Improved discrimination of microwave vehicle profiles

Roe, H. G.; Hobson, G.S.

doi:10.1109/mwsym.1992.188085

Cited by 12 publications

(10 citation statements)

References 1 publication

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“…We also require that F (ĥ) ≥ C, where C is a threshold determined according to (8) in terms of the minimum length of the surface. For many vehicles, the height of the cabin or other similar surfaces can be used as a measure of the vehicle's height.…”

Section: B Feature Vector Extractionmentioning

confidence: 99%

“…In various traffic management applications, roadsidemounted and forward-looking frequency-modulated continuous wave (FMCW) and noise-correlation radar units combined with continuous-wave (CW) Doppler sensors are commonly used [3], [8], [9]. Both of these MW radar sensors are primarily intended for extraction of vehicle length and shape information.…”

Section: Introductionmentioning

confidence: 99%

“…However, high resolution in the distance domain is required to obtain accurate vehicle shape information from a roadsidemounted sensor. As a result, the FMCW radar classification system considered in [8] gives only 75% accuracy when separating traffic into five categories.…”

Section: Introductionmentioning

confidence: 99%

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Vehicle Classification Based on the Radar Measurement of Height Profiles

Urazghildiiev

Ragnarsson²,

Ridderström³

et al. 2007

IEEE Trans. Intell. Transport. Syst.

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Abstract-The problem of classifying road vehicles according to vehicle type is considered. The proposed solution is based on using vehicle height and length and height profiles obtained by a microwave (MW) radar sensor. We show that if the radar sensor satisfies certain requirements, then a precise feature vector can be extracted, and simple deterministic algorithms can be applied to determine the vehicle class. Field trials using a spread-spectrum MW radar sensor system operating on these principles have been carried out. They confirm that accurate classification of a large number of vehicle classes can be reached.

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Section: B Feature Vector Extractionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Vehicle Classification Based on the Radar Measurement of Height Profiles

Urazghildiiev

Ragnarsson²,

Ridderström³

et al. 2007

IEEE Trans. Intell. Transport. Syst.

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“…This input includes manually tuning a radio to a specific frequency, operating a cellular phone, and adjusting an outside rear-view mirror. In-vehicle most glances require more than 1.2 seconds as shown in the Table 3-1 [67,56]. This relatively long glance-duration time is one of the key factors contributing to driver loss of situation awareness to the forward vehicle.…”

Section: Driver Attentionmentioning

confidence: 99%

“…These sensors fulfill the tasks of headway control and obstacle detection, which are the basis of CAS Sensing techniques can be classified in three main groups, according to Stobart and Upton, [67].…”

Section: External Sensorsmentioning

confidence: 99%

Development of rear-end collision avoidance in automobiles

Dravidam¹

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intensity to the following driver. The RECAS is designed the using the LabVIEW software. The simulation is designed to meet several criteria: System warnings should result in a minimum load on driver attention, and the system should also perform well in a variety of driving conditions.In order to illustrate and test the proposed RECAS methods, a Java program has been developed. This simulation animates a multi-car, multi-lane highway environment where car speeds are assigned randomly, and the proposed RECAS approaches demonstrate rear-end collision avoidance successfully. The Java simulation is an applet, which is easily accessible through the World Wide Web and also can be tested for different angles of the sensor.vi

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