A Bidirectional Long Short Term Memory Approach for Infrastructure Health Monitoring Using On-board Vibration Response

The growing volume of available infrastructural monitoring data enables the development of powerful datadriven approaches to estimate infrastructure health conditions using direct measurements. This paper proposes a deep learning methodology to estimate infrastructure physical parameters, such as railway track stiffness, using drive-by vibration response signals. The proposed method employs a Long Short-term Memory (LSTM) feature extractor accounting for temporal dependencies in the feature extraction phase, and a bidirectional Long Short-term Memory (BiLSTM) networks to leverage bidirectional temporal dependencies in both the forward and backward paths of the drive-by vibration response in condition estimation phase. Additionally, a framing approach is employed to enhance the resolution of the monitoring task to the beam level by segmenting the vibration signal into frames equal to the distance between individual beams, centering the frames over the beam nodes. The proposed LSTM-BiLSTM model offers a versatile tool for various bridge and railway infrastructure conditions monitoring using direct drive-by vibration response measurements. The results demonstrate the potential of incorporating temporal analysis in the feature extraction phase and emphasize the pivotal role of bidirectional temporal information in infrastructure health condition estimation. The proposed methodology can accurately and automatically estimate railway track stiffness and identify local stiffness reductions in the presence of noise using drive-by measurements. An illustrative case study of vehicle-track interaction simulation is used to demonstrate the performance of the proposed model, achieving a maximum mean absolute percentage error of 1.7% and 0.7% in estimating railpad and ballast stiffness, respectively.

Ontology for Scenarios for the Assessment of Automated Vehicles

The development of assessment methods for the performance of Automated Vehicles (AVs) is essential to enable and speed up the deployment of automated driving technologies, due to the complex operational domain of AVs. As traditional methods for assessing vehicles are not applicable for AVs, other approaches have been proposed. Among these, real-world scenario-based assessment is widely supported by many players in the automotive field. In this approach, test cases are derived from real-world scenarios that are obtained from driving data. To minimize any ambiguity regarding these test cases and scenarios, a clear definition of the notion of scenario is required. In this paper, we propose a more concrete definition of scenario, compared to what is known to the authors from the literature. This is achieved by proposing an ontology in which the quantitative building blocks of a scenario are defined. An example illustrates that the presented ontology is applicable for scenario-based assessment of AVs.