EP4134933A1 - System und verfahren zur erkennung der klasse eines vorbeifahrenden fahrzeugs - Google Patents

System und verfahren zur erkennung der klasse eines vorbeifahrenden fahrzeugs Download PDF

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Publication number
EP4134933A1
EP4134933A1 EP22189229.2A EP22189229A EP4134933A1 EP 4134933 A1 EP4134933 A1 EP 4134933A1 EP 22189229 A EP22189229 A EP 22189229A EP 4134933 A1 EP4134933 A1 EP 4134933A1
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EP
European Patent Office
Prior art keywords
vehicle
class
license plate
image
data processing
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EP22189229.2A
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English (en)
French (fr)
Inventor
Emanuele FABBRIZI
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Movyon SpA
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Movyon SpA
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    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/017—Detecting movement of traffic to be counted or controlled identifying vehicles
    • G08G1/0175—Detecting movement of traffic to be counted or controlled identifying vehicles by photographing vehicles, e.g. when violating traffic rules
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/015—Detecting movement of traffic to be counted or controlled with provision for distinguishing between two or more types of vehicles, e.g. between motor-cars and cycles
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors

Definitions

  • the present invention relates in general to the field of methods and systems for controlling traffic.
  • the present invention relates to a system and a method for detecting the class of a vehicle in transit.
  • vehicles are classified depending on their type, their use and their weight, power and maximum speed.
  • UNECE classification which provides for the subdivision of the vehicles into different classes, is applied to vehicles.
  • the vehicles which belong to each class are subject to different limits and obligations as regards type approval and road driving performance.
  • Systems which are able to automatically detect the class to which a vehicle in transit belongs are known. These systems are positioned for example at the access gates of toll road sections or at the access gates of road sections which are subject to traffic restrictions or at stations for checking the speed of vehicles in transit. Depending on the class detected for each vehicle in transit, it is thus possible to determine the toll which is due, the access right or prohibition, or the applicable speed limit.
  • Different types of systems for detecting the class of vehicles in transit are known. These systems in general make use of radar or laser sensors or magnetic loops which are installed in the road surface. These sensors generally acquire information about the structure of the vehicle, namely the information regarding the shape of its profile and/or the distribution of its volumes and/or the number of its axles, etc. Based on this information the class to which the vehicle belongs is then determined.
  • EP 2 686 841 describes a system for the classification of vehicles which provides for acquiring rear images of the vehicles and determines firstly whether it consists of a small or large vehicle, based on the width of the vehicle in relation to the width of the lane. Then it determines a more accurate classification depending on the minimum height of the vehicle in relation to the ground (clearance). In the case of vehicles which cannot be classified using the above criteria, it is envisaged applying specific rules, such as the presence of a striped panel which indicates that the vehicle belongs to the class of trucks.
  • US 2013/050492 A1 describes a method for classifying vehicles, which involves acquiring a 3/4 image of the front side of the vehicle in which the license plate is visible.
  • the contour of the license plate is detected in the image and, by means of a comparison with an archive of standard license plate formats, a rule for correction of the perspective of the acquired image is determined.
  • the rule is then applied to a portion of the image in which, in addition to the license plate, other characteristic parts of the vehicle (for example the front headlights and the front grille) are visible.
  • the vehicle class is determined based on the comparison with an archive of reference images of vehicles belonging to different classes, applying a similarity criterion.
  • the known systems which use radars, lasers or magnetic loops often are unable to perform reliable detection of the class of the vehicle, since the structural features of the vehicles which can be detected by these sensors often fall within two or more classes.
  • classification errors which may lead for example to incorrect calculation of the toll due or the applicable speed limit. This may happen, for example, in the case of commercial vans and trucks.
  • These types of vehicles belong to two different classes, but their structural features which may be detected by the sensors of the known systems (length, shape of the profile, number of axles) may be easily confused with each other. Errors in the classification of vehicles which belong to one of the two classes may therefore occur. These errors may in turn result in incorrect determination of the applicable highway speed limit which, in the case of commercial vans, is 130 km/h, while in the case of trucks it is 100 km/h.
  • An object of the present invention is to provide a system and a method for detecting the class of a vehicle in transit which are able to overcome the aforementioned drawbacks.
  • an object of the present invention is to provide a system and a method for detecting the class of a vehicle in transit are more reliable, namely which are subject to a significantly limited risk of classification errors.
  • the shape of the license plate and the syntax of the license plate vary depending on the type of vehicle. For example, in the case of Italian motorcycles and mopeds the license plate has a square shape, while the license plate number has a syntax of the type "AA12345” and "X12345”, respectively, and is arranged in two rows. In motor vehicles, instead, the license plate is rectangular, while the license plate number has a syntax of the type "AA 123AA” and is arranged in a single row.
  • the license plate In the case also of trailers (for motor cars, trailer trucks and semi-trailer trucks) the license plate is rectangular; in the case of trailers subsequent to 2001, the syntax of the license plate number is similar to that of vehicles, but the first letter is always "X", while for trailers prior to 2011 the syntax of the license plate number is of the type "AA123456". In the case of all-terrain vehicles, instead, the license plate has a square shape and the syntax of the license plate is similar to that of motor cars, but the first letter is always "Z”. And so on. Moreover, in the case where a trailer is present (namely in the case where the vehicle is a vehicle assembly composed of two units, i.e. tractor unit and the trailer), the vehicle comprises two license plates (one for each unit).
  • the Applicant has realized that, since there are significant differences between one class and another, the license plates of the vehicles may be used to provide a system which is able to detect in a particularly reliable manner the classes to which the vehicles in transit belong.
  • a system for detecting the class of a vehicle in transit comprising an image acquisition device suitable for acquiring one or more images of the vehicle, in which at least one portion of the surface of the vehicle provided with at least one license plate of the vehicle is visible.
  • the system also comprises a data processing unit which identifies in the acquired image the at least one license plate present and determines one or more properties thereof. On the basis of the properties of the license plate identified in the acquired image, the data processing unit of the system then determines the class to which the vehicle belongs.
  • license plate property will indicate its shape, the syntax of the license plate number shown on it or the number of vehicle license plates which are visible in the image.
  • the “license plate property” is therefore a property of the license plate per se, and not of the license plate in relation to other elements present in the images (such as its distance from the edge of the vehicle or the road surface).
  • the system for detecting the class of a vehicle in transit according to embodiments of the present invention is advantageously more reliable than the known systems.
  • the license plate which has a retroreflective background on which the alphanumeric characters which form the license plate number are written, is indeed visible and identifiable in the images with a particularly high degree of reliability (in particular in the case of infrared images).
  • the license plate properties which vary between one class and another (and which therefore allow one class to be distinguished from another class) are identifiable in the images with a particularly high degree of reliability. In this way, advantageously, the risk of incorrect classification of the vehicle is reduced significantly, especially in the case of vehicles which with the known detection systems fall within two or more classes.
  • the classification is based on properties of the license plate per se (and not of the license plate in relation to other elements present in the image, for example its distance from the bottom edge of the vehicle and/or the ground), calibration of the image acquisition device is not required.
  • the acquisition angle is moreover a less critical parameter, such that the image acquisition device may be positioned along the side of the carriageway so as to acquire 3/4 images of the vehicles, or on an arch or flyover above the carriageway, without this affecting the accuracy of determination of the license plate properties and therefore of the class to which the vehicle belongs.
  • the determination of the license plate properties according to the present invention does not require particularly precise correction of the perspective of the image acquired. More specifically, the determination of the syntax of the license plate number and/or of the number of license plates visible in the image may be performed with a certain degree of reliability also where the correction of the perspective of the acquired image is not particularly accurate or is not even possible.
  • the determination of the shape of the license plate according to the present invention consists in calculating the height to width ratio (or the reciprocal ratio), said calculation - differently from, for example, the exact determination of the positions of the corners of the license plate or its contour - being able to be performed in a reliable manner also where correction of the perspective of the acquired image is not particularly accurate or is not even possible.
  • a system for detecting the class of a vehicle in transit along a road section comprising:
  • the image acquisition device comprises an infrared image acquisition device and said at least one image of the vehicle comprises at least one infrared image of the vehicle.
  • said at least one image of the vehicle comprises at least one rear image of the vehicle.
  • said at least one rear image of the vehicle is acquired at an angle with respect to the direction of travel of the vehicle, in the horizontal plane, of between 0° and 40°.
  • said at least one property of said at least one license plate comprises at least one of the following: shape of said at least one license plate, syntax of a license plate number shown on said at least one license plate, and number of license plates visible in said at least one image.
  • determining the shape of the license plate comprises measuring the height and width of the license plate in the at least one image of the vehicle and calculating the ratio between height and width or the reciprocal ratio.
  • said data processing unit is also configured, if said at least one property of said at least one license plate does not uniquely indicate that the vehicle belongs to any class, to determine whether at least one further retroreflective element present on the surface of the vehicle is visible in said at least one image.
  • the at least one further retroreflective element is present on a rear surface of the vehicle.
  • said at least one further retroreflective element comprises one or more of the following: rectangular retroreflective panel for trucks, rectangular retroreflective panel for trucks or trailer trucks, retroreflective label indicating the applicable speed limit, retroreflective triangles for trailers, set of retroreflective strips defining the rear shape of the vehicle.
  • the data processing unit is configured to determine at least one property of said at least one further retroreflective element and, if said at least one property of said at least one further retroreflective element uniquely indicated that the vehicle belongs to a class, determining that the vehicle belongs to this class.
  • said at least one property of said at least one further retroreflective element comprises at least one of the following: absence/presence of said at least one further retroreflective element and, if present, shape of said at least one further retroreflective element, presence of a drawing, pattern or writing on said at least one further retroreflective element.
  • the image acquisition device and the data processing unit are comprised in a detection station located along said road section.
  • the data processing unit is incorporated in the image acquisition device.
  • the present invention provides a method for detecting the class of a vehicle in transit along a road section, the method comprising:
  • the system 100 preferably comprises at least one class detection station 1 located along a road section 2 along which the vehicle 10 is in transit.
  • the class detection station 1 may be situated at a gate (e.g. entry or exit gate or intermediate gate) of a toll road section, or at a gate (entry or exit gate) of a road section subject to traffic restrictions, or at a station for recording the instantaneous and/or average speed of passing vehicles, located along road section 2.
  • the system 100 may comprise a plurality of class detection stations similar to the station 1, distributed along the road section 2.
  • a class detection station may be provided at each entry gate and/or at each exit gate of the road section.
  • a single detection station 1 may be provided for all the lanes, or a class detection station similar to the station 1 may be provided for each lane or only for the lane reserved for the transit of the types of vehicles which are of interest for detection of their class.
  • the class detection station 1 also comprises at least one sensor 5 suitable for detecting information about the structure of the vehicle 10.
  • Said sensor 5 may for example comprise a radar sensor or laser sensor or a magnetic loop sensor suitable for acquiring information on the shape of the profile of the vehicle 10 and/or the number of vehicle axles.
  • the system 100 may also comprise a server 6 located in a position remote from the class detection station 1.
  • the class detection station 1 is connected to the server 6, for example via a communications network 7.
  • the connection between the class detection station 1 and the communications network 7 may be of any known type, for example a mobile, W-Fi or cable connection (Ethernet, etc.).
  • the image acquisition device 3 is configured, upon passing of the vehicle 10, to acquire one or more images of the vehicle 10 in which at least one portion of the surface of the vehicle 10 is visible, on which at least one retroreflective element, in particular at least one license plate of the vehicle, is present. If the image acquisition device 3 is an infrared image acquisition device, the one or more images of the vehicle 10 acquired are infrared images.
  • some types of vehicles such as buses, trucks, trailer trucks and semi-trailer trucks
  • These further retroreflective elements have different properties (namely shape, colors or writing), depending on the type of vehicle.
  • the license plate on the rear surface of buses, trucks, trailer trucks and semi-trailer trucks there are also present circular shaped retroreflective labels showing the applicable speed limit.
  • also rectangular retroreflective (and fluorescent) panels with yellow/red diagonal stripes are also fixed to the rear surface.
  • trailers of trailer trucks and semi-trailer trucks are also provided with rectangular panels which are retroreflective (and fluorescent), but yellow colored with a red edge, while the trailers are provided with retroreflective triangles.
  • goods transport vehicles are also provided with retroreflective strips which define the rear shape thereof. Any further retroreflective elements, if present, are also preferably visible in the at least one image acquired by the device 3.
  • the at least one image acquired by the device 3 is preferably therefore a rear image of the vehicle 10.
  • This rear image is preferably acquired at an angle with respect to the direction of travel of the vehicle 10, in the horizontal plane, which varies between 0° (front view of the rear surface of the vehicle 10) and 40°, more preferably between 0° and 20°.
  • the angle of acquisition with respect to the direction of travel of the vehicle 10 in the vertical plane preferably is comprised between 10° and 40°.
  • the device 3 is preferably positioned above the lane along which the vehicle 10 is passing.
  • the class detection station 1 may also comprise a transit sensor (not shown in Figure 1 ) which is able to detect passing of the vehicle 10 and, in response to this detection, to send to the device 3 an activation signal, in response to which the device 3 acquires one or more rear images of the vehicle 10.
  • a transit sensor not shown in Figure 1
  • the device 3 acquires one or more rear images of the vehicle 10.
  • Figure 2 is a schematic representation of an examplary infrared rear image acquired by the device 3, assuming by way of a non-limiting example that the vehicle 10 is a trailer truck or a semi-trailer truck.
  • the image 11 shows, on a black background, a plurality of retroreflective elements fixed onto the rear surface of the vehicle 10.
  • Said plurality of retroreflective elements comprises a single license plate 20 in which the license plate number "XA 123AA" is visible, and further retroreflective elements in addition to the license plate, namely: two retroreflective circular labels 21 which show two speed limits applicable to the vehicle 10 (70 km/h and 80 km/h, which are applicable on freeways and highways, respectively), two retroreflective triangles 22, a rectangular retroreflective panel 23 without diagonal stripes, and four retroreflective strips 24 which define the rear shape of the vehicle 20.
  • the retroreflective elements present in the image would be different and would have properties different from those shown in Figure 2 .
  • the only retroreflective element present would be the license plate and the license plate number would not start with the letter "X".
  • the license plate would have a different shape (square, not rectangular) and the license plate number would be arranged in two rows.
  • the only further retroreflective elements in addition to the license plate would be the labels which indicate the applicable speed limits (which limits moreover would be different from those indicated by the labels 21).
  • the license plate number would not start with the letter "X" and the rectangular retroreflective panel 23 would have diagonal stripes.
  • the license plate (or license plates) and any further retroreflective elements present on the surface (in particular on the rear surface) of the vehicle 10 and visible in the at least one image acquired by the device 3 are advantageously used by the system 100 to the detect the class to which the vehicle 10 belongs.
  • the data processing unit 4 preferably receives from the device 3 at least one infrared image of the vehicle 10, in which the license plate (or license plates) of the vehicle 10 and any further additional retroreflective elements (labels, panels, etc., depending on the type of vehicle, as described above) are visible.
  • the data processing unit 4 may receive from the device 3 also other information relating to the vehicle 10, such as its position on the carriageway (namely the lane along which the vehicle 10 is travelling, in the case of several lanes).
  • the data processing unit 4 may receive from the device 3 also the license plate number of the vehicle 10.
  • the device 3 is preferably provided with its own processor able to apply to the at least one infrared image acquired a processing algorithm able to identify the license plate number in the image and convert it into a sequence of alphanumeric characters (OCR algorithm).
  • OCR algorithm alphanumeric characters
  • the reading of the license plate number from the at least one infrared image acquired may be performed by the data processing unit 4.
  • the data processing unit 4 also receives from the sensor 5 information about the structure of the vehicle 10, for example about the shape of its profile and/or the number of its axles (step 301).
  • the data processing unit 4 may receive from the sensor 5 also other information about the vehicle 10, for example its speed at the moment when it passes by the class detection station 1.
  • the data processing unit 4 firstly applies to the information received from the sensor 5 a classification algorithm for performing a first determination of the class to which the vehicle 10 belongs (step 302).
  • the classification algorithm applied at step 302 may perform determination of the class based on the length of the vehicle 10; if instead the sensor 5 comprises magnetic loops, the algorithm determines the class based on the number and the position of the axles of the vehicle 10; if instead the sensor 5 comprises a laser, the algorithm determines the class of the vehicle based on the shape of its profile (for example its side, top or side/top profile).
  • the output of the algorithm applied at step 302 is a class CL* (step 303) chosen from a predefined number of classes.
  • the predefined set of classes may be that shown in the table hereinbelow.
  • the data processing unit 4 preferably performs a check of the class CL* determined at step 302, based on the properties of the retroreflective elements contained in the at least one image acquired by the device 3.
  • the data processing unit 4 preferably first of all determines in the at least one infrared image of the vehicle 10 acquired by the device 3 at least one property of the at least one license plate of the vehicle 10 (step 304).
  • Said at least one property in particular comprises at least one of the following: the shape of the license plate, the syntax of the license plate number shown on it, and the number of license plates of the vehicle 10 which are visible in the image.
  • the recognition of these properties of the license plate is performed by applying image processing algorithms which are known per se.
  • the data processing unit 4 preferably concludes that the vehicle 10 belongs to class CL**, regardless of the class CL* determined at steps 302-303 (step 306).
  • the class CL** may therefore be the same as the class CL*, or different.
  • the license plate properties may be considered by the data processing unit 4 in a certain hierarchical order.
  • the data processing unit 4 may initially consider the shape of the license plate (step 400). Specifically, at step 400 the data processing unit 4 determines whether the shape of the license plate is square or rectangular. For this purpose, the data processing unit 4 preferably measures the height and the width of the license plate in the acquired image and calculates the height to width ratio (or the reciprocal width to height ratio). A ratio close to 1 indicates a square shape, while a ratio different from 1 indicates a rectangular shape. The calculation of the ratio may be carried out on the height and width measured directly in pixels in the acquired image, without any rule for correction of the perspective being applied to the image.
  • the data processing unit 4 determines that the shape of the license plate is square, it is not possible to determine uniquely the class of the vehicle 10 on the basis of the shape alone of the license plate, because the square shape of the license plate is common both to motorcycles and mopeds (class G) and to some vehicle in class A (in particular all-terrain vehicles).
  • the license plate is rectangular
  • the data processing unit 4 may therefore for example then proceed to consider the syntax of the license plate number (step 402).
  • a syntax of the type "XA 123AA” or “AA123456” allows the identification of trailers, but does not distinguish between class B (light vehicles with trailer) and class D (heavy vehicles with trailer).
  • a syntax of the type "AA 123AA” is instead common to vehicles of class A, C and E and therefore in this case also it is not possible to determine uniquely the class on the basis of the syntax alone of the license plate number.
  • the data processing unit 4 may for example consider the number of license plates (step 403).
  • the data processing unit 4 concludes that the vehicle 10 belongs to this class, even if the class CL* determined by the classification algorithm is different.
  • the data processing unit 4 preferably determines the properties of the further retroreflective elements which may be present in the at least one acquired image (step 307). These properties comprise firstly the presence or the absence of these elements and, in the case where they are present, their shape and any drawings or patterns or writing on them.
  • any further retroreflective element (or of any further retroreflective elements) determined in step 307 are uniquely compatible with a class CL** chosen from the predefined number of classes indicated above (step 308)
  • the data processing unit 4 preferably concludes that the vehicle 10 belongs to class CL**, regardless of the class CL* determined at steps 302-303 (step 306). Any classes not compatible with the license plate properties determined at step 304 are preferably excluded from this choice.
  • the class CL** may therefore be the same as the class CL*, or different.
  • the data processing unit 4 preferably then considers which type of element is involved (step 406).
  • the data processing unit 4 may determine that the vehicle 10 belongs to class B or D if retroreflective triangles for trailers are present in the acquired image. This conclusion may be combined with that based on the syntax of the license plate (step 402) so that - for example - it is possible to determine the presence of a trailer (and therefore that the vehicle 10 belongs to class B or D) even in the case where the syntax of the license plate number is not the expected syntax "XA 123AA" or "AA123456".
  • the data processing unit 4 preferably confirms that the vehicle 10 belongs to class CL* determined at steps 302-303.
  • the data processing unit 4 could determine that, in addition to the license plate, a rectangular retroreflective panel for trucks (i.e. with slanting stripes) is present. This may happen for example in the case where the vehicle 10 is a truck with a non-standard license plate (namely with a license plate which starts with the letter "X", despite the fact that it is a syntax which is reserved for trailers), or in the case where it is a trailer truck which onto which a retroreflective panel for trucks has been mistakenly affixed.
  • the data processing unit 4 concludes that it is not possible to determine the class of the vehicle 10 on the basis of the properties of the license plate and the further retroreflective elements visible in the at least one infrared image acquired.
  • the data processing unit 4 therefore considers valid the result determined by the classification algorithm at steps 302-303.
  • the class CL* determined by the classification algorithm is confirmed.
  • the data processing unit 4 may also use other information in order to determine the class of the vehicle 10 and/or to exclude the classes which are not possible.
  • Said information for example comprises the speed of the vehicle 10 received at step 301 and/or the lane in which the transit of the vehicle 10 was detected, received at step 300.
  • the data processing unit 4 determines that the speed of the vehicle 10 detected is greater than a certain maximum threshold plausible for heavy vehicles (i.e. those weighing more than 3.5 t) and/or that the vehicle 10 is in transit along a lane which is prohibited for heavy vehicles (namely vehicles weighing more than 3.5 t), it preferably may exclude from the possible classes the heavy vehicle classes, i.e. classes C, D and E.
  • the plausible maximum threshold may be for example equal to the applicable speed limit increased by a certain percentage (for example, 70%). If, for example, the limit is 80 km/h, the plausible maximum threshold may be 136 km/h.
  • the search for the class CL** with which the properties of license plate and/or of any further retroreflective elements are uniquely compatible is thus limited to only the remaining classes.
  • the system 100 is advantageously more reliable than the known systems.
  • the license plate (or plates) and any further retroreflective elements are indeed visible and may be identified in the images of the vehicle 10 with a particularly high degree of reliability, especially if these images are infrared images.
  • the properties of the license plate and of any further retroreflective elements which vary between one class and another (and which therefore allow one class to be distinguished from another class) are identifiable in the images of the vehicle 10 with a particularly high degree of reliability.
  • the risk of an incorrect classification of the vehicle 10 is significantly reduced, especially in the case where determination of the class CL* of the vehicle 10 by means of the classification algorithm applied at the step 302 borders on two or more classes.
  • the vehicle 10 is a commercial van
  • a known system could mistakenly classify it in class C associated with trucks, and not in class A to which it actually belongs, since a commercial van and a truck have similar structural features.
  • the system 100 according to the present invention instead provides the correct classification since, owing to the absence of rear panels and the absence of circular labels which show a speed limit of 80 or 100 km/h, it is possible to determine in a unique manner that it is a vehicle belonging to class A.
  • the classification is based on properties of the license plate (and any further retroreflective elements) per se, and not on the properties of these retroreflective elements in relation to other elements present in the image (for example the distance of the license plate from the bottom edge of the vehicle and/or the ground), calibration of the image acquisition device 3 is not required.
  • the acquisition angle is moreover a less critical parameter, such that the image acquisition device 3 may be positioned along the side of the carriageway so as to acquire 3/4 images of the vehicle 10, or on an arch or flyover above the carriageway, without this affecting the accuracy of determination of the license plate properties and any further retroreflective elements and therefore the class to which the vehicle 10 belongs.
  • the determination of the syntax of the license plate number and/or of the number of license plates visible in the image may be performed by the data processing unit 4 with a certain degree of reliability also where the correction of the perspective of the image acquired by the image acquisition device 3 is not particularly accurate or is not even possible.
  • the determination of the shape of the license plate consists in calculating the height to width ratio (or the reciprocal ratio) (step 400), said calculation - differently from, for example, the exact determination of the positions of the corners of the license plate or its contour - being able to be performed in a reliable manner by the data processing unit 4 also where correction of the perspective of the acquired image is not particularly accurate or is not even possible.
  • the determination of the class is performed by the data processing unit 4 included in the class detection station 1, this is not limiting.
  • the data processing unit which performs the class determination may be situated in the server 6.
  • the at least one infrared image detected by the device 3 and, where appropriate, the information acquired by the sensors 5 is sent by means of the network 7 to the server 6.
  • the determination of the class may be carried out by only one of the stations which the vehicle 10 has passed by.
  • the presence of the sensor 5 is optional, as is likewise optional the execution of steps 301-302-303 of the algorithm shown in Figure 3 .
  • the determination of the class to which the vehicle 10 belongs is based exclusively on the properties of the license plate and of any further retroreflective elements visible in the at least one acquired image of the vehicle 10.
  • the class detection may be carried out for all the vehicles in transit along the road section 2, are only for some of them.
  • the detection of the class may be excluded for vehicles which are clearly not committing a violation (for example, vehicles which do not exceed the speed limit permitted for the category of vehicles with a lower limit) or only for those vehicles travelling in a certain lane.
  • the detection of the class on the basis of the properties of the license plate and of any further retroreflective elements may be performed only in the case where the class CL* determined by the classification algorithm is of a certain type.
  • the detection of the class based on the properties of the license plates and of any further retroreflective elements could be carried out only if the class CL* is a class such that the classification algorithm is not particularly reliable (for example classes A and C which, as described above, in the case of commercial vehicles, are easily confused with each other).
  • the data processing unit 4 may also read the speed limit shown on them.
  • the class detection system 100 may be used to assist a system for detecting speed limit violations. This is particularly advantageous especially in the case where the speed limit is different for the different types of vehicles. For example, along an Italian highway with a general speed limit of 130 km/h, motor cars and motorcycles may travel at a speed of up to 130 km/h, while trucks with a gross vehicle weight of more than 12 t may only travel at a speed of up to 80 km/h, while buses with a gross vehicle weight of more than 5 t may only travel at a speed of up to 100 km/h.
  • the automatic reading of the speed limit shown on the retroreflective labels by the data processing unit 4 is able to overcome these drawbacks and determine the correct applicable speed limit substantially for all the passing vehicles.
  • the labels which show the speed limit are in fact obligatory according to the highway code and therefore all legally compliant vehicles travelling on the roadway networks must have them (including foreign vehicles).
  • the automatic reading of the speed limits shown on the labels may therefore be carried on all the passing vehicles.
  • Access to external data banks is consequently carried out only by way of confirmation when a possible violation has been identified, thus significantly reducing the number of interrogation operations and increasing the performance of the system for detecting speed limit violations.

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EP22189229.2A 2021-08-11 2022-08-08 System und verfahren zur erkennung der klasse eines vorbeifahrenden fahrzeugs Pending EP4134933A1 (de)

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IT202100021770 2021-08-11

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EP4134933A1 true EP4134933A1 (de) 2023-02-15

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Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH08233525A (ja) * 1995-02-24 1996-09-13 Hitachi Ltd 車種判別装置
US20080143555A1 (en) * 2001-10-17 2008-06-19 Jim Allen System and Synchronization Process for Inductive Loops in a Multilane Environment
US20110103647A1 (en) * 2009-10-01 2011-05-05 Alexander Leopold Device and Method for Classifying Vehicles
WO2012074526A1 (en) * 2010-12-02 2012-06-07 3M Innovative Properties Company Methods and systems for enhancing read accuracy in automated license plate reader systems
US20130050492A1 (en) 2011-08-26 2013-02-28 Michael Lehning Method and Apparatus for Identifying Motor Vehicles for Monitoring Traffic
EP2686841A2 (de) 2011-03-14 2014-01-22 The Regents of the University of California Verfahren und system zur klassifikation von fahrzeugen
EP2820632A1 (de) * 2012-03-02 2015-01-07 Leddartech Inc. System und verfahren zur mehrzweck-verkehrserkennung und -charakterisierung

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JPH08233525A (ja) * 1995-02-24 1996-09-13 Hitachi Ltd 車種判別装置
US20080143555A1 (en) * 2001-10-17 2008-06-19 Jim Allen System and Synchronization Process for Inductive Loops in a Multilane Environment
US20110103647A1 (en) * 2009-10-01 2011-05-05 Alexander Leopold Device and Method for Classifying Vehicles
WO2012074526A1 (en) * 2010-12-02 2012-06-07 3M Innovative Properties Company Methods and systems for enhancing read accuracy in automated license plate reader systems
EP2686841A2 (de) 2011-03-14 2014-01-22 The Regents of the University of California Verfahren und system zur klassifikation von fahrzeugen
US20130050492A1 (en) 2011-08-26 2013-02-28 Michael Lehning Method and Apparatus for Identifying Motor Vehicles for Monitoring Traffic
EP2820632A1 (de) * 2012-03-02 2015-01-07 Leddartech Inc. System und verfahren zur mehrzweck-verkehrserkennung und -charakterisierung

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