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

Mohamed Sabry, Joseba Gorospe, Cristina Olaverri-Monreal

TL;DR

This work tackles the challenge of degraded camera perception in automated vehicles under rain by introducing a physical, cost-efficient Air Pressure System (APS) that directs a controlled air curtain onto camera lenses. The APS is a centralized, modular solution built from off-the-shelf components, compatible with multiple sensors, and designed to support scalable deployment in automotive sensing platforms. Experimental evaluation on a test vehicle using YOLOv4-tiny demonstrates a substantial improvement in pedestrian detection under rainy conditions, increasing from 8.3% without APS to 41.6% with APS over 10-second intervals, highlighting its practical impact for perception robustness and potential sustainability benefits through reduced sensor downtime and maintenance. The authors also outline avenues for future work, including extending the approach to LiDAR and optimizing airflow for broader ITS applications.

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%.

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

TL;DR

This work tackles the challenge of degraded camera perception in automated vehicles under rain by introducing a physical, cost-efficient Air Pressure System (APS) that directs a controlled air curtain onto camera lenses. The APS is a centralized, modular solution built from off-the-shelf components, compatible with multiple sensors, and designed to support scalable deployment in automotive sensing platforms. Experimental evaluation on a test vehicle using YOLOv4-tiny demonstrates a substantial improvement in pedestrian detection under rainy conditions, increasing from 8.3% without APS to 41.6% with APS over 10-second intervals, highlighting its practical impact for perception robustness and potential sustainability benefits through reduced sensor downtime and maintenance. The authors also outline avenues for future work, including extending the approach to LiDAR and optimizing airflow for broader ITS applications.

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%.
Paper Structure (6 sections, 3 figures, 1 table)

This paper contains 6 sections, 3 figures, 1 table.

Table of Contents

  1. Introduction
  2. Related Work
  3. The Proposed Air Pressure System
  4. Experimental Setup
  5. Results
  6. Conclusion and Future Work

Figures (3)

  • Figure 1: The figure illustrates the connectivity of the proposed Air Pressure System (APS) with the JKU-ITS research vehicle and the associated sensors to be cleaned.
  • Figure 2: The figure illustrates the APS system mounted on the JKU ITS research vehicle.
  • Figure 3: The figure illustrates the results from using the proposed APS system. The figure shows there consecutive frames taken from a Basler camera with Yolov4-tiny used as a Deep learning model to test the effect of the system. (a) shows the results from a clean camera lens. (b) shows the results in rain without the proposed system. (c) shows the results while raining and using the proposed APS.