Perception and sensing for autonomous vehicles under adverse weather conditions: A survey
Perception and sensing for autonomous vehicles under adverse weather conditions: A survey
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自动驾驶汽车在恶劣天气条件下的感知和传感：一项调查
DOI：
10.1016/j.isprsjprs.2022.12.021
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发表时间：
2023-01-09
影响因子：
12.7
通讯作者：
Takeda，Kazuya
中科院分区：
文献类型：
--
作者：
Zhang，Yuxiao;Carballo，Alexander;Takeda，Kazuya
自动驾驶系统(ADS)为汽车行业开辟了一个新的领域，并以更高的效率和舒适的体验为未来的交通提供了新的可能性。然而，在恶劣天气条件下自动驾驶的感知和感知一直是阻碍自动驾驶车辆迈向更高自主性的问题。本文系统地评估了天气给ADS传感器带来的影响和挑战，并对恶劣天气条件下的解决方案进行了综述。详细报道了关于各种天气、天气状况分类和遥感的感知增强的最新算法和深度学习方法。传感器融合解决方案、当前可用的数据集中的天气状况覆盖范围、模拟器和实验设施被归类。此外，还讨论了潜在的ADS传感器候选对象和发展方向，如V2X(Vehicle To Everything)技术。通过研究各种主要的天气问题，并回顾近年来传感器和计算机科学的解决方案，这项调查指出了不利天气问题在感知和传感方面的主要发展趋势，即先进的传感器融合和更复杂的机器学习技术；以及出现的1550纳米激光雷达带来的限制。总体而言，这项工作有助于全面概述在不利天气条件下感知和传感研究发展的障碍和方向。
Automated Driving Systems (ADS) open up a new domain for the automotive industry and offer new possibilities for future transportation with higher efficiency and comfortable experiences. However, perception and sensing for autonomous driving under adverse weather conditions have been the problem that keeps autonomous vehicles (AVs) from going to higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in a systematic way, and surveys the solutions against inclement weather conditions. State-of-the-art algorithms and deep learning methods on perception enhancement with regard to each kind of weather, weather status classification, and remote sensing are thoroughly reported. Sensor fusion solutions, weather conditions coverage in currently available datasets, simulators, and experimental facilities are categorized. Additionally, potential ADS sensor candidates and developing research directions such as V2X (Vehicle to Everything) technologies are discussed. By looking into all kinds of major weather problems, and reviewing both sensor and computer science solutions in recent years, this survey points out the main moving trends of adverse weather problems in perception and sensing, i.e., advanced sensor fusion and more sophisticated machine learning techniques; and also the limitations brought by emerging 1550 nm LiDARs. In general, this work contributes a holistic overview of the obstacles and directions of perception and sensing research development in terms of adverse weather conditions.