A Deep Learning Neural Network for Controlling Vehicle Weight, Speed, and Lane from Telecom Dark Fiber Distributed Acoustic Sensing

The vulnerability of urban assets, including soils, buildings, and infrastructure, is influenced by human activities, environmental factors, and societal vulnerabilities. Leveraging Distributed Acoustic Sensing (DAS) technology deployed on telecom fiber networks, we have developed an information model that facilitates data extraction, exchange, and networking to aid decision-making regarding civil infrastructure assets. In collaboration with the French company APRR-AREA, we focus on a 25 km stretch of telecom optic fiber along the A40 motorway concession — known as the "autoroute des Titans" — in eastern France. Ensuring the control of vehicle weight is crucial for the safety of highway transportation systems as it helps assess structural fatigue and traffic impact on roadways. Our initial goal is to predict heavy vehicle weight, speed, and lane using DAS records. For this prediction task, we designed various convolutional neural network (CNN) architectures, which take a 2-dimensional DAS panel as input. The ground truth labels are provided by a dynamic axle weighing system. The data is collected over 17 346 vehicles with weights ranging from 1,660 to 73,123 kilograms, over a 68-hour DAS acquisition period. We first train the networks over purely synthetic datasets. The forward problem, used to produce the synthetic panels, involves a Flamant-Boussinesq approximation for predicting the quasi-static road deformation caused by passing vehicles with different speeds and weights in the two lanes of circulation. We model DAS strain rate signal accounting for signal variability due to vehicles deviations in their lanes and a spatially variable path of the optic fiber. Our synthetic experiments yield promising results in predicting vehicle weight, speed and lane, allowing to disentangle the influence of vehicle weight and distance on fiber data. Moving forward, we plan to further refine our model by training, after image segmentation, the CNN on the A40 DAS dataset. We anticipate that this study will underscore the potential of DAS technology in complementing dedicated instrumentation for enforcing load limits in areas with heavy traffic.