恶劣天气
感知
计算机科学
汽车工业
传感器融合
自治
领域(数学分析)
工作(物理)
深度学习
人工智能
数据科学
机器学习
气象学
工程类
地理
航空航天工程
机械工程
生物
数学分析
神经科学
法学
数学
政治学
作者
Yuxiao Zhang,Alexander Carballo,Hanting Yang,Kazuya Takeda
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing
日期:2023-02-01
卷期号:196: 146-177
被引量:6
标识
DOI:10.1016/j.isprsjprs.2022.12.021
摘要
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.
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