Autonomous driving is a research direction that has gained enormous traction in the last few years thanks to advancements in Artificial Intelligence (AI). Depending on the level of independence from the human driver, several studies show that Autonomous Vehicles (AVs) can reduce the number of on-road crashes and decrease overall fuel emissions by improving efficiency. However, security research on this topic is mixed and presents some gaps. On one hand, these studies often neglect the intrinsic vulnerabilities of AI algorithms, which are known to compromise the security of these systems. On the other, the most prevalent attacks towards AI rely on unrealistic assumptions, such as access to the model parameters or the training dataset. As such, it is unclear if autonomous driving can still claim several advantages over human driving in real-world applications. This paper evaluates the inherent risks in autonomous driving by examining the current landscape of AVs and establishing a pragmatic threat model. Through our analysis, we develop specific claims highlighting the delicate balance between the advantages of AVs and potential security challenges in real-world scenarios. Our evaluation serves as a foundation for providing essential takeaway messages, guiding both researchers and practitioners at various stages of the automation pipeline. In doing so, we contribute valuable insights to advance the discourse on the security and viability of autonomous driving in real-world applications.

翻译：自动驾驶是近年来因人工智能（AI）进步而获得极大关注的研究方向。根据对人类驾驶员依赖程度的不同，多项研究表明，自动驾驶车辆（AVs）能够通过提高效率减少道路事故数量并降低整体燃油排放。然而，针对该议题的安全研究结果不一，且存在若干空白。一方面，这些研究常忽略AI算法固有的脆弱性——此类脆弱性已知会危及系统安全。另一方面，针对AI的最常见攻击依赖于不切实际的假设，例如获取模型参数或训练数据集。因此，在现实应用中，自动驾驶是否仍能声称比人类驾驶更具优势尚不明确。本文通过审视自动驾驶车辆的当前格局并建立实用化威胁模型，评估了自动驾驶的内在风险。通过分析，我们提出具体论断，揭示了现实场景中自动驾驶优势与潜在安全挑战之间的微妙平衡。我们的评估为提供关键启示奠定了基础，可指导自动化流程各阶段的研究人员与从业者。以此方式，我们为推进关于现实世界中自动驾驶安全性与可行性的讨论贡献了重要见解。