This work proposes a method based on field measurements and unsupervised machine learning to faithfully reproduce in controlled environments real weather conditions captured during wintertime in Ontario, Canada. In this respect, experiments in climatic wind tunnels provide a solution for simulating the operating conditions that the autonomous vehicles will confront. Since the sensing of the surroundings by these vehicles relies on optical sensors such as lidars and cameras, it is essential to ensure the robustness of these systems from the early stages of the project. A major challenge encountered in the development of systems exposed to weather stressors, such as autonomous vehicles and unstaffed aerial vehicles, is to ensure their proper functioning under adverse rain or snow conditions.
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