Autonomous Vehicles (AVs) redefine transportation with sophisticated technology, integrating sensors, cameras, and intricate algorithms. Implementing machine learning in AV perception demands robust hardware accelerators to achieve real-time performance at reasonable power consumption and footprint. Lot of research and development efforts using different technologies are still being conducted to achieve the goal of getting a fully AV and some cars manufactures offer commercially available systems. Unfortunately, they still lack reliability because of the repeated accidents they have encountered such as the recent one which happened in California and for which the Cruise company had its license suspended by the state of California for an undetermined period [1]. This paper critically reviews the most recent findings of machine vision systems used in AVs from both hardware and algorithmic points of view. It discusses the technologies used in commercial cars with their pros and cons and suggests possible ways forward. Thus, the paper can be a tangible reference for researchers who have the opportunity to get involved in designing machine vision systems targeting AV

翻译：自动驾驶汽车（AV）通过集成传感器、摄像头和复杂算法，以先进技术重新定义了交通运输。在AV感知中实现机器学习需要强大的硬件加速器，以便在合理的功耗和占地面积下达到实时性能。为实现全自动驾驶的目标，基于不同技术的大量研发工作仍在进行，部分汽车制造商已提供商用系统。然而，这些系统仍缺乏可靠性，因为重复发生的事故，例如最近在加利福尼亚发生的一起事故，导致Cruise公司被该州无限期吊销执照[1]。本文从硬件和算法两个角度，严格综述了AV中机器视觉系统的最新研究成果。它讨论了商用汽车中使用的技术及其优缺点，并提出了可能的推进方向。因此，本文可为致力于设计面向AV的机器视觉系统的研究人员提供切实参考。