EP1099203A2

EP1099203A2 - Method for detecting a vehicle traffic status and system for detecting said traffic status

Info

Publication number: EP1099203A2
Application number: EP99947231A
Authority: EP
Inventors: Armin Anders; Matthias Stein
Original assignee: Siemens AG
Current assignee: Siemens AG
Priority date: 1998-07-17
Filing date: 1999-07-16
Publication date: 2001-05-16
Anticipated expiration: 2019-07-16
Also published as: PT1099203E; ES2209510T3; WO2000004524A2; WO2000004524A3; DK1099203T3; DE59907035D1; EP1099203B1; AU6078099A; DE19981341D2; US6489920B1; JP2002520754A; ATE250262T1; JP3589983B2

Abstract

From a body located at a distance above the surface of the earth, an image is recorded of a region that is located underneath the body on or above the surface of the earth and that has a diameter of at least one kilometer. The recorded image is fully geocoded and comprises a grid dimension small enough that vehicle densities located in the region can be recognized. The recorded image is evaluated with respect to these vehicle densities and the spatial allocation thereof to the associated roadways. The method is used for acquisition of the state of street traffic over a large area.

Description

description

Method for detecting a traffic condition of vehicles and arrangement for detecting the traffic condition

The invention relates to a method for detecting a traffic condition of vehicles and a device for detecting such a traffic condition.

For the targeted use of traffic control systems, a sensible coordination of the switching phases of traffic signal systems as well as for the traffic-oriented determination of traffic route construction measures, the most comprehensive computer-aided simulation and forecasting of traffic flows is necessary. In order to coordinate the programs used with the real conditions, however, extensive knowledge of the actual traffic situation in the areas to be considered must be available. In urban conurbations in particular, it is not sufficient to record only individual traffic routes with regard to traffic flows, but rather a complete picture of the traffic situation including possible alternative routes, alternative routes, etc. is required.

The detection of the actual traffic condition, which is important for optimizing the flow of traffic, has hitherto been carried out using measuring devices on the infrastructure, for example in road traffic using measuring loops in the roadway or by very personnel-intensive traffic counts. However, these measures are very local and do not allow an overall view. Furthermore, depending on the correct or incorrect choice of the measurement location, they may be of little significance. In addition, measuring devices on the infrastructure are localized and involve significant costs in both installation and maintenance. For these reasons, these measurement methods are usually limited to a few places. The invention has for its object to provide a method for large-scale detection of a traffic condition of vehicles.

This object is achieved by the method specified in claim 1.

According to this solution, from a body located at a distance above the surface of the earth, an image of an area which is below the body on and / or above the surface of the earth and has a lateral diameter of at least one kilometer, with such a small grid dimension recorded that densities of at least one specific type of vehicle in the area can be recognized up to a predetermined maximum density, and the recorded image is evaluated with respect to at least one density of at least one type of vehicle.

An earth satellite orbiting the earth is preferably used as the body (claim 2). Because of its large distance from the earth's surface of the order of 100 km, such a satellite has the advantage that particularly large areas of, for example, 50 by 100 km area, in any case an area which has a diameter of the order of ten kilometers 3), can be monitored.

This advantageously enables traffic of any type and / or type of vehicle, including rail-bound land vehicles, for example any type of passenger and / or truck, rail-bound vehicles, for example any type of railroad train for passenger or freight transport, watercraft, for example any type of passenger and cargo ships both at sea and on inland waterways, as well as aircraft, for example all types of passenger and cargo aircraft, in previously unknown and impossible ways

Large areas can be monitored quickly and safely. In particular, vehicles can advantageously both separately their type and / or type and regardless of the type and / or type of vehicles are monitored in particular simultaneously.

With a single satellite orbiting the earth, individual images and temporal sequences of images from the same area can be taken every two to four days.

A geostationary earth satellite can also be used as the body, which advantageously enables constant monitoring of traffic in an area of almost the size of an entire hemisphere, for example shipping traffic in the Atlantic or Pacific.

Images from the large areas with a sufficiently high resolution can be optically generated from an earth satellite, but this type of recording is dependent on the time of day and weather. If, on the other hand, radar radiation is used to record the images, the images can advantageously be recorded at any time of day and in any weather. However, radar radiation and a radar system must be used that enable images with a sufficiently small grid size that corresponds to a sufficiently large resolution. The lower limit of the grid dimension, at least in relation to road traffic, is a dimension of 2 m, in order to be able to differentiate between lane positions. Here, densities of the road vehicles can be clearly identified and assigned since the vehicles have different degrees of reflection than the travel routes and there are corresponding brightness differences in the images taken.

Instead of a body in the form of a satellite, a body in the form of an aircraft can be used in the method according to the invention (claim 5), the aircraft being primarily an aircraft, but also, for example, a balloon and the like. For example, from the plane are images of areas five wide up to seven kilometers realizable, at least areas that have a diameter of the order of one kilometer (claim 6).

In order to be independent of the time of day and the weather in this case as well, it is again advisable not to take the images optically but using radar, advantageously SAR. Here too, at least in relation to road traffic, 2 m is regarded as the lower limit of the grid dimension.

In any case, it is therefore advantageous to record an image by means of radar radiation (claim 7).

If the images are recorded with the aid of interferometry (claim 8) and / or the Doppler effect (claim 9), it is advantageously possible to record the speeds of the vehicles in addition to the vehicle densities.

The method according to the invention is particularly advantageous for large-scale detection of a state of road traffic and monitoring and guiding road traffic in large cities, but also in smaller cities and / or rural areas, but not limited to this, but, as already mentioned, can in principle also be used for monitoring the movement of railway trains, ships and / or aircraft, especially in port and airport areas.

An advantage of the method according to the invention can be seen in its suitability for using the means of georeferencing, which allows a quick and precise assignment between a point of the area and the corresponding point on the image recorded by this area. In an advantageous embodiment of the method according to the invention, a local association between a density of vehicles recognized in an image of the area and a traffic route of the area is established by means of georeferencing (claim 10), in particular in the case of artificial earth satellites Allows the recorded images a local assignment of vehicle densities to the respective traffic routes.

Monitoring changes in the traffic situation can advantageously be achieved if, after taking an image of the area, at least one further image of the same area is taken and also evaluated with regard to vehicle densities in the area, and if at least two taken images are compared with one another ). As a result, e.g. With regard to road traffic, a direct optimization of the control algorithms of traffic control systems and traffic light phases can be realized by a comparison before and after the optimization measure. In addition, changes made by road construction measures can advantageously be checked and existing simulation programs can be precisely coordinated.

In this case, it is particularly advantageous if at least one sequence of two images (3) of the area (10) is produced by individual snapshots consecutive within one hour. (Claim 12). Such a sequence of images can advantageously be used to record the traffic state and its change in time in real time or at a later point in time, for example in relation to road traffic, for generating current traffic situations for traffic information, for direct control of traffic control systems and for coordination of traffic flow simulations, in addition to which a direct optimization of the control algorithms of traffic control systems and traffic light phases can be implemented by means of a before-after comparison. The evaluations of the recordings can be done manually or at short notice and with less personnel, if there is a system for recognizing the density of motor vehicles on the images and for locally assigning the vehicle densities to the respective traffic routes. The actual evaluation of the recordings can already take place on the body, for example on board the satellite or aircraft. An advantageous arrangement for recording a traffic condition suitable for this purpose has the features specified in claim 13, in which the body located at a distance above the surface of the earth is in particular an earth satellite orbiting the earth, a geostatial earth satellite or an aircraft .

According to an advantageous embodiment of the arrangement according to the invention specified in claim 14, the evaluation device converts a certain information content of a recorded image into coded data signals.

The evaluation device advantageously generates georeferenced coded data signals (claim 15), with the aid of which a reference to maps for traffic routes to be examined and thus a local association of vehicle densities with respective traffic routes is advantageously established.

Information about a traffic condition in the area concerned can be obtained from the coded data signals, preferably with a processing device for processing the data signals in order to obtain information about a traffic condition in the area (claim 16). The processing device is preferably and in particular stationary on the surface of the earth.

Information obtained about a traffic condition in the area is used for further use, preferably in the form of data that is only relevant for this use and preferably in a use device provided for this use (claim 17). For different types of use of a traffic condition, different use devices can be used, which are preferably and in particular stationary on the earth's surface. The invention is explained in more detail in the following description using the figures as an example. Show it:

1 shows a perspective view of a body located at a distance from the earth's surface, from which at least one image of an area of the earth's surface is taken,

FIG. 2 shows an image section from an image of the region of the earth's surface, which is photographed from an artificial satellite orbiting the earth,

FIG. 3 shows an image section from an image of the region of the earth's surface, which is recorded by means of radar radiation from an artificial satellite orbiting the earth,

FIG. 4 shows an image section from an image of the region of the earth's surface, which is photographed from a flying aircraft,

5 shows an image section from an image of the region of the earth's surface, which is recorded by means of radar from a flying aircraft, and

Figure 6 shows an exemplary arrangement for detecting a traffic condition.

The figures are schematic and not to scale.

According to FIG. 1, a body 2 is located at a distance a above the earth's surface 1, from which an image of an area 10 is recorded which is located below the body 2 on or in the air space above the earth's surface 1. The body 2 can be an earth satellite or an aircraft. U.N- ter Earth surface 1 is to be understood not only the surface of the mainland but also the water surface of the earth.

It is assumed that the body 2 is an artificial satellite orbiting the earth at a distance a of the order of 100 km, which is usual for such satellites.

From this satellite 2 an image of, for example, a strip-shaped area 10 with a length 1 of approximately 100 km and a width b of approximately 50 km is recorded. In Figure 1, the curvature of the earth's surface 1 is neglected.

The image is to be recorded with radiation 5, which ensures that a grid dimension in the image is so small that densities of at least one specific type located in area 10

Vehicles can be recognized up to a predetermined maximum density.

FIG. 2 shows an image section 11 'from an image 3 of the region 10 taken from the satellite 2, it being assumed that this image 3 of the region 10 is photographic, i. is generated with an optical radiation 5 and the image section 11 'corresponds to the relatively small section 11 of the area 10 in FIG. The optical radiation 5 can be ultraviolet, visible and / or infrared light.

In this photographically recorded image 3 of the area 10 and thus also in the image section 11 'there is a grid dimension which is determined by the wavelength of the optical radiation 5 used and the resolving power of an optical recording system. In this case, grid dimensions of far less than 0.5 m are possible, so that objects such as individual vehicles are depicted somewhat sharply outlined.

For example, area 10 is a part of the earth's surface 1 covered with a network of country roads and highways and a section 110 of area 10 leads to a freeway 110 which is used by vehicles. Other recognizable structures of the landscape in section 11 of area 10, such as trees and bushes, houses, other roads, rivers, bridges, etc., are shown in section 11 'of the figure 2 omitted for simplicity.

The freeway 110 consists, for example, of two lanes 112 and 113 separated by a green strip 111, each of which has, for example, two lanes 112ι_, 1122 and 113] _, 1132 separated by a dividing line II23 and II33.

The roadway 112 is intended for the direction of travel 114 from bottom to top, the roadway 113 for the direction of travel 115 from top to bottom.

Vehicles located on roadways 112 and 113 usually consist of passenger cars, buses and lorries with and without trailers. In FIG. 2, for example, there is a single truck or omnibus that is in the lane 113] _ and is designated by 4 ', all other vehicles on the freeway 110 are assumed to be passenger cars, each of which is already known by its own smaller length e visibly differs from the length e 'of the truck or bus. Some copies of passenger cars are designated as 4 for the rest. There are a total of thirteen passenger cars on the section of the highway 110 in the image section 11 '.

Assuming right-hand traffic, for example, on the right lane 113] _ the lane 113 behind the truck or bus 4 ', for example, three passenger cars 4 came close together, for example the truck or bus with 4' straight from faster-moving passenger cars 4 in the left lane 1132 is overtaken and the three passenger cars 4 behind the truck or bus must wait 4 'until the left lane 1132 is clear again.

The density of vehicles in a lane is determined by the distance d between vehicles following one another in the direction of travel (or also opposite to the direction of travel). The greater the distance d between successive vehicles, the smaller the density of the vehicles.

In the example according to FIG. 2, there is a maximum density of vehicles in the vehicle group 40 consisting of the truck or bus 4 'and the three passenger cars 4 closely behind it, since in this group 40 the distance d0 between the successive vehicles 4' and 4 is evidently minimal compared to the distances d that exist between the successive vehicles 4 not belonging to the group 40.

The absolute maximum density of vehicles in a lane is given when the vehicles collide without gaps, i.e. if d is zero. The absolute maximum density does not occur in road traffic, apart from singular cases, because the drivers always strive to maintain a minimum distance d greater than zero.

The grid dimension r generally determines a maximum density of the vehicles, above which densities of the vehicles, which are determined by distances 0 <d <r, do not differ from one another and therefore cannot be recognized because the

Vehicles can no longer be kept apart. On the other hand, vehicles with distances d> r can be kept apart and densities of these vehicles, which are distinguished from one another by distances d, which are determined by an integer multiple of the grid dimension r, and thus recognized. If the maximum density d = r of the vehicles is present, the grid r can be used to specify a lower limit for the number of vehicles contained in the continuously appearing queue of indistinguishable vehicles.

With regard to FIG. 2, it is assumed, for example, that the photographic recording optics used have such a high resolving power that the grid dimension r is approximately 0.1 m and thus the predetermined maximum density of the vehicles is essentially equivalent to the absolute maximum density, because in relation to the size of vehicles 0.1 m is negligible.

With regard to FIG. 3, it is assumed that the image section 11 'does not come from a photographically recorded image of the area 10, but from an image 3 of the area 10 recorded by means of radar radiation 5.

The image section 11 'according to FIG. 3 shows, like the image section 11' according to FIG. 2, only the freeway 110 and the vehicles located thereon, but for the sake of simplicity no further details of the landscape.

It is further assumed that the image 3 taken with the radar radiation 5 was taken at the same time as the photographic image 3 from the satellite 2, so that in the image detail 11 'according to FIG. 3 the vehicles 4' and 4 are in the same traffic condition as are located on the highway 110 in the image section 11 'according to FIG.

The image recorded with the radar radiation 5 and thus the image section 11 'according to FIG. 3 have an incomparably larger grid dimension r> 0.5 m in comparison to the photographic image section 11' according to FIG. 2 and thus an incomparably weaker geometric resolution. The grid dimension r is indicated in FIG. 3. Due to this comparatively coarse grid dimension r, in contrast to the image detail 11 'according to FIG. 2, the roadways 112 and 113 as well as the vehicles 4' and 4 on the roadways 112 and 113 each appear blurred in the image detail 11 'according to FIG. The dividing lines II23 and II33 are also no longer visible. The primary cause of the coarse grid dimension r lies in the wavelength of the radar radiation 5 which is larger than the optical wavelength.

In addition, each vehicle on a carriageway 112 and / or 113 appears as a diffuse spot which advantageously stands out clearly against the background given by this carriageway. The reason for this lies in the favorable circumstance that a roadway or generally the ground has a significantly different reflectivity for the radar radiation 5 than a vehicle thereon.

Objects and distances that are smaller than the grid dimension r can no longer be perceived in the image recorded by the radar radiation 5 and thus in the image section 11 '.

In the method described here, a grid dimension r, which is essentially equal to 2 meters, is advantageously sufficient.

With this grid dimension r = 2 m, small to medium to strong densities of vehicles which correspond to moderate, medium and heavy traffic can be recognized and distinguished from one another in the image 3 of area 10 in that the vehicles and distances between the successive vehicles are essentially recognizable individually and on average these distances differ significantly from each other in moderate, medium and heavy traffic.

On the other hand, with this grid dimension r = 2 m viscous traffic or traffic jam can be recognized in that the Most of the vehicles in Figure 3 are no longer separated from each other, but rather can essentially be recognized as a continuous queue, because the distances between successive vehicles are close to 2 m. In particular, such a queue with a length of one or more kilometers certainly indicates a traffic jam if, when comparing two or more images 3, which were taken at different times, there is no movement of at least one end and viscous traffic, if such a comparison is Movement of the snake shows.

In FIGS. 2 and 3 it is assumed by way of example and somewhat unrealistically that the distance d0 between the successive four vehicles 4 'and 4 of the vehicle group 40 is in each case close to or equal to 2 m. Accordingly, this group 40 appears like a continuous line of vehicles.

The lengths of passenger cars differ from each other by significantly less than 2 m and can be recognized as such at a distance d of more than 2 m in the method described here. The lengths of trucks and buses of the same weight class also differ from each other by significantly less than 2 m, but in many cases by more than 2 m from passenger cars. In these cases, in the method described here, trucks and buses of the same weight class can be recognized as such at least in the case of fluid traffic and at a distance d of more than 2 m and can be distinguished from passenger cars. Different types and / or types of vehicles can thus be kept apart and their densities can also be determined individually with the radar radiation, which generates a grid dimension r of 2 m.

With reference to FIGS. 4 and 5, it is assumed that the body 2 according to FIG. 1 is an aircraft flying at a distance a of 8 to 10 km from the earth's surface 1, from which an image of, for example, a strip-shaped region 10 of the earth's surface 1 is recorded, the region 10 having a length 1 of approximately 9 km and a width b of approximately 5 to 7 km.

FIG. 4 shows an image section 11 'from the picture 3 of the area 10 taken from the aircraft 2, it being assumed that this picture 3 is produced photographically and the picture section 11' of the relatively small section 11 of the area 10 in FIG. 1 corresponds.

For example, area 10 is now the road traffic network of a city, of which section 11 of area 10 shows a road junction 120 that is used by vehicles. Other recognizable structures of the urban landscape in section 11 of area 10, such as trees and bushes, houses, further streets, rivers, bridges, etc., have been left out of picture section 11 'according to FIG. 4 for the sake of simplicity.

In the crossing 120, for example, two roads intersect 121 and 122. Each street 121 and 122 has for example two by a dividing line 1223 I2I3 or separate roadways 121χ, 1212, 122] _, 1222 ^au f

In the road 121, the roadway 121χ is provided for a direction of travel 12I4 and the roadway 1212 for the direction of travel I2I5 opposite to a direction of travel I2I4. In the road 122, the lane 122] _ for a driving direction of the road 1224 and 1222 f provided ^ÜR the ^2UR a traveling direction 1224 opposite the direction of travel 1225th

At the intersection 120 there is a traffic light system, not shown, which at the time of the image acquisition was switched, for example, so that the street 122 is red and, for example, consisted of 4 cars without exception on both lanes 122ι_ and 1222 of this street 122 in front of the Intersection 120 must wait, while the vehicles, which also consist, for example, of passenger cars 4, have green on both lanes 121ι_ and 1212 of road 121 and are allowed to cross the intersection 120.

~ Ne group 42, for example, five passenger car 4 from a plurality of - Accordingly, for example, four cars 4 and on the roadway 1222 ^e ^ jams on the road 122 before the crossing 120 on the roadway 122_ a group 41 of a plurality.

With regard to FIG. 5, it is assumed that the image section ₁₁ does not come from a photographically recorded image 3 of the area 10, but from an image 3 of the area 10 recorded by means of radar radiation 5.

The image section 11 'according to FIG. 5 shows, like the image section ii _' according to FIG. 4, only the intersection 120 with the streets 121 and 122 and the vehicles located thereon, but for the sake of simplicity no further details of the urban landscape.

The image 3 taken with the radar radiation 5 and thus the image section 11 'according to FIG. 5 have, compared to the photographically taken picture 3 and thus the image section 11' according to FIG on.

It is also assumed here that the image 3 taken with the radar radiation 5 was taken at the same time as the photographic image 3 from the aircraft 2, so that in the image detail 11 'according to FIG. 5 the vehicles 4 are in the same traffic condition as in the image detail 4 on the intersecting streets 121 and 122. Because of this comparatively coarse grid dimension r, in contrast to the image section 11 'according to FIG. 4, the streets 121 and 122 and the vehicles 4 on the streets 121 and 122 also appear unsharp in the picture section 11' according to FIG. The dividing lines I2I3 and 1223 are also no longer visible.

In this case too, each vehicle 4 appears on roads 121 and 122 as a diffuse spot which advantageously stands out clearly against the background given by these roads. The reason for this again lies in the favorable circumstance that a roadway or generally the ground has a significantly different reflectivity for the radar radiation 5 than a vehicle thereon.

It is assumed that in each group 41 and 42 of vehicles 4 on the road 122, every distance d0 between successive vehicles 4 is smaller than the grid dimension r, while on each road 121] _ and 1212 and the road 121, every distance d between successive Vehicles 4 is larger than the grid dimension r. Accordingly, each of these groups 41 and 42 appears like a continuous line of vehicles, while the vehicles 4 on the road 121 can be recognized individually.

The streets 121 and 122 according to FIGS. 4 and 5 are each a street with oncoming traffic, ie a street with a lane which is intended for one direction of travel and a lane which is intended for the opposite direction of travel, these two lanes only through a dividing line are separated from one another or at least run next to each other at a very short distance. With such a road, it is important to be able to assign the vehicles to the individual lanes and thus directions of travel on an image 3 of area 10. This applies in particular to viscous traffic or traffic jams. In this case, it is particularly important to have a queue of vehicles indicating such a traffic condition to assign image 3 to the correct direction of travel, because it would be fatal to report traffic jams, for example, to traffic in the wrong direction. A grid dimension r of 2 m is advantageously sufficient for a clear and reliable assignment of the vehicles to the correct lane and thus the correct direction of travel.

Furthermore, with this grid dimension r = 2 m, it is advantageously also possible to recognize a parking situation on streets and squares, particularly in cities, i.e. determine the extent to which streets and squares are occupied by parked vehicles.

In addition to the advantages set out above, the relatively coarse grid dimension r of 2 m has the advantage that it can be easily realized with the advantageous radar radiation 5. However, the invention is not limited to this coarse grid size, but smaller but also larger grid dimensions can be used, depending on which one is currently favorable according to the circumstances of the individual case. For example, a smaller grid size should be used if it is important to recognize details that are smaller than 2 m.

Regardless of whether an image of an area 10 is recorded with the radar radiation 5 from a satellite 2 or an aircraft 2, it is expedient to take at least one further image of the area 10 after such an image of the area 10 and also to evaluate the at least one density of the at least one specific type of vehicle 4 located in the area 10 and to compare at least two images recorded in this way. A sequence of at least two images of the region 10 is preferably produced by chronologically successive individual snapshots.

In the arrangement for detecting a traffic condition shown in FIG. 6, there is an image recording device 20 present, which is attached to a body 2 located at a distance a above the earth's surface 1. This image recording device 20 is used to record an image 3 of an area 10 which is located below the body 2 on and / or above the earth's surface 1 and has a lateral diameter of at least one kilometer.

The image is recorded with a pitch r that is so small that densities of at least one specific type of vehicle located in area 10, for example passenger cars 4 or buses or lorries 4 'in FIGS. 2 and 3, can be recognized up to the maximum density determined by pitch r are.

In addition, there is an evaluation device 21 for evaluating the recorded image 3 with respect to at least one

Density of at least one type of vehicle is present, which can be present in the body 2.

As already mentioned, the grid dimension r should be so small that on the image of the area 10 there is a local association between at least one density of at least one type of vehicle, for example vehicles 4 or 4 'and at least one traffic route 110 available for this type of vehicle 4 121, 122 of area 10 can be seen. A grid dimension r of 2 m is sufficient for this.

The evaluation device 21 is designed, for example, in such a way that it converts a certain information content of a recorded image 3 into coded data signals 22.

The image recording device 20 and evaluation device 21 can be realized by the fully geocoded interferometric radar with synthetic aperture developed for field recordings for purposes other than the detection of a traffic condition, which is known from the TRANS catalog of MST Aerospace GmbH, Cologne, Federal Republic of Germany. who has no suggestions or references in relation to the present Invention there. In retrospect, from the point of view of the finished invention, this system is particularly suitable for large-scale detection of a state of road traffic, be it via an earth satellite or via an aircraft.

The coded data signals 22 generated by the evaluation device 21 are transmitted to a processing device 30, which processes the data signals 22 to obtain information about a traffic condition in the area 10, for example with the aid of a computer. The processing device 30 is preferably accommodated in a ground station on the earth's surface 1. The transmission of the coded data signals 22 are preferably transmitted in the form of electromagnetic waves from the body 2 through the free space to the ground station.

The information about a traffic condition in the area 10 obtained in the processing device 30 from the data signals 22 can be fed via various transmission paths or information channels to one or more different usage devices for using such information. A usage device can be, for example, a radio transmitter 40, by means of which the road users can be informed about the traffic situation in the area 10 via radio, a device 50, which, for example, makes predictions about the traffic development in the area 10 by means of before and after comparisons and much more. For example, the device 50 can forward its forecasts to a traffic control center 60, which can use them to control the flow of traffic on the roads, for example via variable display devices 70 which display target speeds to the road users.

Images 3 of one and the same area 10 can also be evaluated and / or compared with one another, from different bodies 2, for example from a satellite and have been recorded from an aircraft, in particular also if these images have different grid dimensions.

Claims

claims

1. A method for detecting a traffic condition of vehicles, in which - from a body (2) located at a distance (a) above the earth's surface (1) from an image (3) of an area (10) which is below the Body (2) is located on and / or above the earth's surface (1) and has a lateral diameter of at least one kilometer, is recorded with such a small pitch (r) that densities (d) at least one in the area (10) Type of vehicles (4, 4 ') up to a predetermined maximum density (d = r) can be recognized, and in which - the recorded image (3) with respect to at least one density (d) of at least one type of vehicle (4, 4') ) is evaluated.

2. The method of claim 1, wherein an earth orbiting satellite is used as the body (2).

3. The method according to claim 2 or 3, wherein an image (3) of an area (10) with a diameter (b, 1) in the order of at least ten kilometers is recorded.

4. The method according to any one of the preceding claims, wherein a geostationary earth satellite is used as the body (2).

5. The method according to any one of the preceding claims, wherein an aircraft is used as the body (2).

6. The method according to claim 5, wherein an image (3) of an area (10) with a diameter (b, 1) in the order of one kilometer is recorded.

7. The method according to any one of the preceding claims, wherein an image (3) is recorded by means of radar radiation (5).

8. The method according to any one of the preceding claims, in particular claim 7, wherein an image (3) is recorded using interferometry.

9. The method according to any one of the preceding claims, in particular claim 7, wherein an image (3) is recorded using the Doppler effect.

10. The method according to any one of the preceding claims, wherein a local association between a density (d) of an image (3) of the area (10) of vehicles (4, 4 ') and a traffic route (110, 121 122) of the area (10) is produced by means of georeferencing.

11. The method according to any one of the preceding claims, wherein after taking an image (3) of the area (10) at least one further image of the area (10) is taken and also with respect to the at least one density (d) of the at least one specific in the area (10 ) located type of vehicles (4, 4 ') is evaluated, and at least two images (3) recorded in this way are compared with one another.

12. The method according to claim 11, wherein at least one sequence of two images (3) of the area (10) is produced by individual snapshots consecutive in time within one hour.

13. Arrangement for detecting a traffic condition, with - an image recording device (20) which is attached to a body (2) located at a distance (a) above the surface of the earth (1) and for taking an image (3) of one Area (10), which is located below the body (2) on and / or above the earth's surface (1) and has a lateral diameter of at least one kilometer, serves with a pitch (r) so small that densities (i.e. ) to- at least a certain type of vehicles (4,4 ') in the area (10) up to a certain maximum density (d = r) can be recognized, and

- an evaluation device (21) for evaluating the recorded image (3) with respect to at least one density (d) of at least one type of vehicle (4, 4 ').

14. The arrangement according to claim 13, wherein the evaluation device (20) converts a certain information content of a recorded image (3) into coded data signals (22).

15. The arrangement according to claim 14, wherein the evaluation device (20) generates georeferenced coded data signals (22).

16. The arrangement according to claim 15, wherein a processing device (30) for processing the data signals (22) to obtain information about a traffic condition in the area (10) is present.

17. The arrangement as claimed in claim 16, wherein a usage device (40, 50) for using information obtained about a traffic condition is present in the area (10).