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%.

翻译：自动驾驶车辆的最新进展集中于提升恶劣天气条件下的感知性能；然而，针对物理硬件解决方案的研究仍然有限，尽管此类方案对于车辆编队等感知关键应用至关重要。现有方法，如亲水性或疏水性镜头及喷雾剂，仅能提供部分缓解效果，而工业级防护系统则意味着高成本且无法实现汽车部署的可扩展性。为应对这些局限性，本文提出了一种针对雨天条件的经济高效硬件解决方案，设计为可同时兼容多个摄像头。除其技术贡献外，所提方案支持交通系统的可持续性目标。通过实现与现有基于摄像头的传感平台的兼容性，该系统无需额外的高成本传感器或硬件更换，即可扩展自动驾驶车辆的操作可靠性。该方法减少了资源消耗，支持模块化升级，并促进了自动驾驶车辆技术更具成本效益的部署，特别是在恶劣天气条件下——若系统失效，将导致效率低下和排放增加。所提系统能够将深度学习模型的行人检测准确率从8.3%提升至41.6%。