Autonomous vehicles rely on perception systems to understand their surroundings for further navigation missions. Cameras are essential for perception systems due to the advantages of object detection and recognition provided by modern computer vision algorithms, comparing to other sensors, such as LiDARs and radars. However, limited by its inherent imaging principle, a standard RGB camera may perform poorly in a variety of adverse scenarios, including but not limited to: low illumination, high contrast, bad weather such as fog/rain/snow, etc. Meanwhile, estimating the 3D information from the 2D image detection is generally more difficult when compared to LiDARs or radars. Several new sensing technologies have emerged in recent years to address the limitations of conventional RGB cameras. In this paper, we review the principles of four novel image sensors: infrared cameras, range-gated cameras, polarization cameras, and event cameras. Their comparative advantages, existing or potential applications, and corresponding data processing algorithms are all presented in a systematic manner. We expect that this study will assist practitioners in the autonomous driving society with new perspectives and insights.

翻译：自主车辆依赖感知系统来理解其周围环境，以便执行后续导航任务。由于现代计算机视觉算法在物体检测与识别方面的优势，摄像头相较于激光雷达和雷达等其他传感器而言，在感知系统中至关重要。然而，受限于其固有的成像原理，标准RGB摄像头在多种不利场景下可能表现欠佳，包括但不限于：低照度、高对比度、恶劣天气（如雾、雨、雪等）。同时，与激光雷达或雷达相比，从二维图像检测中估算三维信息通常更为困难。近年来，几种新型传感技术应运而生，旨在解决传统RGB摄像头的局限性。本文综述了四种新型图像传感器的原理：红外摄像头、距离选通摄像头、偏振摄像头及事件摄像头。我们系统性地介绍了它们各自的比较优势、现有或潜在应用，以及相应的数据处理算法。我们期望这项研究能为自动驾驶领域的从业者提供新的视角和见解。