Intelligent Vehicle Monitoring: Distributed-Acoustic-Sensor-Enabled Smart Road Infrastructure

A distributed acoustic sensor (DAS)-enabled smart vehicle monitoring system is investigated in this article to detect the type and passenger occupancy of vehicles for intelligent transportation systems (ITSs). Accurate detection of the number of occupants and the vehicle type is critical for ITS to monitor the occupancy of vehicles, improve vehicle operation efficiency, and achieve intelligent traffic management. We have developed several deep learning (DL) algorithms for occupancy detection and vehicle type discrimination according to the signals provided by DAS. To be more specific, a novel type of basic neural network structure, namely sparse residual (SR) block is proposed, and several DL models are developed for DAS signals based on the basic SR block unit. The proposed DL approaches are tested using a unique dataset collected from a road experiment. The test results indicate that 1) the proposed SR network (SR-Net) and Alex SR (Alex-SR) network can achieve detection accuracy of over 90%; 2) the proposed models exhibit superior convergence, stability, and accuracy than the conventional residual network (ResNet); and 3) the proposed DL solutions have superiority in terms of complexity and model size compared to benchmarks.