Machine Learning Method for Road Vehicle Collected Data Analysis

Abstract

Abstract 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. 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. In this respect, experiments in climatic wind tunnels provide a solution for simulating the operating conditions that the autonomous vehicles will confront. 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. The purpose of this paper is not to investigate correlations between observed weather conditions and the characteristics of the precipitation encountered, but rather to establish a consistent method based on outdoor disdrometer data to identify critical parameters to be simulated in climatic wind tunnels. To achieve this goal, weather data such as temperature, relative humidity, and droplet size distribution were recorded at General Motors’s McLaughlin Advanced Technology Track (MATT) using an FD70 disdrometer and WXT530 weather transmitter, both manufactured by Vaisala, installed on a car provided by the Automotive Center of Excellent (ACE) team of the University of Ontario Institute of Technology. The implementation of the proposed method allowed the identification of precipitation clusters characterized by parameters of a theoretical model for particle size distributions fitted to the collected data.