## 3 An overview of AVs  
The Society of Automotive Engineers (SAE) has established a classification system that divides Autonomous Driving Systems (ADS) into six levels of automation, ranging from Level 0 (No Automation) to Level 5 (Full Automation), as illustrated in Figure 4[22]. This system is pivotal in understanding the evolution of autonomous vehicles (AVs), which are equipped with advanced sensors and software to navigate independently, thereby enhancing safety technologies and autonomy. Such a progression necessitates a collaborative effort among scientists and engineers across various disciplines to address the complex challenges associated with AV development [23]. The SAE automation levels provide a standardized framework that is critical for evaluating the capabilities and limitations of AVs at different stages of their development. By categorizing AVs into these distinct levels, it becomes possible to systematically assess the technological advancements and hurdles encountered at each level. This structured approach is instrumental in setting realistic expectations about AV performance, shaping regulatory frameworks, and educating the public on the operational capabilities of AVs under various scenarios. Thus, the classification of AVs according to SAE levels is essential for advancing the field of autonomous driving, guiding its regulatory landscape, and informing societal understanding of these emerging technologies. Initially, in the 1920s, AVs were referred to as \"phantom autos\" due to their lack of a human driver, relying on remote control [24]. Significant advancements in AV technology emerged in the 1980s, notably with Pomerleau's \"Autonomous Land Vehicle in a Neural Network\" project [25]. This project demonstrated the potential of neural networks to significantly enhance autonomous navigation systems. The Defense Advanced Research Projects Agency (DARPA) initiated the inaugural Grand Challenge in 2004 with the primary aim of stimulating research and the advancement of AVs. Subsequent to the 2004 challenge, DARPA hosted additional grand challenge events in 2005 and 2007, famously recognized as the Urban Challenge [25]. Developments in recent times have resulted in the global implementation of AVs. Some manufacturers have achieved Level 4 SAE standard [26] autonomy through the use of LiDAR technology. Google started secretly building its first AV in 2009, and, on May 1, 2012, in Las Vegas, they successfully completed their first autonomous driving test [27]. The UK government launched a competition in 2014 with the goal of encouraging and developing AVs [28]. The Mcity driverless shuttle project was started recently by the University of Michigan. It is the first Level 4 automated shuttle project in the US [29]. However, due to safety considerations, AVs cannot operate in severe rain or snow. Although testing and research have been carried out in inclement weather, problems such as the persistent usage of windshield wipers may result in an operational stoppage. Low temperatures impacted charging during the night and limited daily operating hours for the Sohjoa Baltic Project [30]. Tesla's autopilot works well in mild rain and snow and has obvious lane markers, but it struggles in inclement weather [31]. Regulation (EU) 2019/2144 was introduced by the European Parliament and Council in 2019. This was the first time that guidelines pertaining to automated and fully autonomous vehicles had been established [32]. When Waymo (formerly Google's self-driving division, rebranded as Waymo in 2016) announced that their \"Waymo Driver\" had successfully completed 20 million self-driven miles and carried out simulations that equated to 15 billion miles, it marked an important turning point in the growth of AVs [33]. While researchers are striving to develop sensors for different weather scenarios, thorough studies that address every facet are still absent. The AVs use environment-sensing skills to function with the least amount of human interaction [34].