A Cost-Effective and Climate-Resilient Air Pressure System for Rain Effect Reduction on Automated Vehicle Cameras

2026-02-19

AI Summary

Rain significantly degrades the perception performance of cameras on autonomous vehicles, and existing physical protection solutions suffer from high cost and poor scalability. This work proposes a low-cost, modular, pneumatic-driven rain-clearing system that requires no sensor replacement and can be seamlessly integrated into multi-camera platforms without relying on hydrophobic coatings or expensive protective hardware. Evaluated in real-world rainy conditions, the system improves the accuracy of deep learning–based pedestrian detection from 8.3% to 41.6%, substantially enhancing perceptual robustness. The approach demonstrates strong climate adaptability and holds promising potential for sustainable transportation applications.

Technology Category

• Computer Vision: Vision for Robotics & Autonomous Driving
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Transparent, Interpretable, Explainable ML

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG

Abstract

Recent advances in automated vehicles have focused on improving perception performance under adverse weather conditions; however, research on physical hardware solutions remains limited, despite their importance for perception critical applications such as vehicle platooning. Existing approaches, such as hydrophilic or hydrophobic lenses and sprays, provide only partial mitigation, while industrial protection systems imply high cost and they do not enable scalability for automotive deployment. To address these limitations, this paper presents a cost-effective hardware solution for rainy conditions, designed to be compatible with multiple cameras simultaneously. Beyond its technical contribution, the proposed solution supports sustainability goals in transportation systems. By enabling compatibility with existing camera-based sensing platforms, the system extends the operational reliability of automated vehicles without requiring additional high-cost sensors or hardware replacements. This approach reduces resource consumption, supports modular upgrades, and promotes more cost-efficient deployment of automated vehicle technologies, particularly in challenging weather conditions where system failures would otherwise lead to inefficiencies and increased emissions. The proposed system was able to increase pedestrian detection accuracy of a Deep Learning model from 8.3% to 41.6%.

Problem

• rain effect reduction
• automated vehicle cameras
• cost-effective hardware
• climate resilience
• perception reliability

Innovation

• cost-effective hardware
• climate-resilient system
• rain mitigation
• automated vehicle perception
• sustainable transportation