# An Overview of Autonomous Vehicles Sensors and Their Vulnerability to Weather Conditions
## 1 Introduction  
According to the Society of Automotive Engineers (SAE), the Advanced Driver-Assistance System (ADAS) is a six-tiered system that categorizes the different levels of autonomy. It ranges from vehicles being solely human-driven to those that are completely autonomous or self-driving, as shown in Figure 1[1]. In order to achieve higher autonomy levels, autonomous vehicles (AVs) must rely on a combination of sensors and software to perceive the surrounding environment and navigate without any human intervention. Currently, cutting-edge sensor technologies are rapidly growing to improve autonomous transportation and mobility that is safe for pedestrians and riders [2]. This has motivated more researchers and engineers, from a variety of fields and backgrounds, to engage in this process and to address all the interconnected challenges that accompanies it. Figure 2 provides a brief summary of the influential events in the history of AVs [3, 4]. As a result of the growing interest in AVs, several testing and validation procedures have been developed to optimize their performance for maximum safety before being deployed onto public roadways and infrastructures [5]. AVs can be tested in simulations or in the real world. Engineers can employ actual vehicles instead of models in physical tests, which provide realistic testing settings. However, due to the high risk associated with suchtests, regulations restrict their use in densely populated areas, such as cities. Furthermore, the probability of encountering edge cases is low, and even when such cases occur, the repeatability of these cases represents a challenge. According to recent reports [6; 7], conducting empirical field experiments to validate the safety of AVs in an acceptable timescale is unfeasible. As a result, verifications and testing in a virtual environment have the ability to bridge the gap and enable AV systems to be evaluated in a rigorous, controlled, and rapid manner. Nevertheless, the research community recognizes the challenge of generating realistic scenarios that mimics the physical world while depending solely on software [8]. For instance, according to the National Highway Traffic Safety Administration (NHTSA), over 5,891,000 vehicle crashes occur each year on average, with approximately 1,235,000 being attributed to adverse weather conditionsm such as snow, rain, fog, and strong winds. Approximately half (\(\sim\)46%) of weather-related accidents are caused by rain, and approximately \(\sim\)17% are caused by snow. Therefore, considering the effects of different weather conditions on the response of AV sensors can significantly improve the validity of simulated tests and generate more realistic scenarios. This paper presents a comprehensive literature review of the effects of different weather phenomena on an AV's capability to perceive their surroundings. The details of the technological aspects involved in the development of AVs are discussed, specifying the different principle of operation for AV sensors ecosystem. In addition, the electromagnetic properties of these sensors are mapped to various weather conditions in a quantitative fashion. The remainder of this paper is organized as follows: Section 2 details the technologies normally used in AV sensors to capture information about the surrounding environment; Section 3 discusses the weather effects on AV sensors functionality; Section 4 describes the synopsis of automotive sensor strengths; and Section 5 includes our conclusions and future work. Figure 1: Society of automotive engineers automation levels.