Vehicle classification and accurate speed calculation using multi-element piezoelectric sensor

Rajab, Samer; Mayeli, Ahmad; Refai, Hazem H.

doi:10.1109/ivs.2014.6856432

Cited by 16 publications

(10 citation statements)

References 12 publications

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“…Different kinds of sensors such as weigh-in-motion sensors [46], peizoelectric sensors [16], and fiber-optic sensors [47] are used to develop in-roadway-based vehicle classification systems. While these sensors are less frequently used for vehicle classification, the mechanism for vehicle classification is similar to other in-road-based solutions in that standard features are used for vehicle classification such as the axle count, axle spacing, and vehicle length.…”

Section: Other Technologiesmentioning

confidence: 99%

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Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

133

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A traffic monitoring system is an integral part of Intelligent Transportation Systems (ITS). It is one of the critical transportation infrastructures that transportation agencies invest a huge amount of money to collect and analyze the traffic data to better utilize the roadway systems, improve the safety of transportation, and establish future transportation plans. With recent advances in MEMS, machine learning, and wireless communication technologies, numerous innovative traffic monitoring systems have been developed. In this article, we present a review of state-of-the-art traffic monitoring systems focusing on the major functionality-vehicle classification. We organize various vehicle classification systems, examine research issues and technical challenges, and discuss hardware/software design, deployment experience, and system performance of the vehicle classification systems. Finally, we discuss a number of critical open problems and future research directions in an aim to provide valuable resources to academia, industry, and government agencies for selecting appropriate technologies for their traffic monitoring applications.

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Section: Other Technologiesmentioning

confidence: 99%

“…Rajab et al develop a multi-element piezoelectric sensor system which consists of 16 piezoelectric sensors [16]. Three main features, i.e., the number of tires, vehicle length, and axle spacing are used for vehicle classification.…”

Section: Other Technologiesmentioning

confidence: 99%

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

133

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“…A common property of intrusive solutions is that sensors (e.g., piezoelectric sensors [17], magnetometers [18] [7], vibration sensors [8], loop detectors [19]) are installed on or under a roadway. As Table I shows, intrusive approaches are capable of classifying a large selection of vehicle types with high classification accuracy leveraging close contact with passing vehicles that allow for securing high-precision sensor data.…”

Section: Related Workmentioning

confidence: 99%

“…The key intuition of their system is that different types of [17] motorcycles, passenger vehicles, two axle for tire unit, buses, two axles six tire single units, three axles single units, four or more axles single unit, four axles single trailer, five axles single trailer, seven axles single trailer, seven or more axles multi-trailer medium 86.9%…”

Section: Related Workmentioning

confidence: 99%

DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning

Won

Sahu

Park

2019

2019 IEEE 16th International Conference on Mobile Ad Hoc and Sensor Systems (MASS)

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A traffic monitoring system (TMS) is an integral part of Intelligent Transportation Systems (ITS) for traffic analysis and planning. This paper addresses the endemic cost issue of deploying a large number of TMSs to cover huge miles of two-lane rural highways (119,247 miles in U.S.). A low-cost and portable TMS called DeepWiTraffic based on COTs WiFi devices and deep learning is proposed. DeepWiTraffic enables accurate vehicle detection and classification by exploiting the unique WiFi Channel State Information (CSI) of passing vehicles. Spatial and temporal correlations of preprocessed CSI amplitude and phase data are identified and analyzed using deep learning to classify vehicles into five different types: motorcycle, passenger vehicle, SUV, pickup truck, and large truck. A large amount of CSI data of passing vehicles and the corresponding ground truth video data are collected for about 120 hours to validate the effectiveness of DeepWiTraffic. The results show that the average detection accuracy of 99.4%, and the average classification accuracy of 91.1% (Motorcycle: 97.2%, Passenger Car: 91.1%, SUV:83.8%, Pickup Truck: 83.3%, and Large Truck: 99.7%) are achieved at a very small cost of about $1,000.

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“…Axle detection accuracy ranged from 86.8 to 90.6% with the use of a single sensor and 100% when combining the data from the three accelerometers. An earlier work of the authors investigated vehicle and motorcycle classifications using single‐ and multi‐element piezoelectric sensors, where axle spacing algorithms were implemented for vehicle classification . Research presented in this paper provides the following contributions when compared with previous literature: Provision of machine learning method capable, with high accuracy, of classifying vehicles into relatively large number of classifications including motorcycles.…”

Section: Related Workmentioning

confidence: 99%

Classification and speed estimation of vehicles via tire detection using single‐element piezoelectric sensor

Rajab

Kalaa

Refai

2016

J of Advced Transportation

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Summary This paper presents novel vehicle classification technology by utilizing a single‐element piezoelectric sensor placed diagonally on a traffic lane to accurately identify vehicles. Novelty of this technique originates from using diagonally placed piezoelectric strip sensor and machine learning technology to provide a highly accurate and cost‐effective alternative to current vehicle classification systems. Diagonal placements of the piezoelectric strip sensor ensure detection of passing vehicle tires by facilitating vehicle classification process. Presented technology is capable of accurately classifying vehicles into a relatively large number of classifications, including motorcycle, which has proven to be a challenging category in present‐day commercial vehicle classifiers. Vehicle classification is a vital intelligent transportation systems application. Accurate data reporting aids suitable roadway design for safety and capacity and can also support other purposes, such as reporting highway congestion to the general public or providing area denseness data to interested businesses. To make a classification decision, a vehicle's signal is acquired from diagonal piezoelectric strip sensor, processed, and then applied to a machine learning algorithm. A speed estimation technique using the same single‐element piezoelectric sensor was also developed, tested, and compared with an embedded vehicle classifier currently used by the Oklahoma Department of Transportation. Testing on several highway sites indicated up to 97% classification accuracy. This paper presents a complete description of the developed system, including sensor installation, data acquisition and processing, and classification algorithm. Overall, the system offers a high‐performance cost‐effective solution for vehicle classification that minimizes roadwork typically required for loop and sensor installations of current systems. Copyright © 2016 John Wiley & Sons, Ltd.

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Vehicle classification and accurate speed calculation using multi-element piezoelectric sensor

Cited by 16 publications

References 12 publications

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

DeepWiTraffic: Low Cost WiFi-Based Traffic Monitoring System Using Deep Learning

Classification and speed estimation of vehicles via tire detection using single‐element piezoelectric sensor

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