Citation

Odat E, Shamma JS, Claudel CG. IEEE Trans. Intel. Transp. Syst. 2018; 19(5): 1593-1606.

Copyright

(Copyright © 2018, IEEE (Institute of Electrical and Electronics Engineers))

DOI

10.1109/TITS.2017.2727224

PMID

unavailable

Abstract

In this paper, a new sensing device that can simultaneously monitor traffic congestion and urban flash floods is presented. This sensing device is based on the combination of passive infrared sensors (PIRs) and ultrasonic rangefinder, and is used for real-time vehicle detection, classification, and speed estimation in the context of wireless sensor networks. This framework relies on dynamic Bayesian Networks to fuse heterogeneous data both spatially and temporally for vehicle detection. To estimate the speed of the incoming vehicles, we first use cross correlation and wavelet transform-based methods to estimate the time delay between the signals of different sensors. We then propose a calibration and self-correction model based on Bayesian Networks to make a joint inference by all sensors about the speed and the length of the detected vehicle. Furthermore, we use the measurements of the ultrasonic and the PIR sensors to perform vehicle classification. Validation data (using an experimental dual infrared and ultrasonic traffic sensor) show a 99% accuracy in vehicle detection, a mean error of 5 kph in vehicle speed estimation, a mean error of 0.7m in vehicle length estimation, and a high accuracy in vehicle classification. Finally, we discuss the computational performance of the algorithm, and show that this framework can be implemented on low-power computational devices within a wireless sensor network setting. Such decentralized processing greatly improves the energy consumption of the system and minimizes bandwidth usage.

Language: en

Keywords

Acoustics; belief networks; calibration; combined passive infrared-ultrasonic sensors; cross correlation; decentralized processing; dual infrared-ultrasonic traffic sensor; dynamic Bayesian networks; dynamic Bayesian Networks; Estimation; Feature extraction; gaussian mixture models; heterogeneous data fusion; inference mechanisms; infrared detectors; joint inference; low-power computational devices; object detection; PIR sensors; real-time vehicle detection; road vehicles; self-correction model; sensing device; sensor fusion; Sensor phenomena and characterization; Sensor systems; signal classification; speed estimation; time delay estimation; traffic congestion monitoring; traffic engineering computing; ultrasonic rangefinder; ultrasonic transducers; urban flash floods; vehicle classification; Vehicle detection; vehicle length estimation; vehicle speed estimation; velocity measurement; wavelet transform; wavelet transform-based method; wavelet transforms; wireless sensor network setting; wireless sensor networks; Wireless sensor networks