Perception and Sensing for Autonomous Vehicles Under Adverse Weather Conditions: A Survey
Yuxiao Zhang, Alexander Carballo, Hanting Yang, Kazuya Takeda

TL;DR
This survey comprehensively reviews the challenges adverse weather poses to autonomous vehicle perception and sensing, analyzing solutions, datasets, and future sensor technologies to advance autonomous driving under such conditions.
Contribution
It provides a holistic overview of hardware and software solutions, datasets, and future sensor approaches addressing adverse weather challenges in autonomous driving.
Findings
Analysis of weather impacts on ADS sensors
Survey of perception enhancement techniques
Identification of future sensor development trends
Abstract
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, autonomous driving under adverse weather conditions has been the problem that keeps autonomous vehicles (AVs) from going to level 4 or higher autonomy for a long time. This paper assesses the influences and challenges that weather brings to ADS sensors in an analytic and statistical way, and surveys the solutions against inclement weather conditions. State-of-the-art techniques on perception enhancement with regard to each kind of weather are thoroughly reported. External auxiliary solutions, weather conditions coverage in currently available datasets, simulators, and experimental facilities with weather chambers are distinctly sorted out. Additionally, potential future ADS sensors candidates…
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Taxonomy
TopicsAir Quality Monitoring and Forecasting · Advanced Neural Network Applications · Autonomous Vehicle Technology and Safety
