A Low-cost Vehicle Detection and Classification System based on Unmodulated Continuous-wave Radar

Fang, Jianxin; Meng, Huadong; Zhang, Hao; Wang, Xiqin

doi:10.1109/itsc.2007.4357739

Cited by 52 publications

(22 citation statements)

References 10 publications

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“…It gains importance because devices following this principle can be low footprint and passive in nature. There are many proposals of vehicle detection using active systems [3], [4], [5]. Main principle of these systems are to transmit a signal and analyze corresponding reflected signal from the vehicle.…”

Section: Introductionmentioning

confidence: 99%

Vehicle detection and classification using LTE‐CommSense

Sardar

Mishra²,

Khan

2019

IET Radar, Sonar & Navigation

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We demonstrated a vehicle detection and classification method based on Long Term Evolution (LTE) communication infrastructure based environment sensing instrument, termed as LTE-CommSense by the authors. This technology is a novel passive sensing system which focuses on the reference signals embedded in the sub-frames of LTE resource grid. It compares the received signal with the expected reference signal, extracts the evaluated channel state information (CSI) and analyzes it to estimate the change in the environment. For vehicle detection and subsequent classification, our setup is similar to a passive radar in forward scattering radar (FSR) mode. Instead of performing the radio frequency (RF) signals directly, we take advantage of the processing that happens in a LTE receiver user equipment (UE). We tap into the channel estimation and equalization block and extract the CSI value. CSI value reflects the property of the communication channel between communication base station (eNodeB) and UE. We use CSI values for with and vehicle and without vehicle case in outdoor open road environment. Being a receiver only system, there is no need for any transmission and related regulations. Therefore, this system is low cost, power efficient and difficult to detect. Also, most of its processing will be done by the existing LTE communication receiver (UE). In this paper, we establish our claim by analyzing field-collected data. Live LTE downlink (DL) signal is captured using modeled LTE UE using software defined radio (SDR). The detection analysis and classification performance shows promising results and ascertains that, LTE-CommSense is capable of detection and classification of different types of vehicles in outdoor road environment.

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Section: Introductionmentioning

confidence: 99%

Vehicle detection and classification using LTE‐CommSense

Sardar

Mishra²,

Khan

2019

IET Radar, Sonar & Navigation

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“…We particularly focus on RADAR sensors here, even though the first part of our analysis applies to any generic type of sensor, that is specified only by the form of its field of view (FoV). RADAR sensors were shown to be feasible instruments for vehicle detection [9]- [11], however, to the best of our knowledge, the possibility of a seamless road surveillance by a network of RADAR sensors has not been studied so far.…”

Section: Introductionmentioning

confidence: 99%

Designing a Roadside Sensor Infrastructure to Support Automated Driving

Geißler

Kohnert

Stollé

2018

2018 21st International Conference on Intelligent Transportation Systems (ITSC)

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Automation of complex traffic scenarios is expected to rely on input from a roadside infrastructure to complement the vehicles' environment perception. We here explore design requirements for a prototypical setup of virtual vision or RADAR sensors along one roadside. Explicitly, we analyze the road coverage and the probability of vehicle occlusions, with the objective of evaluating the completeness of information that is captured by the sensor field. Simulation case studies are performed based on real traffic data acquired at the German Autobahn 9 near Munich. Our findings indicate how the sensor network should be designed in terms of sensor range, orientation and opening angle, in order to enable effective traffic detection. The achieved degree of completeness suggests that such a setup could be used to support automated vehicles to a substantial extent.

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“…In order to improve the presence detection accuracy while keeping low power consumption, a second high accuracy detection method such as infrared or light sensor is expected to work with magnetometer operating at low duty cycle. However, the application of an optical sensor is limited by complicated component design, it could also be influenced easily by snow, leaves and dust [12,13,14]. This paper presented a dual microwave Doppler radar based vehicle detection sensor to detect the movement of a vehicle, and proposed a motion recognition algorithm to identify whether the parking space was occupied or not.…”

Section: Introductionmentioning

confidence: 99%

A novel dual microwave Doppler radar based vehicle detection sensor for parking lot occupancy detection

Bao

Zhan

Chang

et al. 2017

IEICE Electron. Express

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This paper described a novel vehicle detection sensor design in which dual microwave Doppler radar transceiver modules were used to detect the movement of a parking vehicle. A motion recognition algorithm was also presented to identify the vehicle behavior and generate the parking space occupancy status. Comparing with existing methods such as magnetometer and optical based detection, the proposed design simplified engineering integration from complex optical system design as well as achieved a high detection accuracy. Experimental results showed that the proposed dual microwave Doppler radar sensor detected the vehicle movement clearly and the parking space occupancy detection accuracy was higher than 98%.

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A Low-cost Vehicle Detection and Classification System based on Unmodulated Continuous-wave Radar

Cited by 52 publications

References 10 publications

Vehicle detection and classification using LTE‐CommSense

Vehicle detection and classification using LTE‐CommSense

Designing a Roadside Sensor Infrastructure to Support Automated Driving

A novel dual microwave Doppler radar based vehicle detection sensor for parking lot occupancy detection

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