A Wireless Accelerometer-Based Automatic Vehicle Classification Prototype System

Ma, Wenteng; Xing, Daniel; McKee, Adam; Bajwa, Ravneet; Flores, Christopher; Fuller, Brian M.; Varaiya, Pravin

doi:10.1109/tits.2013.2273488

Cited by 98 publications

(46 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Like the in-roadwaybased systems, different types of sensors have been utilized. Some of the most widely used sensors include magnetometers [21] [22], accelerometers [23], and acoustic sensors [24]. Recently, advanced sensors such as Laser Infrared Detection and Ranging (LIDAR) [25] [4], infrared sensors [26], and Wi-Fi transceivers [27] have been employed.…”

Section: Taxonomy Of Vehicle Classification Technologiesmentioning

confidence: 99%

“…Different types of sensors have been integrated with magnetic sensors to enhance the effectiveness of vehicle classification. For example, Ma et al propose a WSN consisting of magnetic sensors and accelerometers [23]. More specifically, the magnetic signatures collected with magnetic sensors are used to estimate the vehicle speed, and the accelerometer is used to count the number of axles and estimate the axle spacing between each pair of axles leveraging the measured vehicle speed.…”

Section: B Magnetic Sensorsmentioning

confidence: 99%

See 1 more Smart Citation

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

147

View full text Add to dashboard Cite

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.

show abstract

Section: Taxonomy Of Vehicle Classification Technologiesmentioning

confidence: 99%

Section: B Magnetic Sensorsmentioning

confidence: 99%

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Won

2020

IEEE Access

147

View full text Add to dashboard Cite

show abstract

“…However, it has been shown that single-loop detectors can approach a similar level of accuracy [23]. However, as discussed in [32], loop-based methods do not perform well under congested traffic. Lamas-Seco et al [24] propose to exploit spectral features, which do not depend on the vehicle speed and allow to perform vehicle classification with a single-loop approach.…”

Section: B Vehicle Detection and Classification Systemsmentioning

confidence: 99%

“…Ma. et al [32] use a combination of magnetometers and accelerometers, which is compared against video images and a nearby WIM system. Although the resulting accuracy is 99% for distinguishing between 2-axle cars, 3-axle cars and 5-axle heavy trucks, the size of the data set is relatively low.…”

Section: B Vehicle Detection and Classification Systemsmentioning

confidence: 99%

The Channel as a Traffic Sensor: Vehicle Detection and Classification Based on Radio Fingerprinting

Sliwa

Piatkowski

Wietfeld

2020

IEEE Internet Things J.

View full text Add to dashboard Cite

Ubiquitously deployed Internet of Things (IoT)based automatic vehicle classification systems will catalyze datadriven traffic flow optimization in future smart cities and will transform the road infrastructure itself into a dynamically sensing Cyber-physical System (CPS). Although a wide range of different traffic sensing systems has been proposed, the existing solutions are not yet able to simultaneously satisfy the multitude of requirements, e.g., accuracy, robustness, cost-efficiency, and privacy preservation. In this paper, we present a novel approach, which exploits radio fingerprints -multidimensional attenuation patterns of wireless signals -for accurate and robust vehicle detection and classification. The proposed system can be deployed in a highly cost-efficient manner as it relies on off-the-shelf embedded devices which are installed into existing delineator posts. In a comprehensive field evaluation campaign, the performance of the radio fingerprinting-based approach is analyzed within an experimental live deployment on a German highway, where it is able to achieve a binary classification success ratio of more than 99% and an overall accuracy of 93.83% for a classification task with seven different classes.

show abstract

“…Averbuch and colleagues developed a diffusion map based framework to detect moving vehicles based on wavelet packets within the dynamic programming framework [5]. In [6], a prototype automatic vehicle classification system was developed, where a grid of accelerometers are installed on roadways to characterize road vibrations to classify vehicle based on the number of axles. The corresponding performance compared with the ground truth is exceedingly valuable at approximately 99%.…”

Section: Introductionmentioning

confidence: 99%

Engine classification using vibrations measured by laser Doppler vibrometer on different surfaces

et al. 2015

View full text Add to dashboard Cite

In our previous studies, vehicle surfaces' vibrations caused by operating engines measured by Laser Doppler Vibrometer (LDV) have been effectively exploited in order to classify vehicles of different types, e.g., vans, 2-door sedans, 4-door sedans, trucks, and buses, as well as different types of engines, such as Inline-four engines, V-6 engines, 1-axle diesel engines, and 2-axle diesel engines. The results are achieved by employing methods based on an array of machine learning classifiers such as AdaBoost, random forests, neural network, and support vector machines. To achieve effective classification performance, we seek to find a more reliable approach to pick authentic vibrations of vehicle engines from a trustworthy surface. Compared with vibrations directly taken from the uncooperative vehicle surfaces that are rigidly connected to the engines, these vibrations are much weaker in magnitudes. In this work we conducted a systematic study on different types of objects. We tested different types of engines ranging from electric shavers, electric fans, and coffee machines among different surfaces such as a white board, cement wall, and steel case to investigate the characteristics of the LDV signals of these surfaces, in both the time and spectral domains. Preliminary results in engine classification using several machine learning algorithms point to the right direction on the choice of type of object surfaces to be planted for LDV measurements.

show abstract

A Wireless Accelerometer-Based Automatic Vehicle Classification Prototype System

Cited by 98 publications

References 6 publications

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

Intelligent Traffic Monitoring Systems for Vehicle Classification: A Survey

The Channel as a Traffic Sensor: Vehicle Detection and Classification Based on Radio Fingerprinting

Engine classification using vibrations measured by laser Doppler vibrometer on different surfaces

Contact Info

Product

Resources

About