###### Abstract
The implications of this kind of attack have the potential to be devastating due to the fact that compromised sensor data might lead to improper driving reactions, accidents, and even deaths [4]. Camera hacking is one major concern. In addition to incorrectly reading road signs, an attacker could also manipulate the camera feed to hide obstacles or other vehicles or create phantom objects, leading to incorrect and potentially disastrous decision-making by the vehicle's AI. Similarly, LiDAR and radar systems, which are used by AVs to create a detailed 3D map of their surroundings, could also be targeted. An attacker could potentially feed the system false data, causing it to'see' obstacles that do not exist, or fail to detect those that do. The GPS system, which is crucial for navigation, can also be a target. For instance, GPS spoofing attacks can feed false location information to the AV, leading it to go off course or even to dangerous locations. As an example, a hacked camera can incorrectly read a speed limit sign, putting the lives of the vehicle's passengers and anybody else who uses the road in danger. Beyond the vehicle itself, the era of interconnected autonomous vehicles is upon us, where vehicles can communicate and share environmental data not just amongst themselves but also with wider infrastructural systems [5]. Although this networking capability enhances operational efficiency, it is susceptible to potential cyber-attacks. Embedded control systems such as engine control units (ECUs), which currently manage functions like electric window controls, may become vulnerable. Any malicious alteration in the programming code of these critical components during design or implementation can degrade hardware performance or remove crucial data, leading to potentially serious consequences [6]. A noteworthy instance of such an intrusion was documented in [7], where a virus was developed to manipulate messages transmitted via the controller area network (CAN) bus, a vital communication system linking all vehicle components. This malware was capable of remotely locking the doors of a vehicle by intercepting the corresponding communications of the core system. Such security vulnerabilities related to the CAN bus pose significant threats to driver safety and privacy, and it is crucial to undertake countermeasures [8]. In this survey, and, in particular, in Table 1, we review and present the state-of-the-art surveys in the field of autonomous vehicles and categorized them based on their scope. Moreover, after presenting each work, we highlight the scope of our work and the gaps in the different fields of autonomous vehicles that we aim to reduce. The purpose of this work is to investigate, highlight, and contribute to the understanding and mitigation of the difficulties that exist in AVs by presenting all factors that frame them, including the following:  
* We present an overview of the state of the art in this field of study by providing an analysis that is comprehensive, specific, and up-to-date on the safety problems that are associated with autonomous cars and the countermeasures that are related to them;
* We analyze all potential attack vectors on autonomous vehicles, an endeavor that has not been previously undertaken to this extent. To the best of our knowledge, this paper represents the first comprehensive exploration of such a wide range of potential threats--a significant contribution relative to previous surveys, which typically address only a subset of these threats;
* We highlight and explore unresolved issues and potential research directions in this domain, thereby creating a roadmap for future studies in academia;
* We present a concrete survey that aims to help readers understand the broader scope of AVs by navigating different sections in the survey and gaining knowledge that is summarized based on all references presented here, without requiring review of all recent works. The remainder of this article is organized as follows. In Section 2, an overview of autonomous vehicles (AVs) is presented.