Driving in the Rain: A Survey toward Visibility Estimation through Windshields

Morden, Jarrad; Caraffini, Fabio; Kypraios, Ioannis; Al-Bayatti, Ali H.; Smith, Richard · 2023 · OpenAlex

DOI: 10.1155/2023/9939174

archive: archived pipeline: cataloged verified

Get this paper ↗ (DOI — opens at the source; we link to it, we don't host it)

Summary

This review article addresses the critical challenge of estimating driver visibility through windshields during rainy conditions, a factor that significantly impairs safety in both human-driven and autonomous vehicles. The authors identify a gap in existing literature, noting that while research on autonomous driving has expanded, there is no comprehensive survey summarizing methodologies for rain visibility estimation, particularly regarding the physical obstruction of the windshield. The study aims to reorganize fragmented research by classifying current state-of-the-art solutions within the framework of Advanced Driver Assistance Systems (ADAS) and Autonomous Driving (AD) functions, specifically focusing on perception, control, and visualization components. The paper employs a systematic review methodology to analyze physical factors contributing to reduced visibility and existing technical solutions. It categorizes visibility reduction causes into rain intensity, vehicle speed, wiper frequency, depth of field perception, and time of day. The authors examine how raindrops, water films, and spray act as lenses that scatter light, reduce contrast, and create glare, thereby obscuring road markers and objects. The review evaluates various sensor technologies, distinguishing between passive sensors (monocular, stereo, and infrared cameras) and active sensors (LiDAR, radar, and ultrasonic devices). It further analyzes scene interpretation and reconstruction techniques, including object detection, tracking algorithms, and 3D scene reconstruction from multiple camera views, assessing their efficacy in adverse weather. Key findings highlight that visibility reduction is not solely dependent on rain intensity but is significantly exacerbated by vehicle speed, which increases the relative velocity of raindrops hitting the windshield, and by spray from other vehicles, which can drastically reduce sight to near-zero levels. The review identifies that while camera-based systems are central to ADAS perception, they struggle with the optical distortions caused by water accumulation on the windshield. The authors classify visibility estimation systems into three ADAS components: perception (sensor data interpretation), control (hardware navigation and steering), and visualization (driver communication). They note that most current research focuses on exterior sensor performance for fully autonomous vehicles, often neglecting the specific challenges of human perception through the windshield in semi-autonomous contexts. The significance of this work lies in its structured classification of visibility estimation methods, providing a clear roadmap for integrating visibility detection into ADAS architectures. By highlighting the limitations of current systems, particularly regarding real-time reconstruction of the driver’s view through a rain-obscured windshield, the paper calls for further research into model-based and data-driven approaches that can better handle physical obstructions. The authors conclude that improving visibility estimation is essential for enhancing the safety and reliability of intelligent transportation systems, urging the field to focus on bridging the gap between sensor perception and the actual visual experience of the driver or autonomous system in rainy conditions.

Key finding

The paper identifies that current literature on rain visibility estimation is fragmented and lacks a comprehensive comparison of methodologies across the perception, control, and visualization components of autonomous driving systems.

Methodology

review

Provenance

The full processing record for this entry. Every stage of this paper's journey through the pipeline is logged — what ran, with which tool and model, how many attempts it took, and when it last completed.

Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

Ranked by relevance to this paper. Hover a topic for its definition.

• weather rain fog snow