable mindsets [2, 3]. Driving under the influence, reckless driving, drowsy driving, etc., can increase accidents.  
AVs, connected automobiles, AI, personalization, and data analytics play significant roles in giving great driving experiences. AVs are cars that can pretty much drive themselveswith little or no human interaction [4]. The latest technology in AVs allows drivers to relax while the car conducts all the tough work. This is considerably better than driving because AVs are machines with specified functions. They make decisions based on the information supplied to them. The data are given to the AVs by sensors installed around the vehicles [5]. These sensors provide critical statistics, such as the distance of the AV to other vehicles in the local region, the depth field, etc. Cameras installed on AVs are sensors that function as eyes and provide vision to the vehicles. Processing the sensor data allows the AV to drive autonomously without human interaction [6].  
Figure 2 displays the growing demand for AVs. The figure displays the growth rate of AVs in terms of sales (in thousands) from 2018 to 2025. A prediction analysis was conducted (and is displayed) to foresee the future AV sales trends. Using these prediction results, the anticipated sale of AVs from 2026 through the year 2030 was obtained. These future insights into AV sales provide foresight into the technology's projected demand. It signifies that the technology caters to many people, making it increasingly necessary to efficiently control, monitor, and upgrade. It is estimated that by 2035, AVs will control 25% of the worldwide commercial vehicle sector.  
Although the field algorithms from the field of DL generate more accurate results, it is difficult for the average user to comprehend. It is vital to make DL more explainable for wider and better usage of DL as a whole. XAI is a technology that allows naive consumers to grasp the complexity of AI. One use case of XAI in AVs involves explaining the semantic segmentation predictions by evaluating the input frames that the AV observed [7]. There is not much work on semantic segmentation with XAI integrated into it, notably in AVs. Ref. [8] suggests a toolbox to build a segmented image for a given input image and a specific model. Although very accurate results are expected from such methods, not much model interpretability is gained. Motivated by this, we used XAI techniques, such as GradCAM and saliency maps, to explain the segmentation maps generated from DL models in AVs. GradCAM and saliency maps are backpropagation-based XAI techniques that generate heat maps and localize important regions in the image. We also analyzed intermediate model layers and provide explanations for them.  
Figure 1: Worldwide road traffic death rate per 100,000 people [1].