EP3465653A1

EP3465653A1 - Method, device and system for detecting wrong-way drivers

Info

Publication number: EP3465653A1
Application number: EP17717425.7A
Authority: EP
Inventors: Simon GEISLER
Original assignee: Robert Bosch GmbH
Current assignee: Robert Bosch GmbH
Priority date: 2016-06-07
Filing date: 2017-04-13
Publication date: 2019-04-10
Also published as: DE102016210027A1; US20190279504A1; CN109313851B; US10769942B2; WO2017211489A1; CN109313851A; JP2019519044A

Abstract

The invention relates to a method for detecting wrong-way drivers, comprising the following steps: inputting positional data (106) via an interface, the positional data (106) representing a measured position of a vehicle (100); a step of inputting map data (116) which represents the sections of the road which can be driven by the vehicle (100); a step of inputting a plurality of particles, one particle representing a position occupied by the vehicle (100) and a weighting associated with the occupied position, and a step of determining a deviation between the plurality of particles and the measured position represented by the positional data (106) using the map data (116).

Description

Description title

Method Apparatus and system for wrong driver recognition state of the art

The invention is based on a device or a method according to the preamble of the independent claims. The subject of the present invention is also a computer program.

False drivers ("ghost drivers") cause at least substantial property damage in the event of an accident

Navigation device (street class and direction) is too late for most cases, i. the wrong-way driver is already on the wrong lane (at high speed and with a high probability of collision).

Disclosure of the invention

Against this background, with the approach presented here, a method, furthermore a device and a system for wrong-way driver recognition, and finally a corresponding computer program according to the

Main claims presented. The measures listed in the dependent claims advantageous refinements and improvements of the independent claim device are possible.

An example cloud-based forwarder warning can be advantageously realized with a specially adapted to the application detection with a particle filter.

A method for detecting wrong-way drivers comprises the following steps:

Reading position data via an interface, the position data representing a measured position of a vehicle; Reading in map data depicting road sections accessible by the vehicle;

Reading a plurality of particles, wherein a particle represents an assumed position of the vehicle and a weight assigned to the assumed position; and

Determining a deviation between the plurality of particles and the measured position represented by the position data using the map data.

The deviation can be used to determine whether the majority of particles match the measured position.

The vehicle may be a road vehicle. A wrong travel can be understood to mean a journey of the vehicle on a road contrary to a prescribed direction of travel. The measured position may have been measured using a sensor disposed in the vehicle. The map data may map a road network drivable by the vehicle. The plurality of particles may have been determined using a method used with known particulate filters or using a particulate filter. The particles may have different assumed positions, which may be grouped around the measured position, for example. The

Deviation can be used to determine or protect a current position of the vehicle. The current position can be an under

Use the Particle Filter to present the estimated position that can be used as the actual position of the vehicle. The current position can be used instead of the measured position for detecting a wrong-way of the vehicle.

The method may include a step of determining a wrong-way signal using the current position. In this case, the wrong-way signal can indicate whether a wrong-way drive of the vehicle is present or not present. For example, the wrong-way signal can be provided only if a wrong-way is assumed.

The method may include a step of determining the plurality of particles using a particulate filter. For example, a weighting of the particles can be changed by the particle filter.

The deviation may be determined in the step of determining using a distance between the plurality of particles and the measured position. The smaller the distance, for example a mean distance, the smaller the deviation will be.

The map data may be parameters of the vehicle passable

Map road network. In the step of determining, the deviation can be determined using the parameters. By using the parameters, the information included in the map data can be used to determine the deviation.

The method may include a step of reading in movement data representing measured movements of the vehicle. In the step of determining, the deviation may be determined based on a match between the motion data and the parameters. As a result, the deviation can be determined even more accurately.

For example, the movement data can map a lateral acceleration of the vehicle. The parameters may map a curvature of a road segment mapped by the map data associated with or attributable to at least one of the plurality of particles. This makes it possible to check whether a road section matches a movement performed by the vehicle.

The motion data may map a direction of travel of the vehicle and the parameters map a heading specification of a road segment mapped by the map data associated with or attributable to at least one of the plurality of particles. In this way, it can be checked whether a road section fits to a direction of travel of the vehicle. The method may include a step of selecting at least one plausible road segment from those represented by the map data

Road sections include. In this case, the plausible road section may represent a road section to which at least one of the plurality of particles can be assigned, and one with the direction of travel of the vehicle

has the same direction of travel.

In the step of determining, the deviation may be based on a

Assignability of the plurality of particles to the road sections mapped by the map data. If the particles can not be assigned to a road section, this indicates a large deviation.

In the read-in step, the position data can be read in via an interface of a computer cloud, a so-called cloud. This enables a cloud-based solution.

A corresponding device for identifying wrong-way drivers is set up to execute steps of said method in corresponding units. For example, such a device may comprise a read-in device, which is designed to read position data via an interface, has a further read-in device, which is designed to read in map data depicting vehicle passable road sections, having a further read-in device, which is embodied to read in a plurality of particles, wherein a particle represents an assumed position of the vehicle and a weight assigned to the assumed position, and a determination device configured to detect a deviation between the plurality of filtered particles and the measured position comprised by the measurement signal Use of map data to determine. Furthermore, the device may comprise a particle filter for producing and / or further processing the particles.

A corresponding system for detecting wrong-way drivers comprises at least one transmitting device which can be arranged or arranged in a vehicle and is designed to transmit position data, as well as a named one False driver recognition device, which is designed to receive the position data transmitted by the at least one transmitting device

received, for example via a wireless connection.

Another system for false driver detection includes at least one

A transmission device that can be arranged or arranged in a vehicle and is configured to transmit position data, the position data representing a measured position of a vehicle, and at least one receiving device that can be arranged or arranged in the vehicle and is configured to supply data to a device received, which is designed according to the approach described here for wrong driver identification to receive the transmitted from the at least one transmitting device position data.

The method described may be implemented in software or hardware or in a hybrid of software and hardware, for example in a device.

For this purpose, the device can have at least one arithmetic unit for processing signals or data, at least one memory unit for storing signals or data, and / or at least one communication interface for reading in or outputting data that is included in a

Embedded communication protocol. The arithmetic unit can

For example, be a signal processor, a microcontroller or the like, wherein the memory unit is a flash memory, an EPROM or a

magnetic storage unit can be. The communication interface can be designed to read or output data wirelessly and / or by line, wherein a communication interface that can read or output line-bound data, for example, electrically or optically read this data from a corresponding data transmission line or output to a corresponding data transmission line.

In the present case, a device can be understood as meaning an electrical device which processes sensor signals and outputs control and / or data signals in dependence thereon. The device may have an interface, which may be formed in hardware and / or software. At a For example, in terms of hardware, the interfaces may be part of a so-called system ASIC, which includes various functions of the device. However, it is also possible that the interfaces are their own integrated circuits or at least partially consist of discrete components. In a software training, the interfaces may be software modules that are present, for example, on a microcontroller in addition to other software modules.

Also of advantage is a computer program product or computer program with program code which can be stored on a machine-readable carrier or storage medium such as a semiconductor memory, a hard disk memory or an optical memory and for carrying out, implementing and / or controlling the steps of the method according to one of the above

described embodiments, in particular when the program product or program is executed on a computer or a device.

Embodiments of the approach presented here are shown in the drawings and explained in more detail in the following description. It shows:

Fig. 1 shows a system for Falzfahrererkennung according to a

Embodiment;

FIG. 2 is a flowchart of a method for detecting wrong-way drivers according to an embodiment; FIG.

3 shows a Hidden Markov Chain Model;

4 shows a sequence of a particle filter process according to a

embodiment

Fig. 5 shows a system for wrong driver identification according to a

Embodiment;

6 shows a vehicle according to an embodiment; 7 shows a program sequence according to an embodiment;

8 shows a program sequence of a particle filter according to a

Embodiment; and

9 is an illustration of road sections according to a

Embodiment.

In the following description of favorable embodiments of the present invention are for the in the various figures

represented and similar elements acting the same or similar

Reference numeral used, wherein a repeated description of these elements is omitted.

Fig. 1 shows a system for wrong driver identification according to a

Embodiment. The system includes a vehicle 100 that has a

Transmission device 102, which is configured to wirelessly using a at least one sensor device 104 arranged in the sensor 100 measured data, here for example position data 106 and optionally motion data 107, wirelessly to a device 110 for

Send wrong-way driver recognition. The device 110 is designed to prepare the measurement data into prepared data and to further process the processed data using a particle filter

False drive signal 112 to generate and send out. The wrong-way signal 112 indicates, according to one embodiment, that the vehicle 100 whose measurement data has been processed currently performs a wrong-way drive. According to this exemplary embodiment, both the transmission device 102 of the vehicle 100 and a transmission device 102 of another vehicle 100 are configured to receive the wrong-way signal 112 and to activate a warning device of the respective vehicle 100, 114, which, for example, is one in response to a reception of the wrong-way signal 112 Driver of the respective vehicle 100, 114 before the wrong drive warns or according to a

Embodiment in an at least semi-automatic control,

For example, a brake system or steering system, the respective vehicle 100, 114 engages. According to different embodiments, the Transmission device 102 may be designed only as a transmitting device or as a transceiver device.

According to an embodiment, the measurement data includes the position data 106 obtained by using a position determination device of the

Vehicle 100 were detected and depict a current position of the vehicle 100. According to a further exemplary embodiment, the measurement data further comprises the movement data 107, which were acquired, for example, using at least one acceleration sensor of the vehicle 100 and information about a current movement of the vehicle 100,

For example, information about a direction of travel, a

Longitudinal acceleration, a lateral acceleration or over a rotation of the vehicle about a vehicle axis include.

In one embodiment, the device 110 is configured to read in map data 116 indicative of a vehicle passable by the vehicle 100

Map road network. According to one embodiment, the

Map data 116, for example, information about road sections of the road network. According to an exemplary embodiment, the map data 116 with respect to each road section further comprises at least one parameter that defines, for example, a driving direction specification for the respective road section or a course of the respective road section. For example, it can be defined via the parameter whether the road section runs in a straight line or describes a curve. According to one embodiment, the device 110 has a memory device in which the map data 116 are stored.

In one embodiment, the device 110 is configured to read a plurality of particles. For example, the particles can be read from an internal or external storage device. Each particle may represent an assumed position of the vehicle and a weight assigned to the assumed position. In one embodiment, the apparatus 110 is configured to determine and directly process the plurality of particles using the position data 106 and the map data 116. According to one embodiment, the device 110 is configured to detect a deviation between the plurality of particles and that through which

Position data 106 mapped measured position of the vehicle 100 using the map data to determine. The deviation is used or taken into account in the determination of the wrong-way driver signal 112 according to one exemplary embodiment.

In one embodiment, the device is 110 or

Function blocks of the device 110 are arranged or realized in a cloud 118.

The described approach can be used in addition to or instead of various methods for detecting a wrong-way driver, in which e.g. the use of a video sensor is used to detect the passage of a "forbidden entry" sign or the use of a digital map is used in conjunction with a navigation to detect a detection of a wrong direction of travel on a road section, which is only passable in one direction Furthermore, the approach can be combined with wireless methods that detect wrong-way drivers by means of infrastructure such as beacons in the lane or at the lane.

In addition to the detection of a wrong-way driver, the described approach offers many possibilities of responding to a wrong-way driver. Examples are the warning of the wrong driver himself via a display or acoustic information. Also, methods may be used to warn other drivers in the vicinity of a wrong-way driver, e.g. via vehicle-vehicle communication or via mobile radio. Furthermore, the warning of other road users on the roadside established variable traffic signs is possible. An intervention in the engine control or brake of the wrong-traveling vehicle 100 can also take place.

The approach described makes it possible to detect a wrong-way driver and to warn other road users in the vicinity in time, for which there is very little time available. The described approach uses for a wrong-way driver detection (Wrong Way Driver Detection) with a client-server solution. As a client, a device can be seen, located on or in a motor vehicle, which has a

Intemetanbindung and has at least access to position coordinates. For example, this may be the transmission device 102. The transmission device 102 may be, for example, a

Smartphone act. In the transmission device 102, the

Sensor device 104 may be integrated. Thus, wrong-driver-specific server-client communication can be implemented with a smartphone as an exemplary client. The smartphone can be connected via a mobile radio network with a gateway (PDN_GW) to the Internet, in which the device 110, for example in the form of a server, can be arranged.

The following key problem areas for this technology result from the possible functions of a wrong-way driver warning with a client-server solution, which are addressed by the approach described here: a) False-positive reduction

False positives, ie misdetections with the correct driving style, must be reduced as far as possible or completely avoided in the case of a self-warning and / or active intervention. Depending on the warning concept, the standards must meet up to ASIL-A. b) Time-critical execution of the trip chain

To the endangerment of other road users starting from a

To keep wrong-hand drivers as low as possible should be intervened or warned as quickly as possible. Ie. The complete functional chain from the detection of a critical situation to the detection of a wrong-way driver until intervention or warning should be carried out in as short a time as possible. The utilization and thus the required performance of the server,

For example, the device 110, in a nationwide use of this function plays a very important role. In addition to the trip time also the economy represents an important aspect. c) communication, data efficiency and power consumption

Communication and power consumption, especially for mobile devices, must be as low as possible to achieve acceptable battery life. The overload of a mobile radio cell or other wireless communication unit must also be ensured by a data-efficient one

Communication be prevented. Also the data volume and the associated costs are, as far as possible, to be limited. The efficiency of communication is an extremely important factor on the server side for computing power reasons.

The approach described above is mainly used for the key fields a) "false-positive reduction" and b) "time-critical execution of the trigger chain", but also c) "communication, data efficiency and power consumption"

possibly influenced by it. The detection of wrong-way drivers in the cloud 118, based on standard smartphone and connectivity control unit sensor technology is not a trivial undertaking.

FIG. 2 shows a flowchart of a method for wrong-way driver recognition according to one exemplary embodiment. The method may, for example, be carried out using devices of the device for false driver recognition shown with reference to FIG.

The method comprises a step 201, in which position data are read in via an interface. The position data represent a measured position of a vehicle. Optionally, motion data of the vehicle can additionally be read in step 201. In a step 203, map data is read in which can be driven by the vehicle

Map road sections. The map data may include parameters that specify the individual road sections in more detail, for example with regard to a roadway curvature or driving direction. In a step 205, a plurality of particles are read. The plurality of particles may, for example, have been created in a previous creation step using the position data and / or previously filtered particles. In this case, one of a particle filter is used according to an embodiment. Each of the particles represents an assumed position of the vehicle and a weight assigned to the assumed position. The assumed positions are distributed according to one

Embodiment preferably around the measured position. Typically, the assumed positions differ from the measured position and the actual position of the vehicle. In a step 207, a

Deviation between the majority of particles and that of the

Position data represented measured position using the map data determined. The determination of the deviation may represent a partial step of steps performed in the particulate filter. According to an embodiment, using the particulate filter, a current position of the vehicle based on the plurality of particulates and the deviation is determined using.

In a further optional step 209, a wrong-way signal is generated and provided using a plurality of particles and the deviation. For example, the wrong-way signal can be provided if a plausible route section is determined from the plurality of particles and the deviation, which is assumed to be on the vehicle, and a current travel direction of the vehicle does not coincide with a direction indication associated with the route section. According to one embodiment, generating or providing a

Falschfahrersignals depending on a comparison of the deviation with a threshold enabled or disabled. For example, a

Provision of the wrong-drive signal can be prevented if, due to a large deviation is assumed that a determined using the particulate filter current position of the vehicle does not match the actual position of the vehicle.

For the wrong driver recognition, it is not decisive which route the wrong-way driver drove. Above all, the information needed is where the wrong-way driver is currently located and whether this is a street opposite

Driving direction. Of course, the history is needed for this determination, but this is not part of the question, but rather the way to the result. Due to these circumstances, a method based on a particle filter is presented. Similar to the Kalman filter, the particle filter is applicable to systems which are subject to a hidden Markov chain characteristic, ie a Markov chain with unobserved states.

Fig. 3 shows a Hidden Markov Chain Model 320 with state x and observation z at time k and k-1.

That is, the state of a system can not be measured directly, but estimated based on other observations. In this case, it is important to estimate the position and thus the current road. For this, the following equation must be solved:

The state at time k is described below with X _k , the previous states are with summarized. Analogous to x, this convention also applies to the control quantities u and

Observations u. η describes a normalization term, which in the following, however, has no great importance. This equation can be simplified to the following equation:

And these are described in two steps: the forecasting step

and the weighting term:

For a particle filter, the integral over the

Probability distributions with a numerical approximation

and Monte Carlo methods solved. here describes the weight / the

Probability of the young particle. A lot of particles will be with you

described. Thus, each particle has the weight and the condition

4 shows the sequence of a particle filter process according to a

Embodiment. For this purpose, a hidden Markov Chain Model with the state x and the observation z at time k and k-1 is shown in FIG.

Much of the work is a suitable feature for

to find the optimal picture of the problem. The basis for this is to define the states x to be estimated.

Block 401 stands for the particle filter (xk-i, Uk, z)

From block 403, jump is made to block 405 until all values j = 1: J have passed. In block 405, a new state is calculated:

In block 407, the weight is calculated:

If all values have been traversed in block 403, block 409 is entered. From block 409, jump to block 411 until all values have passed.

In block 411, a value is drawn in accordance with.

In block 413, the particle set is added according to

If all values have been traversed in block 409, jump to block 415 which represents the end.

Fig. 5 shows a system for wrong driver recognition according to a

Embodiment. The system comprises devices 102, for example in the form of the transmission means referred to with reference to FIG. 1 and a

Device 110 for wrong driver identification, according to this

Embodiment designed as a so-called WDW server. The device 110 is designed to receive data 106 from the device 102,

For example, to receive the measurement data described with reference to FIG. 1 and to provide a warning 112 based on the data 106 and to send back to the devices 102, for example in the form of the wrong-way signal described with reference to FIG.

The apparatus includes pre-processing means 530, particulate filter 532, and warning module 534.

In a simplified cloud-based forklift warning architecture, the particulate filter 532 embeds as shown in FIG. With the particle filter 532, the probability distribution of the position of the car can be approximated.

FIG. 6 shows by means of a vehicle 100 values that can be included in the model shown with reference to FIG. 5. The values may, for example, be states in the direction of the longitudinal axis x, the transverse axis y, the vertical axis z, as well as a roll p about the longitudinal axis, a pitch q about the transverse axis and a yaw r about the vertical axis.

Concerning map matching using the particle filter, the Bayesian filter can be understood by referring to FIG. 3

Xk stands for what the condition (not measured) is, for example, the latitude, longitude, latitude, Uk + i, how the car 100 moves, for example, in terms of speed and yaw rates, and Zk stands for what can be observed for example, a G PS signal or a signal relating to the environment of the vehicle 100 (camera, etc.)

Fig. 7 shows a program flow according to an embodiment. The process starts with a block 701. In a block 530, a

Data preprocessing performed, as described for example with reference to FIG. 5. In a block 703, if present, the state is loaded from the previous point. In a block 705, a map matching takes place with the particle filter. In a block 707 is a

Interpretation of the results. In a block 709 it is checked if a

Wrong course exists. If so, a warning is sent in block 534, as described, for example, with reference to FIG. If there is no wrong drive, the end of the program sequence takes place with a block 711.

FIG. 8 shows a program flow of a particle filter according to a

Embodiment. A block 801 stands for a beginning of the particle filter. In a block 803, a displacement of the particles taking into account the sensor inaccuracy, for example, the sensor device described with reference to FIG. 1 takes place. In a block 805, a determination of the

card-related parameters. Such a parameter indicates, for example, whether a particle is on a road or what its title is. In a block 807, a calculation of the new particle weights takes place. In a block 809 a so-called resampling takes place in which an elimination of the irrelevant regions and / or particles takes place. In a block 811 an interpretation of the individual particles takes place and in a block 813 a return of the possible roads.

By using the particulate filter, the following aspects are improved. On the one hand, a sequential (real-time possible) working method is created, which primarily determines the current position on the road network. Furthermore, a robust estimate of the current position on the road network is possible. An uncertainty about the current estimate can be determined. This makes it possible to delay the decision on a potential wrong-way reliably to a reasonable extent.

9 shows a representation of road sections 930, 932, 934, 936 according to one exemplary embodiment. The road sections 930, 932, 934, 936 are part of one of a vehicle, for example with reference to FIG. 1

described vehicle, passable road network. A plurality 940 of particles is substantially distributed among the three road sections 932, 934, 936. Each of the particles indicates an assumed position and an assumed probability or weighting. As can be seen from FIG. 9, a measured position 950 of the vehicle deviates significantly from the positions assumed for the plurality 940 of particles. The measured position 950 is also associated with a direction vector, which may represent a measured heading of the vehicle and may have been determined using motion data received from the vehicle. Each of the plurality 940 of particles is also associated with a direction vector indicating a direction of movement of the respective particle. From FIG. 9 it can be seen again that the direction vector associated with the measured position 950 does not coincide with the direction vectors associated with the plurality 940 of particles.

The plurality 940 of particles have been determined according to an embodiment as described with reference to the preceding figures using a particle filter. A deviation between the measured Position 950 and the assumed positions of the plurality 940 of particles is determined as a partial functionality of the particulate filter according to one embodiment. The deviation can then, for example, for further

Filtering of the majority 940 of particles are used.

The approach described here is classified in a part of the particle filter shown for example with reference to FIG. 5.

Even the best method can sometimes not match the vehicle reliably on a road, due to various circumstances, such. B. incorrect map data. Thus, it can happen that the sensor data

(Observations, Controls) does not fit the current road and

For example, the position of the particles 940 deviates very far from the GPS. This is illustrated for example in FIG. 9.

The particles 940 represent particles from the current calculation cycle (k) with the direction vector. The position 950 represents a current (k) GPS position with a direction vector.

To detect this condition, various measures can be calculated which give an indication that the positions of the particles do not coincide with the

Observations match. Parameters could be (including the temporal behavior / change of these):

Curvature of the road on which a particle 940 is located does not match

Sensor data match.

Heading the road on which a particle 940 is located does not match sensor data.

Many 940 particles are moved so they do not end up on a road.

The median / mean / minimum / maximum distance between particle 940 and GPS position 950 is exceptionally large. In this case, for example, it suggests that the conditions for the topology are canceled (fallback level).

If no particle 940 is on a road (fallback level), the

Weighting equation (observation model) according to an embodiment adapted as follows:

For both fallback levels, in the case of particles 940 landing again on roads, due to the false positives susceptibility, no road should be considered which is opposite to the direction of travel. Ie. The particles 940 can thus migrate to any road except on roads opposite to the road

Driving direction are. For example, in the embodiment shown in FIG. 9, the road element 936 would be excluded as a possible location for the particles 940 because that part of the highway is opposite to the direction of travel.

The approach described can be used in conjunction with a cloud-based driver error warning with a specially adapted to the application with a particulate filter detection. Particularly advantageous are the conditions for the two described fallback levels and the approach when roads are found again.

If an exemplary embodiment comprises an "and / or" link between a first feature and a second feature, then this is to be read so that the embodiment according to one embodiment, both the first feature and the second feature and according to another embodiment either only first feature or only the second feature.

Claims

claims

1. A method for detecting wrong-way drivers, wherein the method

following steps include:

Reading in (201) position data (106) via an interface, wherein the position data (106) comprises a measured position (950) of a

Represent vehicle (100);

Reading (203) map data (116) depicting road sections (930, 932, 934, 936) accessible by the vehicle (100);

Reading (205) a plurality (940) of particles, wherein a particle represents an assumed position of the vehicle (100) and a weight assigned to the assumed position; and

Determining (207) a deviation between the plurality (940) of particles and the measured position (950) represented by the position data (106) using the map data (116).

2. The method of claim 1, further comprising the step of providing a wrong-way signal using the plurality of particles and the deviation, wherein the wrong-way signal indicates whether a wrong-way of the vehicle is present or absent ,

A method according to any one of the preceding claims, comprising a step of determining the plurality (940) of particles using a particulate filter (532).

4. The method of claim 1, wherein in the step of determining the deviation is determined using a distance between the plurality of particles and the measured position.

5. The method as claimed in one of the preceding claims, in which the map data (116) map parameters of the road network accessible by the vehicle (100) and in step (207) of determining

Deviation is determined using the parameters. 6. The method according to claim 5, comprising a step of reading in

Motion data (107) representing measured motions of the vehicle (100), and wherein in step (207) of determining the deviation based on an alignment between the

Movement data (107) and the parameters is determined. 7. The method according to claim 6, wherein the movement data (107) depict a lateral acceleration of the vehicle (100) and the parameters a curvature of a through the map data (116)

mapped road section (930, 932, 934, 936) associated with at least one of the plurality (940) of particles or assignable. 8. The method according to claim 6 or 7, wherein the movement data

(107) map a direction of travel of the vehicle (100) and the parameters map a heading of a road section (930, 932, 934, 936) imaged by the map data (116) associated with or attributable to at least one of the plurality (940) of particles , A method according to claim 8, comprising a step of selecting

at least one plausible road section (934) from the road sections (930, 932, 934, 936) imaged by the map data (116), the plausible road section representing a road section to which at least one of the plurality (940) of particles is assignable and the one having the direction of travel of the vehicle (100) matching direction of travel. 1 0. A method according to any one of the preceding claims, wherein in

Determining the deviation based on assignability of the plurality (940) of particles to the particles Map data (116) of mapped road sections (930, 932, 934, 936) is determined.

11. The method according to any one of the preceding claims, wherein in the step of reading the position data (106) via an interface of a computer cloud (118) are read.

12. A device (110) for wrong driver identification, which is configured to perform steps of the method according to one of the preceding claims in corresponding units.

13. A system for detecting wrong-way drivers, the system comprising the following features: at least one transmitting device (102) which can be arranged or arranged in a vehicle (100) and is designed to transmit position data (106), wherein the position data (106) represent a measured position (950) of a vehicle (100); and a device (110) according to claim 12 for wrong driver identification, which is designed to receive the position data (106) emitted by the at least one transmitting device (102).

A computer program adapted to carry out the method according to any one of the preceding claims.

15. A machine-readable storage medium on which the computer program according to claim 14 is stored.