A vehicle classification system based on microwave radar measurement of height profiles

Urazghildiiev, Ildar R.; Ragnarsson, Rolf; Wallin, K.; Rydberg, Anders; Ridderström, P.; Öjefors, Erik

doi:10.1109/radar.2002.1174736

Cited by 15 publications

(3 citation statements)

References 7 publications

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“…The vehicle classification can be based on the vehicle shape. Urazghildiiev, I. R. et al proposed a classification algorithm based on the full height and length profiles of vehicles [28]. If the precision of the estimation is sufficient, most vehicles can be classified, based on height H and length L , when relatively general classification schemes are introduced [18].…”

Section: B Height and Length Estimationmentioning

confidence: 99%

Single Variable-Constrained NDT Matching in Traffic Data Collection Using a Laser-Based Detector

Qiu¹,

Li²,

Zhang³

et al. 2021

IEEE Access

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As indispensable components of intelligent transportation systems, traffic detection and surveillance technologies deliver speed monitoring, traffic counting, and vehicle identification and classification. This paper proposes a normal distribution transform (NDT) algorithm to improve the speed accuracy and robustness of a laser-based detector. This method can deliver more accurate estimation of vehicle speed, enabling computation of the parameters of length and height. The results of simulation with different detector update rates suggest that the average estimation errors of vehicle parameters can be reduced using the NDT matching method, especially for the low detector update rate. The study also implemented a series of field experiments using the proposed detector prototype to verify the detector's measurements of vehicle parameters. The proposed method is a promising way in which to improve the laser-based traffic detector. In simulation test, initial experiments show that the accuracy of speed estimation can reach 95%, given the update rate of 1000 Hz for detector, the average length error can be reduced by approximately 60%. Even for speeding vehicles traveling at 150 km/h, the estimated speed error is limited to 10 km/h. In field test, for a vehicle at the speed of 80km/h, the estimation errors are within the threshold of the maximum errors of simulation, that is, 32 cm for length error and 5.71 km/h for speed error results.

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Section: B Height and Length Estimationmentioning

confidence: 99%

Single Variable-Constrained NDT Matching in Traffic Data Collection Using a Laser-Based Detector

Qiu¹,

Li²,

Zhang³

et al. 2021

IEEE Access

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“…Therefore, such types of classification systems are feasible for categorization into a relatively small number of vehicle classes such as small, medium, and large cars [19], [20] or tracked and wheeled vehicles [21]. To enhance classification accuracy and increase the number of vehicle types being classified, a vehicle classification system using down-looking spread-spectrum MW radar was proposed in [22]. In this system, the sensor was mounted above the roadway in such a way that vehicles pass directly below the sensor.…”

Section: Remote Sensing Technologies Used In Itsmentioning

confidence: 99%

Modeling and Implementation Of Wireless Embedded System For Intelligent Transport System Application

Bera

Dhar

Kandar

et al. 2008

2008 IEEE Region 10 and the Third International Conference on Industrial and Information Systems

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Intelligent Transport system (ITS) deals with the remote sensing as well as access to the internet and multimedia on the move. There is a need for a system that supports both remote sensing and communication aspects with simple and reconfigurable hardware.In the present work MATLAB/SIMULINK based modeling of such a system is done based on DSSS. Hardware realization of this model is achieved through the SignalWAVE software defined radio (SDR). From the simulation results two empirical formulae are developed for target detection.

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“…However, not all vehicles have this device. Additionally, a large network infrastructure or an infrastructure with long-term-evolution mobile coverage is required, as described in [9].…”

Section: Introductionmentioning

confidence: 99%

Autonomous Wireless Sensor System for Emergency Monitoring Roads with Low Communication Coverage

Postigo-Malaga,

Jimenez-Caceres,

Pelegri-Sebastia

et al. 2023

Electronics

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Rural areas often face communication challenges due to limited mobile coverage on remote roads, posing significant difficulties in reporting emergencies and accidents. This study presented an autonomous vehicle tracking system using low-cost radar sensors to detect possible emergencies in the sections of roads with low cell coverage. The radar sensor system could determine the number of vehicles that passed through the nodes and classify them based on the vehicle type. Each node within the system is equipped with ten radars, a processor unit, and a radio transmitter to communicate with the network in real-time, achieving a rapid response time of just 0.2 s. To ensure seamless connectivity, two distinct wireless communication networks are employed, one for the connection between the towers in the same node and the other for the connection between nodes and a center with cellular coverage. The results of this study can be useful in conveying emergency messages, as well as traffic management.

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A vehicle classification system based on microwave radar measurement of height profiles

Cited by 15 publications

References 7 publications

Single Variable-Constrained NDT Matching in Traffic Data Collection Using a Laser-Based Detector

Single Variable-Constrained NDT Matching in Traffic Data Collection Using a Laser-Based Detector

Modeling and Implementation Of Wireless Embedded System For Intelligent Transport System Application

Autonomous Wireless Sensor System for Emergency Monitoring Roads with Low Communication Coverage

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