Distributed Acoustic Sensor Systems for Vehicle Detection and Classification

Abstract

Intelligent transport systems (ITS) are pivotal in the development of sustainable and green urban living. ITS is data-driven and enabled by the profusion of sensors ranging from pneumatic tubes to smart cameras which are used to detect and categorise passing vehicles. Simple sensors, such as pneumatic tubes, are successfully deployed for counting passing vehicles but are not useful for vehicle tracking or re-identification. Smart cameras, on the other hand, collect comprehensive information but suffer from occlusion, patchy coverage, and compromised vision in adverse weather and visibility. This work explores a novel ITS data source based on optical fibre which acts as uninterrupted length of virtual sensors using a distributed acoustic sensor (DAS) system. Based on real DAS data collected in the field, we first present a study of latent DAS features that uniquely identify a given vehicle, otherwise referred to as vehicle signature. We formulate a classification problem that examines incoming DAS data to extract vehicle signatures and identify the different types of vehicle. To this end, we implement different classification methods and present a comparative performance analysis that reveals novel insights into the potential role of DAS in ITS applications. This work is a pilot study of DAS for vehicle classification that is driven by real-DAS data and validated by promising results where a vehicle type is correctly identified with 94% accuracy and the size of a vehicle with 95% accuracy.INDEX TERMS Intelligent transport system (ITS), distributed acoustic sensors (DAS), classification, vehicle type

IoT and machine learning for enabling sustainable development goals

“…Cameras and computer vision suffer from adverse weather and illumination conditions and obstructions due to moving objects such as tree branches blown by the wind. In contrast, DAS signals are much less, if at all, affected by these issues, as discussed in (Chiang et al, 2023). Given the nature of the optical fibre line underground, the illumination conditions do not impact the DAS signal at night or during the day.…”

“…Interruption in the DAS signal may be caused by a fibre splice or a fibre break; however, the locations of these are well known which makes overcoming their impact almost trivial in comparison with unpredictable camera obstructions. As for the weather conditions, the authors in (Chiang et al, 2023) posit that the mitigation of potential impact from rainfall on the quality of the DAS signal is manageable using basic signal processing techniques that can block the periodic signal generated by the rain. However, this concept has not been validated yet and would require more data collection and processing to be confirmed.…”

“…In the seminal work, (Chiang et al, 2023), present a proof of concept research in which they demonstrate that these DAS signals carry unique information about the moving object, referred to as signature, that allows identifying the type of moving vehicle with reliable accuracy. The authors formulate two different problems based on real DAS data collected in a controlled environment: 1) Identification of specific vehicle type, and 2) Categorisation of the size of a vehicle.…”

An Optical Frequency Domain Reflectometer’s (OFDR) Performance Improvement via Empirical Mode Decomposition (EMD) and Frequency Filtration for Smart Sensing

“…In a smart city, a sensing system can be used to monitor and control various parameters, such as temperature [ 4 ], humidity [ 5 ], noise level [ 6 ], air quality [ 7 ], traffic, traffic jams, etc. [ 8 , 9 , 10 ]. This allows one to automatically regulate the operation of smart city systems, for example, turn on heating when the temperature drops, regulate the speed of transport depending on road congestion, etc.…”