DE102019211102A1 - Method, device and computer program for evaluating traffic light information about a traffic light system - Google Patents

Abstract

A method for evaluating traffic light information about a traffic light system is described. All traffic light pairs are formed from traffic lights of the traffic light system. For each pair of traffic lights, a correlation factor is determined for a phase curve of a first traffic light of the pair of traffic lights and a phase curve of a second traffic light of the pair of traffic lights. The traffic lights are grouped taking into account the correlation factors of the traffic light pairs. Furthermore, a device and a computer program for processing the data about the traffic light system are described.

Description

The present invention relates to a method, a device and a computer program for grouping traffic lights in a traffic light system.

There are automatic driving functions of a motor vehicle that process information from traffic lights and surrounding vehicles in order to react accordingly in traffic.

From the DE 10 2013 220 662 A1 a method for recognizing traffic situations when operating a vehicle is known. Ambient data, such as the status of a signal system, a road delimitation, a lane delimitation, are recorded by means of a vehicle-mounted sensor system. Using the environment data, an environment model can be generated with the positioning of the own vehicle in it. A special lane and one or a maximum of two traffic light signals are assigned to the own vehicle.

The DE 10 2015 003 847 A1 describes a method and driver assistance system for detecting and signaling light signals from at least one light signal device of a traffic light. Here, an area of the surroundings in front of a vehicle is optically detected. The captured image data are evaluated for the presence of a red light signal from at least one light signal device. The red light signal from at least one light signal device recognized in the evaluated image data is displayed by means of a vehicle-side display device. Furthermore, by means of a corresponding operating action by a driver, there is a further, that is to say a time-lasting, display of the current light signal of the recognized light signal device.

The DE 10 2016 217 558 A1 describes a method for assigning a traffic light to a lane, wherein recognized phase sequences are compared with expected phase data. The phase data contain a number of expected phase sequences for respective lanes.

The challenge is therefore that it is not recognized by the driver but by means of a method, a device and / or a computer program which information is relevant for an automatic vehicle function. Particularly in the case of a vehicle that drives on a section of lane controlled by a traffic light system and is moving towards the traffic light system, the relevant traffic light information such as traffic light phases (also called “traffic light signal curves”) must be reliably recorded. This relevant traffic light information can then also be used as input for further driving functions.

The object of the present invention is to provide a method, a device and a computer program which at least partially overcome the above-mentioned challenge.

This object is achieved by a method according to claim 1, a device according to claim 14 and a computer program according to claim 15.

Further advantageous embodiments of the invention emerge from the subclaims and the following description of preferred exemplary embodiments of the present invention.

• - Bilden aller Ampelpaare aus Ampeln der Ampelanlage;
• - Ermitteln, für jedes Ampelpaar, einen Korrelationsfaktor für einen Phasenverlauf einer ersten Ampel des Ampelpaars und einen Phasenverlauf einer zweiten Ampel des Ampelpaars; und
• - Gruppieren der Ampeln unter Berücksichtigung der Korrelationsfaktoren der Ampelpaare.

A first aspect of the disclosure relates to a method for evaluating traffic light information about a traffic light system. The procedure includes:

• - Formation of all traffic light pairs from traffic lights of the traffic light system;
• - Determining, for each pair of traffic lights, a correlation factor for a phase curve of a first set of traffic lights of the pair of traffic lights and a phase curve of a second set of traffic lights of the pair of traffic lights; and
• - Grouping of the traffic lights taking into account the correlation factors of the traffic light pairs.

Traffic light information about a traffic light system is recorded by a detection device that is attached to a vehicle, for example. Using a swarm of vehicles equipped with detection devices, a large number of traffic light information can be recorded via the same traffic light system. This traffic light information is represented by traffic light objects, the traffic light objects corresponding to data or data records. The traffic light objects are described in more detail later. The term “traffic light” is used for a physical traffic light. A traffic light system comprises at least one traffic light.

The traffic light information is thus recorded by the vehicle-side recording devices and stored as traffic light objects, for example in a database. This database can be located on a server.

The traffic light information about the traffic light system in any case includes phase progressions of the (at least one) traffic light of the traffic light system. In addition, position data of the traffic light can also be recorded as traffic light information.

A phase curve of a traffic light is also referred to as a signal curve. A phase progression indicates phase durations and the corresponding traffic light phases. A phase progression for a traffic light can be represented as a plurality of time-traffic light phase pairs. A traffic light phase is a signal that a traffic light can display. As a rule, a traffic light has the (traffic light) phases “green”, “yellow”, “red” and “yellow and red”, whereby the traffic light phases are usually also displayed in this order by the traffic light. Other orders are also possible.

One step comprises the creation of all traffic light pairs from the traffic lights of the traffic light system. In this step, the possible pairing combinations (without repetition and without sequence) of the traffic lights of the traffic light system are created.

A correlation factor is determined for each pair of traffic lights. A phase profile of the first traffic light of a respective pair of traffic lights and a phase profile of the second traffic light of the respective traffic light pair are taken into account, in particular compared with one another. The correlation factor is a measure of the similarity between two phase courses. There are several approaches to determining the correlation factor. In particular, that approach is used that is described in the present disclosure.

In one step, the groups of traffic lights of the traffic light system are formed. This group formation takes place taking into account the correlation factors of the traffic light pairs determined above. It may well happen that a traffic light group formed in this way only has one traffic light of the traffic light system.

The traffic lights can be grouped using appropriate models, algorithms or heuristics. In particular, corresponding clustering methods can be used.

The correlation factors can be used to at least partially compensate for the detection and measurement inaccuracies of a detection device mounted on the vehicle when detecting the traffic light information via the traffic light system. Accordingly, traffic light groups, i.e. groups of traffic lights with the same phase progression, can be identified comparatively reliably using the above method.

In an alternative, the traffic lights located in a group have correlation factors with one another which exceed a first predetermined minimum correlation value. The correlation factor can be in an interval of [0; 1], with a correlation factor of 1 indicating an identical phase profile of two traffic lights. In one example, the predetermined minimum correlation value for the correlation factor may be 0.9. This ensures that not only traffic lights with identical phases, but also with sufficiently similar phases are grouped. Using the predetermined correlation value, the traffic lights with a common phase profile can be identified with a comparatively high probability from the traffic light information recorded. The measurement inaccuracies and errors of the vehicle-side detection devices can thus be compensated.

• - Ermitteln einer Überlappungsdauer zwischen den Phasenverläufen der Ampeln des Ampelpaars, in der sich der Phasenverlauf der ersten Ampel und der Phasenverlauf der zweiten Ampel zeitlich überlappen; und
• - Ermitteln einer Übereinstimmungsdauer, in der eine Ampelphase der ersten Ampel und eine Ampelphase der zweiten Ampel während der Überlappungsdauer übereinstimmen.

Furthermore, determining the correlation factor for each pair of traffic lights can include:

• Determination of an overlap duration between the phase profiles of the traffic lights of the pair of traffic lights, in which the phase profile of the first traffic light and the phase profile of the second traffic light overlap in time; and
• Determination of a coincidence period in which a traffic light phase of the first traffic light and a traffic light phase of the second traffic light coincide during the overlap period.

The phases of the traffic lights of the pair of traffic lights mean the phase of the first traffic light of the respective pair of traffic lights and the phase of the second traffic light of the respective pair of traffic lights.

The period of overlap indicates the period of time at which the two phases are present simultaneously, i.e. overlap in time.

The period of correspondence indicates the period of time at which the first and second traffic lights (or their phase profiles) have the same traffic light phase. Because the phase course of the first traffic light and the phase course of the second traffic light can coincide at times. In other words, the first and second traffic lights can temporarily have the same traffic light phase.

In this step, the correlation factor can therefore take into account the duration of overlap and duration of correspondence of the phase progressions of the traffic lights of the pair of traffic lights. This enables a statement to be made regarding the similarity of the phase progressions.

• - Abrufen von Ampelinformationen repräsentierenden Ampelobjekten, wobei jedes Ampelobjekt einer entsprechenden Ampel (der Ampeln der Ampelanlage) zuordenbar ist.

In an alternative, the following steps can also be included:

• - Retrieving traffic light objects representing traffic light information, each traffic light object being assignable to a corresponding traffic light (the traffic lights of the traffic light system).

As mentioned above, the traffic light information is stored in data or data records referred to as traffic light objects, each traffic light object being created from the data or a measurement recorded by a detection device on the vehicle. It can happen that not just one, but several traffic light objects are created for a traffic light. One of the reasons for this is that the detection device incorrectly detects the corresponding traffic light and creates several traffic light objects for this traffic light. Furthermore, during the detection by the detection device, a line of sight between it and the corresponding traffic light can be interrupted by an obstacle, so that once the obstacle has disappeared, the corresponding traffic light is detected again by the detection device and a new traffic light object is generated for it.

Furthermore, the traffic light objects can be recorded specific to the crossing. During a crossing, a vehicle is on the section of the lane controlled by the traffic light system and is moving towards and crossing the traffic light system. During this crossing, in particular continuously, the vehicle detects the traffic light system by means of its detection device. This allows the traffic light information to be evaluated more precisely, since traffic light objects resulting from a crossing, for example, can be better compared with one another.

• - Bilden von Überlappungsdauern und/oder von Übereinstimmungsdauern zwischen allen die erste Ampel repräsentierenden Ampelobjekten und allen die zweite Ampel repräsentierenden Ampelobjekten; und
• - Aufsummieren der Überlappungsdauern und/oder Aufsummieren der Übereinstimmungsdauern zwischen allen die erste Ampel repräsentierenden Ampelobjekten und allen die zweite Ampel repräsentierenden Ampelobjekten.

In an alternative, determining the correlation factor for each pair of traffic lights can further include:

• - Formation of overlapping periods and / or coincidence periods between all traffic light objects representing the first traffic light and all traffic light objects representing the second traffic light; and
• Summing up the overlap periods and / or summing up the correspondence periods between all traffic light objects representing the first traffic light and all traffic light objects representing the second traffic light.

Each traffic light object also represents a (recorded) phase progression of the corresponding traffic light. In other words, a corresponding phase course of the corresponding traffic light is stored / stored in each traffic light object. Corresponding overlapping periods and / or coincidence periods can be formed from the phase progressions from the traffic light objects.

• - Bilden aller Ampelobjektpaare aus Ampelobjekten, die einer der Ampeln zugeordnet sind;
• - Ermitteln, für jedes Ampelobjektpaar, eines Korrelationsfaktors für einen Phasenverlauf des ersten Ampelobjekts des Ampelobjektpaars und einen Phasenverlauf eines zweiten Ampelobjekts des Ampelobjektpaars; und
• - Gruppieren der Ampelobjekte (hinsichtlich ihrer Phasenverläufe) unter Berücksichtigung der Korrelationsfaktoren der Ampelobjektpaare;
• - Erkennen der einen Ampel als inkonsistent, wenn eine Anzahl an Ampelobjektpaargruppen einen vorbestimmten Maximalwert überschreitet; und
• - Ausschließen der inkonsistenten Ampel aus dem Bilden aller Ampelpaare.

Furthermore, the formation of the overlapping periods and / or the coincidence periods between all traffic light objects representing the first traffic light and all traffic light objects representing the second traffic light can take place in a crossing-specific manner. This makes the evaluation of the traffic light information, as mentioned above, more precise.
In an alternative, forming all traffic light pairs from the traffic lights can further comprise:

• - Formation of all traffic light object pairs from traffic light objects that are assigned to one of the traffic lights;
• Determining, for each pair of traffic light objects, a correlation factor for a phase curve of the first traffic light object of the pair of traffic light objects and a phase curve of a second traffic light object of the pair of traffic light objects; and
• - Grouping of the traffic light objects (with regard to their phase progressions) taking into account the correlation factors of the traffic light object pairs;
• - Recognition of the one traffic light as inconsistent when a number of traffic light object pair groups exceeds a predetermined maximum value; and
• - Exclude inconsistent traffic lights from creating all traffic light pairs.

As stated above, each traffic light object is assigned to a corresponding traffic light. In this step, the possible pairing combinations (without repetition and without sequence) of the traffic light objects belonging to a traffic light are created.

Since each traffic light object also represents a (recorded) phase curve of the corresponding traffic light, a correlation factor can be determined for each traffic light object pair.

Groups of traffic light objects can be formed in one step. This group formation takes place taking into account the correlation factors of the traffic light object pairs determined above.

The traffic light objects can be grouped using appropriate models, algorithms or heuristics. In particular, corresponding clustering methods can be used.

Each traffic light whose traffic light objects are considered can be identified as inconsistent if a number of groups from the traffic light objects considered exceeds a predetermined maximum value. The predetermined maximum value can be used here 1 be. So if two traffic light object groups can be recognized, this indicates that the traffic light objects of the corresponding traffic light indicate different phases of this corresponding traffic light. Accordingly, this corresponding traffic light can be viewed as inconsistent due to the different phase progressions represented by the traffic light objects.

In particular, the traffic light object pairs can also be formed specifically for each crossing. Accordingly, only traffic light objects assigned to a traffic light, which are created from the same crossing, are used to form the traffic light object pairs. For this corresponding traffic light, the traffic light objects can be grouped as described above for each crossing. A mean value for the number of traffic light object groups can then be formed over all crossings (for the corresponding traffic light). If this mean value exceeds the predetermined maximum value, which in this case can be 1.2, for example, the traffic light is identified as inconsistent.

By forming crossing-specific traffic light object pairs and thus crossing-specific traffic light object groups, the identification of inconsistent traffic lights can be made more reliable.

• - Zuordnen der inkonsistenten Ampel zu einer entsprechenden Ampelgruppe in Abhängigkeit von Korrelationsfaktoren zwischen den Ampelobjekten der inkonsistenten Ampel und den Ampelobjekten der entsprechenden Ampelgruppe.

An alternative may further include

• - Assigning the inconsistent traffic light to a corresponding traffic light group depending on correlation factors between the traffic light objects of the inconsistent traffic light and the traffic light objects of the corresponding traffic light group.

This means that an inconsistent traffic light can also be assigned to a corresponding traffic light group. This ensures that every traffic light recorded can be assigned to a traffic light group.

Here, the inconsistent traffic light can also be assigned to the corresponding traffic light group on the condition that the number of correlated traffic light objects from the traffic light objects of the inconsistent traffic light and traffic light objects of the corresponding traffic light group corresponds to at least a predetermined fraction of the number of all (correlated and uncorrelated) traffic light objects that assigned to the inconsistent traffic light. For example, the predetermined fraction can be a third. That is, if the number of correlated traffic light objects corresponds to at least one third of all traffic light objects assigned to the inconsistent traffic light, the inconsistent traffic light is assigned to the corresponding traffic light group.

Only the correlated traffic light objects are taken into account for the following steps, the rest corresponds with a high probability of detection of a turning traffic light. Such a turning traffic light can be evaluated by other appropriate measures.

• - Prüfen der durch die Ampelobjekte repräsentierten Phasenverläufe auf Plausibilität; und
• - Verwerfen von nicht-plausiblen Ampelobjekten.

In an alternative, the method can further comprise:

• - Checking the phase progressions represented by the traffic light objects for plausibility; and
• - Rejection of implausible traffic light objects.

• - Gelb-Phase auf Rot-Phase;
• - Rot-Gelb-Phase auf Grün-Phase;
• - Grün-Phase oder Rot-Gelb-Phase auf eine Gelb-Phase; oder
• - Rot-Phase oder eine Gelb-Phase auf eine Rot-Gelb-Phase.

A phase course is identified as implausible if it has one of the following sequences of traffic light phases within its phase course:

• - Yellow phase to red phase;
• - red-yellow phase to green phase;
• - Green phase or red-yellow phase to a yellow phase; or
• - Red phase or a yellow phase to a red-yellow phase.

This increases the reliability of recognizing the traffic light groups with the same phase progression.

• - Startzeitpunkt des entsprechenden Phasenverlaufs mit einer entsprechenden Phase;
• - Wechselzeitpunkte des entsprechenden Phasenverlaufs mit einer entsprechenden Phase; und
• - Endzeitpunkt des entsprechenden Phasenverlaufs.

Furthermore, each traffic light object can contain the following information for the corresponding phase progression:

• - Start time of the corresponding phase progression with a corresponding phase;
• - Change times of the corresponding phase course with a corresponding phase; and
• - End time of the corresponding phase progression.

As mentioned above, a phase curve can be represented as a multiplicity of time / traffic light phase pairs. The starting time (first recording time) indicates the moment at which the traffic light is recorded for the first time by the vehicle-side recording device. At the change times (phase change times), phase changes take place, in particular according to the phase sequence described above. At the end time (last time of detection), the traffic light is detected for the last time by the detection device on the vehicle.

In this way, an actually continuous phase course of the traffic light can be reduced to the few above data (times and traffic light phase) by means of the traffic light objects. This enables the phase progression of a traffic light to be reproduced reliably, while at the same time reducing the amount of data per traffic light object.

In an alternative, the traffic light objects can be detected by a large number of vehicles. In particular, the traffic light information can be detected by detection devices that are attached to a large number of vehicles.

In an alternative, the grouping of the traffic lights can take place using a first hierarchical clustering method and / or the grouping of the traffic light objects using a second hierarchical clustering method.

A hierarchical clustering process or a hierarchical cluster analysis is a distance-based process for the structure discovery in databases such as the present traffic light objects. The resulting clusters (traffic light groups) consist of traffic lights / objects that have a predetermined similarity to one another than to the traffic lights / objects of other clusters.

A second aspect of the disclosure relates to a device for evaluating traffic light information about a traffic light system, the device being designed and set up to carry out one of the methods described above.

A third aspect of the present disclosure relates to a computer program for evaluating traffic light information about a traffic light system, the computer program being designed to cause a data processing device to execute one of the methods described above when it is executed.

• 1 schematisch einen durch eine Ampelanlage gesteuerten Fahrbahnabschnitt;
• 2 schematisch ein Verfahren zum Aufbereiten von Daten über die Ampelanlage;
• 3 schematisch ein Ampelbild der in 1 gezeigten Ampelanlage;
• 4 schematisch ein beispielhaftes Ampelbild der in 1 gezeigten Ampelanlage;
• 5 schematisch ein Verfahren zur Gruppierung von Ampeln der in 1 gezeigten Ampelanlage;
• 6 schematisch ein Verfahren zum Bestimmen einer Ampelphase einer Ampel; und
• 7 schematisch und beispielhaft zwei Phasenverläufe.

Embodiments of the invention will now be described by way of example and with reference to the accompanying drawings. It shows:

• 1 schematically a section of the roadway controlled by a traffic light system;
• 2 schematically a method for processing data on the traffic light system;
• 3 schematically a traffic light image of the in 1 shown traffic lights;
• 4th schematically an exemplary traffic light image of the in 1 shown traffic lights;
• 5 schematically a method for grouping traffic lights of the in 1 shown traffic lights;
• 6 schematically a method for determining a traffic light phase of a traffic light; and
• 7th schematic and exemplary two phase courses.

In 1 is schematically a road section Fb shown by a traffic light system A. is controlled. The road section Fb has three lanes F1 , F2 , F3 on that in turn by four corresponding traffic lights tp ₁ , tp ₂ , tp ₃ , tp ₄ , tp ₅ the traffic lights A. to be controlled. The traffic lights A. can through a the road section Fb driving vehicle 1 are recorded. In particular, the individual traffic lights tp ₁ -tp ₅ the traffic lights A. detected. The vehicle points to the detection 1 a detection device 3 on, which is designed in such a way, a phase curve and / or position of each traffic light tp ₁ -tp ₅ capture. Furthermore, the detection device 3 at least one of the position, position progression and orientation of the vehicle 1 to capture. The detection device 3 can, for example, comprise a camera device and a GPS device. The vehicle 1 has a radio interface 5 to communicate with a server / database (not shown) and to exchange data.

In the context of the disclosure means “detection by the vehicle 1 "Also" detection by the vehicle-side detection device 3 ".

A phase curve of a traffic light is also referred to as a signal curve. A phase progression indicates phase durations and the corresponding traffic light phases. A phase progression for a traffic light can be represented as a plurality of time-traffic light phase pairs. A traffic light phase is a signal that a traffic light can display. As a rule, a traffic light has the (traffic light) phases “green”, “yellow”, “red” and “yellow and red”, whereby the traffic light phases are usually also displayed in this order by the traffic light. Other orders are also possible.

It goes without saying that the vehicle 1 can also exemplify a variety of vehicles, so that the traffic light system A. can be detected by a variety of vehicles. So is for every crossing over the traffic lights A. by a vehicle 1 A vehicle-specific, or otherwise called crossing-specific, data record can be created from the multitude of vehicles. This crossing-specific data record is described later.

With the present traffic light system A. control the traffic lights tp ₁ , tp ₂ the lane F1 and the traffic lights tp ₃ , tp ₄ the lanes F2 , F3 . Thus form the traffic lights tp ₁ , tp ₂ a first group of traffic lights and the traffic lights tp ₃ , tp ₄ a second group of traffic lights. The traffic lights tp ₁ , tp ₂ the first set of traffic lights have the same first phase progression and the traffic lights tp ₃ , tp ₄ the second traffic light groups have the same second phase progression. In other words, the respective traffic lights of the traffic light groups have the same phase progression. Finally, the lane can F3 additionally through a separate right-turn traffic light tp ₅ be controlled.

In the 2 is a method for creating a traffic light image 7th shown. With traffic light 7th a data record is meant that can be stored in a database, for example, and positions of traffic lights of a traffic light system, e.g. the one in 1 shown traffic lights A. with the traffic lights tp ₁ -tp ₅ include. In other words, the position of a traffic light system, and in particular its traffic lights, can be displayed / represented by means of a traffic light image.

In a step S101, traffic light information, so-called traffic light objects, n _c retrieved, for example from an existing database. Every traffic light object n _c is representative of a corresponding traffic light of the traffic lights tp ₁ -tp ₅ and therefore includes at least position data of the corresponding traffic light. The traffic light objects also include n _c also information about the traffic light phases of the corresponding traffic lights.

Every traffic light object n _c is created specifically for the crossing. Here "crossing" means that the vehicle 1 the lane section from the multitude of vehicles Fb drives on to the traffic lights A. approaches and the traffic lights A. then crossed. During such a passage c, the vehicle detects 1 by means of the detection device 3 , especially continuously, the positions and the phases of the individual traffic lights tp ₁ -tp ₅ .

Each of the traffic light objects n _c , in particular the traffic light position represented by the corresponding traffic lights tp ₁ -tp ₅ , can for example be represented as a point (traffic light object point) p _n in a global coordinate system. The entirety of all traffic light objects n _c can therefore be represented as a point cloud {p _n } with n = 1 .... N in a global coordinate system K.

For the in 2 The methods shown, the terms “traffic light objects” and “traffic light object points” can be used interchangeably, since the traffic light object points correspond to a representation of the traffic light objects in the global coordinate system K.

It is possible to display the traffic light object points in a “semi-global” coordinate system that is valid for a predetermined area around a given reference point (origin of the “semi-global” coordinate system). The predetermined area can also extend several hundred meters from the reference point in all possible directions, in particular in the east, north and above (away from the earth's surface).

The traffic light object points p _n can have an undesirably high scatter in a longitudinal direction along a course of the road section Fb exhibit. The traffic light object points p _n can therefore be pre-filtered in order to create the traffic light image. This pre-filtering is described below.

In a step S102, a course d _{traj of} the lane section is determined Fb retrieved. This is the course of the road that leads to the traffic lights A. feeds. For this purpose, movement _{history data} r _{traj of} the vehicle 1 retrieved, the at least one of the position, the position history and the orientation of the vehicle 1 represent. In other words, the course d _{traj of} the road section Fb can be determined from the movement _history data r _traj . The course d _{traj of} the roadway section can be used here Fb also representative of an orientation of the vehicle 1 be.

The course r _{traj of} the road section Fb becomes like the traffic light objects n _c also, shown in the global coordinate system K.

It is possible for step S101 to take place before, after or at the same time as step S102.

In a step S103, the traffic light object points p _{n are set} to a longitudinal direction d _{traj_intersec of} the course d _{traj of} the lane section Fb projected, the longitudinal direction d _{traj_intersec} the course d _{traj of} the road section Fb on the traffic light line. The traffic light line is the position along the course of the road section Fb at which the traffic lights A. is located. This can be, for example, that position along the course d _{traj of} the roadway section at which (in the direction of travel of the vehicle 1 ) the vehicle for the first time at a traffic light object n _c or traffic light point p _n passes. In particular, the traffic light line can therefore be determined specific to the crossing (and thus also specific to the vehicle). For the method described here, a cross-crossing traffic light line can also be formed, in that the mean of the traffic light lines is formed from each crossing. In step S103, the traffic light line is also determined / retrieved.

Since the traffic light object points p _n and the course d _{traj of} the lane section Fb can be represented in the coordinate system K (as vectors), the projection for each traffic light object p _{n takes place} according to the following formula: $$x{\text{'}}_{p_n}={\overrightarrow{p}}_n{}^T{\overrightarrow{d}}_{traj\text{\_}intersec}$$

von dem Medianwert über allen projizierten Ampelobjektpunkte {x'_{p n} } liegen Es werden also die projizierten Ampelobjektpunkte {x'_{p n} } verworfen, die folgende Bedingungen erfüllen: $$x{\text{'}}_{p_n}>\underset{n}{\text{median}}\left\{x{\text{'}}_{p_n}\right\}+2{\sigma }_{x{\text{'}}_{p_n}}$$

In a step S104, those traffic light object points p _{n are} discarded which in the longitudinal direction d _{traj_intersec are} further than two standard deviations ${\sigma }_{x{\text{'}}_{p_n}}$

of the median value over all projected traffic light object points {x ' _{p n} } lie So the projected traffic light object points {x ' _{p n} } discarded that meet the following conditions: $$x{\text{'}}_{p_n}>\underset{n}{\text{median}}\left\{x{\text{'}}_{p_n}\right\}+2{\sigma }_{x{\text{'}}_{p_n}}$$

abhängig ist. Dieser vorbestimmte Grenzwert kann bspw. ein fester (absoluter) Wert sein.In other words, traffic light object points p _n are sorted out, taking into account a scatter (in the longitudinal direction) of all traffic light object points {p _n }. Alternatively, it is also possible to provide a predetermined limit value which, in particular, does not depend on the standard deviation ${\sigma }_{x{\text{'}}_{p_n}}$

is dependent. This predetermined limit value can be, for example, a fixed (absolute) value.

The discarded traffic light object points p _n represent with a high probability traffic lights that are not on the for the lane section Fb valid traffic light line of the traffic light system A. such as pedestrian lights, traffic lights for lane sections from other directions)

Steps S102, S103 and S104 represent the above-mentioned pre-filtering of the traffic light object points p _n .

Furthermore, the traffic light objects p _n remaining after the pre-filtering are subdivided into groups by means of a clustering method. A clustering method here means a method for discovering similarity structures in (large) databases, such as the traffic light objects p _n present here. The groups of "similar" objects found by the clustering process are called clusters.

In a step S105, the traffic light object points p _{n are} projected onto an image plane E which is perpendicular to the course d _{traj of} the lane section Fb is at the level of the traffic light line. In other words, the image plane E is the plane that is perpendicular to the direction of travel of the vehicle 1 and along the course d _{traj of} the road section Fb is at the level of the traffic light line.

For the projection onto the image plane E, each traffic light object point is mapped onto a transverse direction d _{traj_intersec of} the course d _{traj of} the lane section Fb projected, the transverse direction d _{traj_intersec of} the course d _{traj of} the lane section Fb represents on the traffic light line and in a plane of the road section Fb lies. Heights of the traffic lights tp ₁ -tp ₅ are also represented by the traffic light object points p _n and can be adopted (without being projected in one direction). Accordingly, each can be projected onto the image plane E traffic light object p _'n by one as follows 2D vector of the co-ordinate system K represent: $$\overrightarrow{p}{\text{'}}_n=\left[\begin{array}{c}{\overrightarrow{p}}_n{}^T{\overrightarrow{d}}_{traj\text{\_}intersec}\\ {\overrightarrow{p}}_n{}^T\overrightarrow{k}\end{array}\right]$$

der kanonische Basisvektor des Koordinatensystems K.Here is $\overrightarrow{k}$

the canonical basis vector of the coordinate system K.

In a step S106, a (first) clustering method is carried out on the traffic light object points p ' _n projected in the direction of travel. Thus, the traffic light object points p ' _n projected in the direction of travel can be grouped in order to recognize / determine traffic light groups. The traffic light object points p ' _n are clustered with regard to their position (data).

Various clustering methods can be used here. As a rule, the well-known algorithm “Density-Based Spatial Clustering of Applications with Noise” (DBSCAN) is used. This algorithm works on a density basis and can recognize several clusters. Noise points are ignored and returned separately.

The algorithm forms clusters of so-called “densely connected” points, i.e. of points that are not further than a specified distance (neighborhood length) from a “core point” in the same cluster t. A core point is a point which is closer than the neighborhood length ε to at least a predetermined minimum number minPts of further core points in the same cluster t.

For the present method, a neighborhood length ε _{1 is} two meters and the predetermined minimum number minPts = 3. In the present case, a comparatively high neighborhood length ε ₁ = 2m is selected to avoid the comparatively high scatter in the traffic light objects p ' _n , in particular with regard to their positions (in which Coordinate system) to counteract.

Other values are also possible in order to adapt the clustering method to different circumstances.

The result of the clustering method from step S106 are clusters t with t = 1... T from the traffic light object points p ' _n projected in the direction of travel. Each of these clusters t represents a traffic light object group that is representative of a “physical” traffic light tp ₁ -tp ₅ is.

In a step S107, the traffic light objects belonging to a traffic light cluster t, that is to say a traffic light t, are referenced with the corresponding index t, so that each projected traffic light object point has p ' _{n c, t} lets specify. The projected traffic light object point p ' _{n c, t} can therefore be assigned to corresponding traffic light clusters t and corresponding crossings c.

It should be noted that a traffic light group t can comprise more than one traffic light object (and thus a traffic light position) which can originate or originate in the same crossing c. The reason for this is that double detection by the detection device 3 is possible, whereby these traffic light objects are at least partially time-overlapping or not time-overlapping.

In a step S108, the results of the first clustering method are post-filtered.

For this purpose, traffic light object points p ' _n that cannot be assigned to any traffic light group t and thus represent noise are discarded.

Alternatively or additionally, in step S108, traffic light clusters t with a number of traffic light object points p ' _n which is smaller than a predetermined minimum number, for example one fifth of the number of the second largest traffic light cluster, are discarded. The traffic light object points p ' _{n of} the relevant traffic light clusters t are also discarded. This ensures that no traffic light clusters t are taken into account for the further method which are formed from scattered traffic light object points p ' _n which do not correspond to any physical traffic light t.

As an alternative or in addition, a main component analysis is carried out for each traffic light cluster t in step S108.

Due to the comparatively high first neighborhood length ε ₁ = 2m, it can happen that two closely adjacent traffic lights t are combined into a cluster t by the clustering process. As a result, the two closely adjacent traffic lights t are not recognized as such, but are only represented by a traffic light cluster t and thus only as a traffic light t. Such a traffic light cluster t can be determined by means of the principal component analysis.

Principal component analysis is a method of multivariate statistics. It is used to structure, simplify and visualize extensive data sets by approximating a large number of statistical variables with a smaller number of linear combinations (main components) that are as meaningful as possible.

For the main component analysis of a traffic light cluster t, all traffic light object points p ' _n associated with the corresponding traffic light cluster t are determined _t determined. The following formula can be used for this: $$\left\{p{\text{'}}_{n_t}\right\}={\displaystyle \underset{c=1}{\overset{\mathrm{C.}}{\cup }}\left\{p{\text{'}}_{n_{c,t}}\right\}}$$

In the above formula, c stands for the corresponding crossing from c = 1 ... C.

From this traffic light object point cloud {p ' _{n t} } The main components (main directions) and the corresponding (maximum) eigenvalues λ _{max t} determined. An eigenvalue λ _{max t} corresponds to the length of a corresponding main component. The principal component analysis is used to determine how far a traffic light cluster t is “pulled apart”.

This allows those traffic light clusters to be identified as “double clusters” whose main component is longer than a predetermined value. In other words, double clusters exist when eigenvalues λ _{max t} of the double cluster exceed a predetermined eigenvalue limit value.

In the present case, the predetermined eigenvalue limit value can be twice the median value of all eigenvalues λ _{max t} be over all traffic light clusters t. To identify the double clusters, the following condition can apply: $${\lambda }_{ma{x}_t}>2\underset{t}{median}\left\{{\lambda }_{ma{x}_t}\right\}$$

It is also possible to use other predetermined eigenvalue limit values. For example, the predetermined eigenvalue limit value can be another factor of the median value of the eigenvalues λ _{max t} over all traffic light clusters than the above two-fold factor.

The clustering process (second clustering process) is applied again to the identified double clusters. In particular, the DBSCAN algorithm is used here again, but with a neighborhood length ε ₂ that corresponds to half of the first neighborhood length ε _{1 of} the first clustering method. In the present case, the neighborhood length is ε ₂ = 1m. The neighborhood length ε _{2 of} the second clustering method can also have a different value as long as it is lower than the first Neighborhood length ε ₁ . It is important that the second clustering process again detects “sub-clusters” within the double clusters.

The resulting “sub-clusters” are treated as traffic light clusters t. From the DBSCAN algorithm with the neighborhood length ε _{2 of} the second clustering method, traffic light object points {p ' _n } that cannot be assigned to a “sub-cluster” are discarded.

In a step S109, all crossing-specific data records that have less than a predetermined minimum number of traffic light objects after the above steps are discarded. It is therefore checked whether, after the previous steps, it can happen that only a predetermined minimum number of traffic light objects is still present from a passage c.

Here, this predetermined minimum number is two. In other versions, a different predetermined minimum number of traffic light objects is also possible. What is decisive is that this minimum number is selected in such a way that it is ensured that a reliable assignment of the traffic light object points p ' _{n t} to the traffic light clusters t can take place.

If a traffic light cluster t no longer has any traffic light objects as a result of step S109, these “empty” traffic light clusters are likewise discarded.

In a step S110, the traffic light image 7th created. For this purpose, a spatial mean value is formed from each of the remaining traffic light clusters t, which then represents a position of the physical traffic light t, which is represented by the corresponding traffic light cluster t. The traffic light object points p ' _n remaining from the previous steps S101 to 108 _t are the physical traffic lights tp ₁ -tp ₅ assignable.

A result of the previously described method is shown in FIG 3 shown. Here is the traffic light picture 7th shown that the traffic lights A. out 1 represents. The traffic light objects n _{c, t} from all crossings c are as (cross-shaped) point cloud {p ' _{n t} } shown. It can be seen that in the traffic light image 7th the traffic light objects n _{c, t} in four clusters t = 1 to t = 4 (cluster t ₁ -t ₄ ) are grouped. Each of the clusters corresponds to one of the traffic lights tp ₁ to tp ₄ . The hatched diamonds represent centers of the clusters t (spatial mean values), which each represent a spatial traffic light position of the corresponding physical traffic light.

The traffic light objects for the traffic light tp ₅ are recorded in the cluster for traffic lights tp ₄ assigned. This is because the traffic light tp ₅ (as a right-turn traffic light) very close to or even adjacent to the traffic light tp ₄ is arranged. In the 3 The horizontal axis (x-axis) shown represents a direction transverse to the road section and the vertical axis (y-axis) a direction perpendicular to the earth's surface towards the sky. 3 corresponds to a view from the vehicle, so to speak 1 on the traffic lights A. . The axis values are given in meters. For example, the traffic light cluster t _{2 is} approx. 4 m to the left and 7 m above the vehicle 1 .

In the 4th is an example and schematic of another traffic light image 7 ' the traffic lights A. shown for further explanation. In the traffic light picture 7 ' is a multitude of traffic light objects n _{c, t} shown representative of the traffic lights tp ₁ -tp ₅ the traffic lights A. are.

As described above, the traffic light objects n _{c, t} recorded specific to the crossing. Usually "crossing" means that the vehicle 1 the lane section from the multitude of vehicles Fb drives on to the traffic lights A. approaches and then crosses it. During a passage c, the vehicle is recorded, in particular continuously 1 the positions and the phases of the individual traffic lights tp ₁ -tp ₅ . Using the in 2 The procedures shown are from the totality of the traffic light image 7 ' included traffic light objects n _{c, t} Traffic light cluster t ₁ , t ₂ , t ₃ , t ₄ formed, the traffic light clusters t ₁ -t ₄ the physical traffic lights tp ₁ -tp ₅ the traffic lights A. correspond.

So can be seen from the traffic light image 7 ' determine that in this example the traffic light objects n _{c, t} can be created from four passes c = 1 ... 4. It should be mentioned that these four crossings c = 1 ... 4 not only by the same vehicle 1 , but also may have been carried out by different vehicles from the wide variety of vehicles. In the 4th are the traffic light objects n _{c, t} shown in different shapes, each shape standing for a corresponding passage c. It can be seen that from a crossing c = 1 for the traffic light tp ₁ and the traffic light tp ₃ one traffic light object n _1.1 or n _1.3 recorded and the traffic light clusters t ₁ , t ₃ be assigned. For the same crossing c = 1 for the traffic light tp ₂ and the traffic light tp ₄ two traffic light objects n _1,2 or n _1,4 recorded and the traffic light cluster t ₂ , t ₄ assigned.

The fact that more than one traffic light object is generated for a traffic light during a passage may be due to the fact that the detection device 3 of the corresponding vehicle, the corresponding traffic light was incorrectly recorded and several traffic light objects were created for this traffic light and assigned to it. It is also possible that during the acquisition by the acquisition device 3 a line of sight between the sensing device 3 and the corresponding traffic light is interrupted by an obstacle, so that when the obstacle disappears, the corresponding traffic light is detected by the detection device 3 is recorded again and therefore a new traffic light object is generated for it and assigned to this traffic light.

It should be noted that the in 4th shown different shapes of the traffic light objects n _{c, t} serve only for clarification and the disclosure is not limited to the fact that the traffic light objects n _{c, t} in a special form in the traffic light image 7 ' being represented.

In 5 is a method of grouping traffic lights tp ₁ -tp ₅ shown. The groups are formed in such a way that each traffic light within a (traffic light) group has the same phase behavior or a similar phase progression.

In a step S201, the traffic light image 7th or the traffic light objects n _{c, t} retrieved that or the traffic lights A. represents. As described above, the traffic light image 7th the traffic light objects detected over several crossings c n _{c, t} for every traffic light tp ₁ -tp ₅ the traffic lights A. on. That in the 5 The method shown does not specifically require the traffic light image 7th the traffic lights A. (as input data set). Traffic light images representing other traffic light systems can also be used.

Inconsistent traffic lights are identified in a step S202. It should be noted that the physical traffic lights tp ₁ -tp ₅ by corresponding traffic light clusters t ₁ -t ₄ in the traffic light image 7th be represented.

A traffic light is inconsistent if the phases of individual traffic light objects assigned to the same traffic light appear to be inconsistent with one another. This usually indicates that there is a (right or left) turning light at the traffic light concerned, which only lights up occasionally. These turning lights are usually so close to the traffic lights concerned that the in 2 The method shown incorrectly assigns the traffic light object representing the turning traffic light to the traffic light concerned. This situation is, for example, in the 1 shown traffic lights A. in front. Here is the traffic light tp ₅ close to the traffic lights tp ₄ arranged so that the in 2 Process shown no two traffic light clusters for the traffic lights tp ₄ and tp ₅ recognizes, but only the traffic light cluster t ₄ . In other words, the two traffic lights tp ₄ and tp ₅ are through the in 2 The procedures shown are not kept apart.

In the exemplary traffic light picture 7 ' the traffic light object n _2.4 and the traffic light object n _{2.5 of} the traffic lights tp ₄ or. tp ₅ so close together that they are the same traffic light cluster t ₄ be assigned. Hence this is in 4th shown traffic light object n _2.5 (the traffic light object created for a crossing c = 2 for the traffic light t ₅ ) in the traffic light cluster t ₄ .

To counteract this circumstance of inconsistent traffic lights or traffic light clusters, for each traffic light cluster t ₁ to t ₄ all possible traffic light object pair combinations are determined for each crossing c and a correlation factor ρ _{n is} determined for each of the determined traffic light object pair combinations _{c, t , n ' c, t} certainly. The correlation factors ρ _{n c, t , n ' c, t} (ρ _obj ) are formed as follows: $${\rho }_{n_{c,t},n{\text{'}}_{c,t}}=\frac{T{m}_{n_{c,t},n{\text{'}}_{c,t}}}{T{t}_{n_{c,t},n{\text{'}}_{c,t}}}$$

Here is n _{c, t} a first traffic light object of the traffic light object pair and n'c _{, t} a second traffic light object of the traffic light object pair. Tm _{n c, t , n ' c, t} and Tt _{n c, t , n ' c, t} stand for the duration of the overlap or the period of correspondence between the first traffic light object n _{c, t} and the second traffic light object n ' _{c, t of} the pair of traffic light objects.

All traffic light objects assigned to a crossing c are determined taking into account the corresponding correlation factors ρ _{n c, t , n ' c, t} clustered. In the present case, this is usually done using a hierarchical cluster algorithm. The resulting clusters are counted. Then for each traffic light cluster t ₁ to t ₄ the mean of the number of resulting clusters is determined over all crossings c. If this determined mean value exceeds a predetermined limit value, the traffic light cluster is assessed as inconsistent. The predetermined limit value can be 1.2, for example.

In the present example of the traffic light image 7th the traffic light cluster t _{4 represents} an inconsistent traffic light cluster.

In a step S203, the phase profiles of all traffic light objects n _c , especially those of the traffic light clusters assessed as consistent, checked for plausibility. Here are those traffic light objects n _c discarded for the following steps of the process, the phases of which have the following sequences of traffic light phases within their phase: yellow phase to red phase, red-yellow phase to green phase, green phase or red-yellow phase to a yellow Phase, red phase or a yellow phase to a red-yellow phase. Implausible traffic light objects n _c are discarded

In a step S204, all traffic light pair combinations are formed from the consistent traffic light clusters t ₁ , t ₂ , t ₃ . In the present case, the traffic light pair combinations are therefore used t ₁ / t ₂ , t ₁ / t ₃ , t ₂ / t ₃ educated.

Subsequently, in step S205, an overlap duration Tt and a coincidence duration Tm between all traffic light objects are calculated across the crossing n _{c, t} assigned to the first traffic light cluster of the traffic light pair and all traffic light objects n _{c, t} , which are assigned to the second traffic light cluster of the traffic light pair, summed up and from this determine a correlation factor ρ _AP between the first and the second traffic light of the traffic light pair. In reference to 7th the terms “overlap duration” and “match duration” are described in more detail later.

Step S205 is exemplary for the pair of traffic lights t ₁ / t ₂ described. For the calculation of the overlap duration Tt _{t 1 , t 2} (Tt _AP ) for the pair of traffic lights from the traffic light cluster t ₁ and traffic light clusters t ₂ the following formula applies: $$\mathrm{<figure-callout\; id="1"\; label="T\; t\; t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">T</figure-callout>}{t}_{t_{}}{{}_1,{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="DE102019211102A1\_0057.png,DE102019211102A1\_0058.png"\; state="\{\{state\}\}">t</figure-callout>}}_2}_{}={\displaystyle \sum _{c=1}^{\mathrm{C.}}{\displaystyle \sum _{n_{c,{\mathrm{<figure-callout\; id="1"\; label="t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">t</figure-callout>}}_1}=1}^{N_{c,{\mathrm{<figure-callout\; id="1"\; label="t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">t</figure-callout>}}_1}}{\displaystyle \sum _{n_{c,{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="DE102019211102A1\_0057.png,DE102019211102A1\_0058.png"\; state="\{\{state\}\}">t</figure-callout>}}_2}=1}^{N_{c,t_2}}T{t}_{n_{c,{\mathrm{<figure-callout\; id="1"\; label="t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">t</figure-callout>}}_1},n_{c,{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="DE102019211102A1\_0057.png,DE102019211102A1\_0058.png"\; state="\{\{state\}\}">t</figure-callout>}}_2}}}}}$$

Here, c stands for the respective passage from c = 1 ... C and n _{c, t 1} and n _{c, t 2} for the respective traffic light object of the traffic light cluster t ₁ or traffic light clusters t ₂ , where N _{c, t 1} and N _{c, t 2} the number of traffic light objects n _{c, t} represents that from a crossing c for the corresponding traffic light tp ₁ or traffic light tp ₂ created / recorded.

(Tt_n) steht für die überfahrtspezifische Überlappungsdauer zwischen einem dem Ampelcluster t₁ zugehörigen Ampelobjekt n_{c,t 1} und einem dem Ampelcluster t₂ zugehörigen Ampelobjekt n_{c,t 2} . $T{t}_{n_{c,{\mathrm{<figure-callout\; id="1"\; label="t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">t</figure-callout>}}_1},n_{c,{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="DE102019211102A1\_0057.png,DE102019211102A1\_0058.png"\; state="\{\{state\}\}">t</figure-callout>}}_2}}$

(Tt _n ) stands for the crossing-specific overlap duration between a traffic light object n _{c, t} belonging to the traffic light cluster t _{1 1} and a traffic light object n _{c, t} belonging to the traffic light cluster t _{2 2} .

For example, from the exemplary traffic light picture 7 ' out 4th it can be determined that for the crossing c = 1 for the first traffic light tp ₁ only one (circular) traffic light object n _1,1 is present and for the second traffic light tp ₂ two (circular) traffic light objects n _1,2 . For the second crossing c = 2, there are two (triangular) traffic light objects n _2.1 for the first traffic light tp ₁ , while only one (triangular) traffic light object n _2.2 is present for the second traffic light tp ₂ . For the crossing c = 3 is for both traffic lights t ₁ , t ₂ a (star-shaped) traffic light object n _3.1 , n _3.2 each.

For the calculation of the period of conformity Tm _{t 1 , t 2} (Tm _AP ) the following formula applies to the pair of traffic lights made up of traffic light cluster t ₁ and traffic light cluster t ₂ : $$\mathrm{<figure-callout\; id="1"\; label="T\; m\; t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">T</figure-callout>}{m}_{t_{}}{{}_1,{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="DE102019211102A1\_0057.png,DE102019211102A1\_0058.png"\; state="\{\{state\}\}">t</figure-callout>}}_2}_{}={\displaystyle \sum _{c=1}^{\mathrm{C.}}{\displaystyle \sum _{n_{c,{\mathrm{<figure-callout\; id="1"\; label="t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">t</figure-callout>}}_1}=1}^{N_{c,{\mathrm{<figure-callout\; id="1"\; label="t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">t</figure-callout>}}_1}}{\displaystyle \sum _{n_{c,{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="DE102019211102A1\_0057.png,DE102019211102A1\_0058.png"\; state="\{\{state\}\}">t</figure-callout>}}_2}=1}^{N_{c,t_2}}T{m}_{n_{c,{\mathrm{<figure-callout\; id="1"\; label="t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">t</figure-callout>}}_1},n_{c,{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="DE102019211102A1\_0057.png,DE102019211102A1\_0058.png"\; state="\{\{state\}\}">t</figure-callout>}}_2}}}}}$$

(Tm_n) steht für die überfahrtspezifische Übereinstimmungsdauer zwischen einem dem Ampelcluster t₁ zugehörigen Ampelobjekt n_{c,t 1} und einem dem Ampelcluster t₂ zugehörigen Ampelobjekt n_{c,t 2} . $T{m}_{n_{c,{\mathrm{<figure-callout\; id="1"\; label="t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">t</figure-callout>}}_1},n_{c,{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="DE102019211102A1\_0057.png,DE102019211102A1\_0058.png"\; state="\{\{state\}\}">t</figure-callout>}}_2}}$

(Tm _n ) stands for the crossing-specific period of agreement between one of the traffic light clusters t ₁ associated traffic light object n _{c, t 1} and one of the traffic light cluster t ₂ associated traffic light object n _{c, t 2} .

A correlation factor ρ _t can then then be used ₁ , _{t 2} (generally ρ _AP ) for the pair of traffic lights t ₁ / t ₂ can be determined according to the following: $${\mathrm{<figure-callout\; id="1"\; label="\rho \; t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">\rho </figure-callout>}}_{t_{}}=\frac{\mathrm{<figure-callout\; id="1"\; label="T\; m\; t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">T</figure-callout>}{m}_{t_{}}{{}_1,t_2}_{}}{\mathrm{<figure-callout\; id="1"\; label="T\; t\; t"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">T</figure-callout>}{t}_{t_{}}{{}_1,{\mathrm{<figure-callout\; id="2"\; label="t"\; filenames="DE102019211102A1\_0057.png,DE102019211102A1\_0058.png"\; state="\{\{state\}\}">t</figure-callout>}}_2}_{}}$$

In step S206, all consistent traffic light clusters are grouped based on the correlation factors of all traffic light pair combinations. The traffic lights are grouped in such a way that all traffic lights within a group have the same phase sequence. The grouping takes place via appropriate clustering methods, such as the hierarchical clustering algorithm. The resulting groups, so-called traffic light groups, are referenced with an index g = 1 ... G. Each consistent traffic light cluster is assigned to one of these traffic light groups.

In step S207, the traffic light clusters t * recognized as inconsistent are assigned to a traffic light group g. An overlap period Tt _n is used for each pass c _{c, t *, g} and a match duration Tm _{n c, t *, g} for a traffic light object n _{c, t *} in an inconsistent traffic light cluster t * with regard to all traffic light objects n _{c, t} , determined in a traffic light group g.

The crossing-specific overlap duration Tt _{n c, t *, g} between the traffic light object n _{c, t *} and all traffic light objects n _{c, t} , determined from traffic light group g as follows: $$T{t}_{n_{c,t*},G}={\displaystyle \sum _{t_G=1}^{T_G}{\displaystyle \sum _{n_{c,t_G}=1}^{N_{c,t_G}}T{t}_{n_{c,t*},n_{c,t_G}}}}$$

steht für die überfahrtspezifische Überlappungsdauer zwischen einem der inkonsistenten Ampel t* zugehörigen Ampelobjekt n_c,t* und einem dem Ampelcluster t_g zugehörigen Ampelobjekt n_{c,t g} .Here, T _g stands for the number of traffic light clusters in the respective traffic light group g. Furthermore stands n _{c, t} , for the respective traffic light object assigned to the traffic light cluster t _g , where N _{c, t G} represents the number of traffic light objects within a crossing c for the corresponding traffic light cluster t _g . $T{t}_{n_{c,t*},n_{c,t_G}}$

represents the crossing specific overlap duration between one of the inconsistent traffic signal t * corresponding traffic light object n _{c t *} and a traffic light, the traffic light associated cluster t _g object n _{c, t G} .

The crossing-specific overlap duration Tm _{n c, t *, g} per pass c is determined as follows: $$T{m}_{n_{c,t*},G}={\displaystyle \sum _{t_G=1}^{T_G}{\displaystyle \sum _{n_{c,t_G}=1}^{N_{c,t_G}}T{m}_{n_{c,t*},n_{c,t_G}}}}$$

steht für die überfahrtspezifische Übereinstimmungsdauer zwischen einem dem inkonsistenten Ampelcluster t* zugehörigen Ampelobjekt n_c,t* und allen der Ampelgruppe g zugehörigen Ampelobjekten n_{c,t g} . $T{m}_{n_{c,t*},n_{c,t_G}}$

stands for the crossing-specific period of correspondence between a traffic light object n _c, t * belonging to the inconsistent traffic light cluster t _* and all traffic light objects n _{c, t} belonging to the traffic light group g _G .

Then, for each inconsistent traffic light t * and each traffic light group g formed, a cross-crossing record Γ _{g, t *} with all traffic light objects n _{c, t * of} the inconsistent traffic light t * is created according to the following formula: $${\Gamma }_{G,t*}={\displaystyle \underset{c=1}{\overset{\mathrm{C.}}{\cup }}\left\{n_{c,t*}|T{t}_{n_{c,t*},G}>0\wedge \frac{T{m}_{n_{c,t*},G}}{T{t}_{n_{c,t*},G}}>0.99\right\}}$$

von 0.99 aufweisen. Der Wert des Mindestkorrelationsfaktors ist hier beispielhaft und kann angepasst werden.It can thus be seen that the traffic light objects of the inconsistent traffic light _{t *} belonging to the set Γ _{g, t * have} a minimum correlation factor for the traffic light objects n _{c, g of} the traffic light group g $\frac{T{m}_{n_{c,t*},G}}{T{t}_{n_{c,t*},G}}$

of 0.99. The value of the minimum correlation factor is an example here and can be adjusted.

$$T{m}_{t*,g}={\displaystyle \sum _{c=1}^C{\displaystyle \sum _{n_{c,t*}=1}^{N_{c,t*}}T{m}_{n_{c,t*},g}}}$$

mit n_c,t* ∈ Γ_g,t* For the traffic light objects from the set Γ _{g, t *} , an overlap period Tt _{t *, g} and a correspondence period Tm _{t *, g are determined} according to the following formulas, across crossing and traffic light objects. $$T{t}_{t*,G}={\displaystyle \sum _{c=1}^{\mathrm{C.}}{\displaystyle \sum _{n_{c,t*}=1}^{N_{c,t*}}T{t}_{n_{c,t*},G}}}$$

$$T{m}_{t*,G}={\displaystyle \sum _{c=1}^{\mathrm{C.}}{\displaystyle \sum _{n_{c,t*}=1}^{N_{c,t*}}T{m}_{n_{c,t*},G}}}$$

with n _{c, t *} ∈ Γ _{g, t *}

In the above two formulas, n _{c, t *} ∈ Γ _{g, t *} also applies. Thus, only traffic light objects n _{c, t * of} the inconsistent traffic light t * which have or exceed the above-mentioned minimum correlation factor for traffic light group g are added up.

ermittelt, so dass die Anzahl der Ampelobjekte Γ_g,t* der inkonsistenten Ampel t* am höchsten ist, wobei die Ampelobjekte n_c,t* ∈ Γ_g,t* mit der Phase der entsprechenden Gruppe $g_{t*}^{*}$

einen hohen Korrelationsfaktor $\left(\frac{T{m}_{n_{c,t*},g}}{T{t}_{n_{c,t*},g}}>\mathrm{0,99}\right)$

aufweisen.For each inconsistent traffic light t * there is a corresponding traffic light group $G_{t*}^{*}$

determined so that the number of traffic light objects Γ _{g, t * of} the inconsistent traffic light t * is highest, with the traffic light objects n _{c, t *} ∈ Γ _{g, t *} with the phase of the corresponding group $G_{t*}^{*}$

a high correlation factor $\left(\frac{T{m}_{n_{c,t*},G}}{T{t}_{n_{c,t*},G}}>0.99\right)$

exhibit.

The following formula is used for this: $$G_{t*}^{*}=\underset{G}{\text{argmax}}\left\{\left|{\Gamma }_{G,t*}\right|\right\}$$

zugeordnet, wenn die Anzahl der korrelierten Ampelobjekte $\left|{\Gamma }_{g_{t*}^{*},t*}\right|$

aus den Ampelobjekten n_c,t* der inkonsistenten Ampel t* und Ampelobjekten $n_{c,g_{t*}^{*}}$

der entsprechenden Ampelgruppe $g_{t*}^{*}$

gleich der oder größer als ein vorbestimmter Bruchteil aller (korrelierten und nicht-korrelierten) Ampelobjekte n_c,t* der inkonsistenten Ampel t* ist. Dabei kann der vorbestimmte Bruchteil bspw. ein Drittel sein.The inconsistent traffic light t * becomes this traffic light group $G_{t*}^{*}$

assigned when the number of correlated traffic light objects $\left|{\Gamma }_{G_{t*}^{*},t*}\right|$

from the traffic light objects n _{c, t * of} the inconsistent traffic light t * and traffic light objects $n_{c,G_{t*}^{*}}$

the corresponding traffic light group $G_{t*}^{*}$

equal to or greater than a predetermined fraction of all (correlated and non-correlated) traffic light objects n _{c, t * of} the inconsistent traffic light t *. The predetermined fraction can be, for example, a third.

zugeordnet. Die übrigen (nicht-korrelierten) Ampelobjekte n_c,t* ∉ Γ_g,t* entsprechen mit hoher Wahrscheinlichkeit (Phasen-)Sichtungen der Abbiegerampel t₅, die nicht weiter interpretierbar sind.Only the correlated traffic light objects n _{c, t *} ∈ Γ _{g, t * of} the inconsistent traffic light t * of the traffic light group are used $G_{t*}^{*}$

assigned. The remaining (non-correlated) traffic light objects n _{c, t *} Γ _{g, t *} correspond with a high probability to (phase) sightings of the turning traffic light t ₅ , which cannot be further interpreted.

In 6 is a method for determining a traffic light phase of a traffic light or a traffic light group of a traffic light system. The following is the procedure for a traffic light from the traffic lights tp ₁ -tp ₅ or a traffic light group from traffic light groups g is carried out.

For example, the information represented by the traffic light objects can be used for the method. In alternatives, the information required for the method can also be provided / represented in a different form than traffic light objects.

In particular, the method is carried out when the vehicle 1 just before or at the stop line of the traffic lights A. (corresponds to the stop line at an intersection). The stop line can be known, for example from map information. Alternatively, the stop line can be used as a median value over all positions along the course of the road section (or along the trajectory of the vehicle 1 ) can be determined at which the vehicle 1 drives past the traffic light objects.

In any case, the vehicle is there 1 that is, in a position in which the vehicle-side detection device 3 the traffic lights A. and can no longer see the traffic lights. This is primarily due to a limited opening angle of a camera device of the detection device 3 . However, the point in time when driving over or stopping at the stop line is relevant for a traffic light lane allocation.

In a step S301, a point in time T _event at which the vehicle 1 either crosses the stop line or stands at it. This can be seen from the vehicle's detection system 3 derive and especially in combination with information from the traffic light objects n _{c, t} .

When the vehicle 1 Passing this stop line at a speed greater than a predetermined speed, for example 3 km / h, is interpreted as crossing the stop line. When the vehicle 1 If the speed drops below 1 km / h for the first time at the stop line, this is interpreted as a stop at the stop line. “At the stop line” includes a predetermined distance from the vehicle 1 (in the direction of travel) to the stop line of up to at least 25m, preferably 15m. Here, the distance from a front end of the vehicle 1 be meant up to the stop line. Other predetermined distances are also conceivable, provided they are suitable for indicating that the vehicle is stopping at the stop line.

In a step S302, a first detection time T _{i is} retrieved at which the vehicle 1 a traffic light is detected for the first time.

A time interval ΔT _i between the time T _{i of} the first sighting of the traffic light and the event time T _event can be determined.

In a step S303, a last detection time T _{e is} retrieved, at which the vehicle detects the traffic light for the last time.

A time interval ΔT _e between time T _{e of} the last sighting of the traffic light and the event time T _event can be determined. The time interval ΔT _e is set to 0 if the traffic light is still visible at the event time T _event .

In a step S304, a phase profile of the traffic light is called up, which extends from the first detection time T _i to the last detection time T _e . A traffic light phase C _{g of} the traffic light, which is present at time T _i , can be determined from the retrieved phase profile. As a rule, the traffic light phase C _{g can have} at least the following values: green, yellow, red as well as red and yellow. The following traffic light phases C _g can therefore occur: red phase, yellow phase, red phase, green phase and red-yellow phase

In the event that a traffic light phase C _{g of} a traffic light group is called up, traffic lights of this traffic light group may assume / reproduce contradicting traffic light phases instead of a common traffic light phase. Then the traffic light phase C _{g is} set to “invalid” for this traffic light group.

In a step S305, a phase change point in time T _{switch is} determined or retrieved at which a phase change of the traffic light takes place or was detected. In other words, the traffic light phase C _{g of} the traffic light changes at the time T _switch . The phase change time T _switch is before the last acquisition time T _e .

A time interval ΔT _switch between the time T _{switch of} the phase change of the traffic light and the event time T _event can be determined.

If there is no phase change or if there are contradictions between two or more traffic lights in a traffic light group, the time T _switch and thus also the time interval Δt _{switch are set} to "invalid". If there is no phase change in the retrieved phase profile, the phase change time (as described later) can be determined.

In a step S306, a traffic light phase C ^{Δ- is determined} before the phase change time T _{switch of} the traffic light and a traffic light phase C ^{Δ +} after the time T _{switch of} the phase change of the traffic light. The traffic light phases C ^Δ- and C ^{Δ +} thus indicate those traffic light phases that exist before or after the point in time T _switch . The traffic light phases C ^Δ- and C ^{Δ +} can assume the traffic light phases described in step S304.

In a step S307 it is determined which traffic light phase C _{g is present} at the event time T _event . In particular, a probability P (C ₉ = red) is determined that the traffic light has a traffic light phase of “red” at time T _event . In other words, it is determined with what probability the traffic light is red (or indicates a red traffic light phase) when the vehicle 1 stops at the traffic lights or drives past them. The following applies to the probability P (C _g = red): $$P\left({\mathrm{C.}}_G=rOt\right)=P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{t}_e,\mathrm{\Delta }{t}_i,{\mathrm{C.}}_G=c,\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{t}_{switcH},{\text{C.}}^{\mathrm{\Delta }+}={\text{c}}^{\mathrm{\Delta }+},{\text{C.}}^{\mathrm{\Delta }-}={\text{c}}^{\mathrm{\Delta }-}\right)$$

The probability P (C ₉ = red) is therefore a conditional probability that takes into account the quantities ΔT _e , ΔT _i , C _g , ΔT _switch , C ^Δ- and C ^{Δ +} .

The following applies to the probability P (C _g = green) that the traffic light has a traffic light phase C _g of "green" at time T _event : $$\begin{array}{l}P\left({\mathrm{C.}}_G=Grün\right)=\\ =P\left({\mathrm{C.}}_G=Grün|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{t}_e,\mathrm{\Delta }{T}_i\mathrm{=\; \Delta }{t}_i,{\mathrm{C.}}_G=c,\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{t}_{switcH},{\text{C.}}^{\mathrm{\Delta }+}={\text{c}}^{\mathrm{\Delta }+},{\text{C.}}^{\mathrm{\Delta }-}={\text{c}}^{\mathrm{\Delta }-}\right)=\\ =1-P\left({\mathrm{C.}}_G=rOt\right)\end{array}$$

The probability P (C _g = green) is also a conditional probability that takes into account the variables ΔT _i , ΔT _e , C _g , ΔT _switch , C ^Δ- and C ^{Δ +} .

To calculate the probabilities P (C _g = red) or P (C _g = green) there can be three cases I, II, III. These cases depend on the time intervals ΔT _e and / or ΔT _switch .

In case I, ΔT _e = 0s. The detection device 3 has still seen the traffic light at time T _event . Accordingly, the traffic light phase C _{g of} the traffic light is known, so that the following applies to the probability P (C _g = red): $$P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_e=\mathrm{0,}{\mathrm{C.}}_G=c\right)=\{\begin{array}{ll}\mathrm{1,}\hfill & c=rO\mathrm{<figure-callout\; id="0"\; label="t"\; filenames="DE102019211102A1\_0057.png,DE102019211102A1\_0058.png"\; state="\{\{state\}\}">t</figure-callout>}\hfill \\ \mathrm{0,}\hfill & c=Grün\hfill \\ \mathrm{0.8,}\hfill & c=Gelb\hfill \\ \mathrm{0.7,}\hfill & c=Gelb+rOt\hfill \end{array}$$

The probability P (C _g = red) of 0.8 for the traffic light phase c = yellow allows a yellow traffic light to be interpreted as green or red, this value of 0.8 being dependent on the driver. The same applies to the probability P (C _g = red) of 0.7 for c = yellow + red. The probabilities for c = yellow and c = yellow + red are exemplary here. In alternatives, these values can be determined specifically for the driver. In other words, the probabilities for c = yellow and c = yellow + red can be determined / ascertained as a function of a driver's driving behavior.

In case II, the time interval ΔT _{e is} greater than 0s and the time interval ΔT _switch has a valid value (and thus a value greater than 0s). The traffic light phase C _g at time T _event can thus be derived from the most recently recorded phase change at time T _switch .

For this purpose, the phase durations of the traffic light phases, which are usually unknown, are taken into account by modeling the phase durations as random variables. A reference traffic light cycle is modeled. In the model, T _cycle stands for a circulation time of the reference _{traffic light cycle} . A cycle time corresponds to the time a traffic light needs to go through a complete switching process. For example, a complete switching process can be a traffic light phase sequence with the traffic light phases in the order “green”, “yellow”, “red” and “yellow and red”.

The following applies to the cycle time T _cycle : $$T_{cycle}\sim U\left(t_{cycle\_min,}{t}_{cycle\_max}\right)$$

Here t _{cycle_min is} a minimum cycle time and t _{cycle_max is} the maximum cycle time.

For the reference _{traffic light cycle} , it is assumed that a uniform distribution of the cycle time in the above value range [t _{cycle_min} ; t _{cycle_max} ] is present.
For example, t _{cycle_min} can be 30s and t _{cycle_max} 120s. These values come from the guideline for traffic light systems, which is a set of rules applicable in Germany. For other countries, other values can be specified accordingly.

For a phase duration X _red , in which the reference traffic light cycle shows _red , the following applies: $$X_{rOt}\sim U\left(rO{t}_{min},rO{t}_{max}\right)$$

Here, too, it is assumed for the reference traffic light cycle that the phase duration X _{rot is} in the value range [rot _min ; rot _max ] is evenly distributed. As a rule, red _min and red _max indicate the minimum or maximum fraction of the cycle time T _cycle at which the reference _{traffic light cycle} shows “red”. For example, red _min = 0.3 and red _max = 0.7. Alternatively, the range of values for the phase duration X _{red can} also be specified by absolute minimum and maximum times in seconds.

In case II the following applies to the probability P (C _g = red): $$\begin{array}{l}P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{T}_{switcH},{\mathrm{C.}}^{\mathrm{\Delta }+}=c^{\mathrm{\Delta }+},T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)=\\ =\{\begin{array}{ll}min\left\{\frac{\mathrm{\Delta }{t}_{switcH}^{\text{'}}}{t_{trans}}\mathrm{,1}\right\},\hfill & c^{\mathrm{\Delta }+}=rOt\vee Gelb,\mathrm{\Delta }{t}_{switcH}^{\text{'}}<x_{rOt}*t_{cycle}\hfill \\ \mathrm{0,}\hfill & c^{\mathrm{\Delta }+}=rOt\vee Gelb,\mathrm{\Delta }{t}_{switcH}^{\text{'}}\ge x_{rOt}*t_{cycle}\hfill \\ \mathrm{0,}\hfill & c^{\mathrm{\Delta }+}=Grün\vee \left(rOt+Gelb\right),\mathrm{\Delta }{t}_{switcH}^{\text{'}}<\left(1-x_{rOt}\right)*t_{cycle}\hfill \\ min\left\{\frac{\mathrm{\Delta }{t}_{switcH}^{\text{'}}-\left(1-x_{rOt}\right)*t_{cycle}}{t_{trans}}\mathrm{,1}\right\},\hfill & c^{\mathrm{\Delta }+}=Grün\vee \left(rOt+Gelb\right),\mathrm{\Delta }{t}_{switcH}^{\text{'}}\ge \left(1-x_{rOt}\right)*t_{cycle}\hfill \end{array}\end{array}$$

das zykluskorrigierte Zeitintervall, das seit dem letzten Phasenwechsel verlaufen ist. Für $\mathrm{\Delta }{t}_{switch}^{\text{'}}$

gilt: $$\mathrm{\Delta }{t}_{switch}^{\text{'}}=\mathrm{\Delta }{t}_{switch}mod{t}_{cycle}$$

Here is $\mathrm{\Delta }{t}_{switcH}^{\text{'}}$

the cycle-corrected time interval that has elapsed since the last phase change. For $\mathrm{\Delta }{t}_{switcH}^{\text{'}}$

applies: $$\mathrm{\Delta }{t}_{switcH}^{\text{'}}=\mathrm{\Delta }{t}_{switcH}mOd{t}_{cycle}$$

verwendet, um den Fall abzufangen, dass mehr als eine komplette Umlaufzeit der Ampel nach dem Zeitpunkt T_e verstrichen ist. Es ist auch möglich, lediglich das (nicht korrigierte) Zeitintervall Δt_switch in für den Fall II zu verwenden.In particular, the cycle-corrected time interval $\mathrm{\Delta }{t}_{switcH}^{\text{'}}$

used to intercept the case that more than one complete cycle time of the traffic light has passed after the point in time T _e . It is also possible to use only the (uncorrected) time interval Δt _switch in for case II.

Furthermore, t _{trans is} a predetermined, in particular fixed, parameter which corresponds to a transition time after the traffic light has changed to “red”. A driver of the vehicle behaves in this transition time t _trans 1 as if the traffic light was still "green". During this transition time t _trans there is a probability other than 0 that the current phase of the traffic light is “interpreted” as green. The transition time t _trans usually has a value of a few seconds. For example, the transition time t _trans can assume a value between 0s and 5s and in particular 3s. A further transition period after a phase change to “yellow” can also be taken into account. The further transition time is generally longer than the transition time t _trans and the probability that a driver will cross the traffic light in the further transition time is higher.

The distinctions made in Case II are explained below.

mit der roten Phasendauer X_rot des Referenzampelzyklus verglichen. Hier wird die rote Phasendauer X_rot des Referenzampelzyklus als Bruchteil x_rot von der Umlaufzeit t_cycle angegeben.If the traffic light phase C ^{Δ +} present after the phase change is a red or yellow traffic light phase, the time interval ΔT _switch (or the cycle-corrected time interval $\mathrm{\Delta }{t}_{switcH}^{\text{'}})$

compared with the red phase duration X _{red of} the reference traffic light cycle. Here the red phase duration X _{rot of} the reference traffic light _{cycle is} specified as a fraction x _rot of the cycle time t _cycle .

annimmt. Es wird also angenommen, dass die Ampel nach dem letzten Erfassungszeitpunkt T_e nicht auf die nächste Ampelphase weitergeschalten hat, so dass eine rote Ampelphase zum Event-Zeitpunkt T_event vorliegt.If the time interval ΔT _{switch is} smaller (shorter) than the red phase duration X _{rot of} the reference traffic light cycle, it is assumed that the red traffic light phase is present with a probability P (C _g = red), the probability P (C _g = red) being the smaller value of $\left\{\frac{\mathrm{\Delta }{t}_{switcH}^{\text{'}}}{t_{trans}}\mathrm{,1}\right\}$

accepts. It is therefore assumed that the traffic light has not switched to the next traffic light phase after the last detection time T _e , so that a red traffic light _phase is present at the event time T _event .

If, however, the time interval ΔT _{switch is} greater (longer) or equal to the red traffic light phase X _{red of} the reference traffic light cycle, it is assumed that the traffic light switched to the next (i.e. green) traffic light phase between the last detection time T _e and the event time T _event so that there is a green traffic light phase at the event time T _event . The probability P (C _g = red) is then 0 accordingly.

The same applies to the other case distinctions in Case II.

It should be noted that the reference traffic light _cycle does not have to take into account the transition time t _trans . The transition time t _trans can thus be ignored, so that the first and fourth case distinctions from case II always have a probability of 1. The following then applies: $$\begin{array}{l}P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{T}_{switcH},{\mathrm{C.}}^{\mathrm{\Delta }+}=c^{\mathrm{\Delta }+},T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)=\\ =\{\begin{array}{ll}\mathrm{1,}\hfill & c^{\mathrm{\Delta }+}=rOt\vee Gelb,\Delta t_{switcH}^{\text{'}}<x_{rOt}*t_{cycle}\hfill \\ 0\hfill & c^{\mathrm{\Delta }+}=rOt\vee Gelb,\Delta t_{switcH}^{\text{'}}\ge x_{rOt}*t_{cycle}\hfill \\ 0\hfill & c^{\mathrm{\Delta }+}=Grün\vee \left(rOt+Gelb\right),\mathrm{\Delta }{t}_{switcH}^{\text{'}}<\left(1-x_{rOt}\right)*t_{cy\mathrm{<figure-callout\; id="1"\; label="c\; l\; e"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">c</figure-callout>}le}\hfill \\ \mathrm{1,}\hfill & c^{\mathrm{\Delta }+}=Grün\vee \left(rOt+Gelb\right),\mathrm{\Delta }{t}_{switcH}^{\text{'}}\ge \left(1-x_{rOt}\right)*t_{cycle}\hfill \end{array}\end{array}$$

The probability for P (C _g = rot) given for case II can be calculated for all possible cycle times T _cycle and all possible phase durations X _rot . Thus, an overall probability P (C ₉ = red) can be determined over all cycle times T _cycle and phase durations X _red as follows: $$\begin{array}{l}P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{T}_e,\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{t}_{switcH},{\mathrm{C.}}^{\mathrm{\Delta }+}=c^{\mathrm{\Delta }+}\right)=\\ ={\displaystyle {\int }_{t_{cycle\_min}}^{t_{cycle\_max}}{\displaystyle {\int }_{max\left(rO{t}_{min}:\frac{\mathrm{\Delta }{t}_{switcH}^{\text{'}}-\mathrm{\Delta }{t}_e}{t_{cycle}}\right)}^{rO{t}_{max}}P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{t}_{switcH},{\mathrm{C.}}^{\mathrm{\Delta }+}=c^{\mathrm{\Delta }+},T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)}}\\ *P\left(X_{rOt}=x_{rOt}\right)*P\left(T_{cycle}=t_{cycle}\right)d{x}_{rOt}d{t}_{cycle}\end{array}$$

The term P (C _g = red | ΔT _switch = Δt _switch , C ^{Δ +} = c ^{Δ +} , T _cycle = t _cycle , X _red = x _red ) can be determined as stated above.

P (X _rot = x _rot ) is the probability that the phase duration X _{rot has} a certain value x _rot from the above-mentioned value range [rot _min ; rot _max ] for the phase duration. P (T _cycle = t _cycle ) is the probability that the cycle time T _{cycle has} a certain value t _cycle from the above-mentioned value range [t _{cycle_min} ; t _{cycle_max} ] for the cycle time. In the case of a uniform distribution of the cycle times and phase durations modeled as random variables, as is the case here, the probability for each value from the value ranges is the same.

In order to calculate the overall probability, the integrals can be approximated, for example, by means of sums. Other approximation methods are also possible.

In case III, neither of the two previous cases I and II apply. This means that in case III only the last seen / recorded traffic light phase of the traffic light is known. Accordingly, it is not known how long this traffic light phase has been present.

Therefore, the probability P (C _g = red) is determined taking into account a relative duration of the green and red phases and the time passed between time T _i and time T _e as a function of the last phase seen. This results in four subcases III.a, III.b, III.c and III.d. The above explanations for case II apply accordingly.

For case III.a, in which the last seen phase C _g = red, the following then applies to the probability P (C _g = red): $$\begin{array}{l}P\left({\mathrm{C.}}_G=rOt\right)=P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{t}_e,\mathrm{\Delta }{T}_i=\mathrm{\Delta }{t}_i,{\mathrm{C.}}_G=rOt,T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)\\ ={\displaystyle {\int }_{\mathrm{\Delta }{t}_i}^{\mathrm{\Delta }{t}_e+t_{cycle}}P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{t}_{switcH},{\mathrm{C.}}_G=rOt,T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)}\\ *P\left(\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{t}_{switcH}|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{t}_e,\mathrm{\Delta }{T}_i=\mathrm{\Delta }{t}_i,{\mathrm{C.}}_G=rOt,T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)d\mathrm{\Delta }{t}_{switcH}\end{array}$$

The following also applies: $$\begin{array}{c}P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{t}_{switcH},{\mathrm{C.}}_G=rOt,T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)\\ =\{\begin{array}{cc}\mathrm{1,}& \mathrm{\Delta }{t}_{switcH}\text{mod}{t}_{cycle}<x_{rOt}*t_{cy\mathrm{<figure-callout\; id="0"\; label="c\; l\; e"\; filenames="DE102019211102A1\_0057.png,DE102019211102A1\_0058.png"\; state="\{\{state\}\}">c</figure-callout>}le}\\ \mathrm{0,}& \mathrm{\Delta }{t}_{switcH}\text{mod}{t}_{cycle}\ge x_{rOt}*t_{cycle}\end{array}\end{array}$$

The phase change time T _switch and, accordingly, the time interval ΔT _switch are unknown and are therefore modeled. For this purpose, it is assumed that the phase change time T _{switch had to take place} between a cycle time T _cycle before the first detection time T _i and the last detection time T _e .

Therefore, the following applies to the probability that the phase change time T _{switch occurs} at a certain time t _switch or that the time interval ΔT _{switch has} a certain value Δt _switch : $$\begin{array}{l}P\left(\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{t}_{switcH}|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{t}_e,\mathrm{\Delta }{T}_i=\Delta t_i,{\mathrm{C.}}_G=rOt,T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)\\ =\frac{1}{\mathrm{\Delta }{t}_e+\mathrm{\Delta }{t}_{cycle}-\mathrm{\Delta }{t}_i}\end{array}$$

In case III.b, the last seen phase C _g = green. This corresponds to case III.a, only that the green phase replaces the red phase in case III.a. Hence: $$\begin{array}{l}P\left({\mathrm{C.}}_G=rOt\right)=P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{t}_e,\mathrm{\Delta }{T}_i=\mathrm{\Delta }{t}_i,{\mathrm{C.}}_G=Grün,T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)\\ ={\displaystyle {\int }_{\mathrm{\Delta }{t}_i}^{\mathrm{\Delta }{t}_e+t_{cycle}}P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{t}_{switcH},{\mathrm{C.}}_G=Grün,T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)}\\ *P{(\mathrm{\Delta }T}_{switcH}=\mathrm{\Delta }{t}_{switcH}|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{t}_e,\mathrm{\Delta }{T}_i=\mathrm{\Delta }{t}_i,{\mathrm{C.}}_G=Grün,T_{cycle}=t_{cycle},X_{rOt}=\left(x_{rOt}\right)d\mathrm{\Delta }{t}_{switcH}\end{array}$$

The following applies to the probability that the red traffic light phase is present at the event time T _event if the green traffic light phase was last seen: $$\begin{array}{l}P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{t}_{switcH},{\mathrm{C.}}_G=Grün,T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)\\ =\{\begin{array}{cc}\mathrm{0,}& \mathrm{\Delta }{t}_{switcH}\text{mod}{t}_{cycle}<x_{rOt}*t_{cy\mathrm{<figure-callout\; id="1"\; label="c\; l\; e"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">c</figure-callout>}le}\\ \mathrm{1,}& \mathrm{\Delta }{t}_{switcH}\text{mod}{t}_{cycle}\ge x_{rOt}*t_{cycle}\end{array}\end{array}$$

For the probability that the phase change time T _{switch occurs} at a specific time t _switch or that the time interval ΔT _{switch has} a specific value Δt _switch , the following applies analogously to case III.a: $$\begin{array}{l}P\left(\mathrm{\Delta }{T}_{switcH}=\mathrm{\Delta }{t}_{switcH}|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{t}_e,\mathrm{\Delta }{T}_i=\mathrm{\Delta }{t}_i,{\mathrm{C.}}_G=Grün,T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)\\ =\frac{1}{\mathrm{\Delta }{t}_e+\mathrm{\Delta }{t}_{cycle}-\mathrm{\Delta }{t}_i}\end{array}$$

There is the same probability P (ΔT _switch = Δt _switch | ...) for cases III.a and III.b, since the circulation time T _{cycle of} the reference traffic light _cycle does not change.

In case III.c, the last seen traffic light phase is yellow. It is assumed here that time T _e corresponds to time T _switch , that is, T _e = T _switch . The same applies to the time intervals Δt _e = Δt _switch . In this case the following applies to the probability P (C _g = red): $$\begin{array}{l}P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{t}_e,\mathrm{\Delta }{t}_i=\mathrm{\Delta }{t}_i,{\mathrm{C.}}_G=Gelb,T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)\\ =\{\begin{array}{cc}\mathrm{1,}& \mathrm{\Delta }{t}_e\le x_{rOt}*t_{cy\mathrm{<figure-callout\; id="0"\; label="c\; l\; e"\; filenames="DE102019211102A1\_0057.png,DE102019211102A1\_0058.png"\; state="\{\{state\}\}">c</figure-callout>}le}\\ \mathrm{0,}& \mathrm{\Delta }{t}_e>x_{rOt}*t_{cycle}\end{array}\end{array}$$

In case III.d, the traffic light phase last seen is “red + yellow”. Here, too, it is assumed that time T _e corresponds to time T _switch , that is, T _e = T _switch . Correspondingly, the following also applies to the time intervals Δt _e = Δt _switch . $$\begin{array}{l}P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{t}_e,\mathrm{\Delta }{t}_i=\mathrm{\Delta }{t}_i,{\mathrm{C.}}_G=rOt+Gelb,T_{cycle}=t_{cycle},X_{rOt}=x_{rOt}\right)\\ =\{\begin{array}{cc}\mathrm{0,}& \mathrm{\Delta }{t}_e\le \left(1-x_{rOt}\right)*t_{cy\mathrm{<figure-callout\; id="1"\; label="c\; l\; e"\; filenames="DE102019211102A1\_0055.png,DE102019211102A1\_0057.png"\; state="\{\{state\}\}">c</figure-callout>}le}\\ \mathrm{1,}& \mathrm{\Delta }{t}_e>\left(1-x_{rOt}\right)*t_{cycle}\end{array}\end{array}$$

The probabilities P (C ₉ = red) for cases III.a to III.d can be calculated for all possible cycle times T _cycle and all possible phase durations X _red , similar to case II. The following applies to the overall probability: $$\begin{array}{l}P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{t}_e,\mathrm{\Delta }{T}_i=\mathrm{\Delta }{t}_i,{\mathrm{C.}}_G=c\right)=\\ ={\displaystyle {\int }_{t_{cycle\_min}}^{t_{cycle\_max}}{\displaystyle {\int }_{max\left(rO{t}_{min}:\frac{\mathrm{\Delta }{t}_i-\mathrm{\Delta }{t}_e}{t_{cycle}}\right)}^{rO{t}_{max}}P\left({\mathrm{C.}}_G=rOt|\mathrm{\Delta }{T}_e=\mathrm{\Delta }{t}_e,\mathrm{\Delta }{T}_i=\mathrm{\Delta }{t}_i,{\mathrm{C.}}_G=c,T_{cycle}=t_{cycle,}{X}_{rOt}=x_{rOt}\right)}}\\ *P\left(X_{rOt}=x_{rOt}\right)*P\left(T_{cycle}=t_{cycle}\right)d{x}_{rOt}d{t}_{cycle}\end{array}$$

Here, too, the integrals can be approximated, for example by means of sums, in order to calculate the overall probability. Other approximation methods are also possible.

In 7th are schematic and exemplary two phase courses 9 , 9 ' represented by two traffic light objects. The phases 9 , 9 ' show the green traffic light phase G , the red light phase R. , the yellow traffic light phase Y and the red-yellow traffic light phase R + Y .

The combination of the solid lines results in the coincidence time Tm of the two phase courses 9 , 9 ' in which they have the same traffic light phase (simultaneously). The combination of the solid lines with the dashed line results in the overlap duration Tt of the two phase courses 9 , 9 ' , in which the phase progressions 9 , 9 ' (regardless of their traffic light phases) overlap in time.

The overlap period Tt thus indicates the period of time at which the two phase courses 9 , 9 ' are present at the same time, ie overlap in time. The period of correspondence Tm indicates that period of time (within the overlap period Tt) at which the phase progressions 9 , 9 ' have the same traffic light phase.

Furthermore, in 7th corresponding first acquisition times T _i , last acquisition times T _e and phase change times T _{switch of} the phase profiles 9 , 9 ' shown.

List of reference symbols

A.

Traffic lights ( A. )

F1-F3

Lanes

Fb

Lane section

G

green traffic light phase

tp ₁ -tp ₅

traffic light

n _c

Traffic light objects

n _{c, t}

traffic light object assigned to a cluster t

R.

red light phase

R + Y

red-yellow traffic light phase

t ₁ -t ₅

(Traffic light / traffic light object) cluster

tp ₁ -tp ₅

physical traffic light

Y

yellow traffic light phase

1

vehicle

3

Detection device

5

Radio interface

7th

Traffic light image

7 '

Traffic light image

9

Phase progression

9 '

Phase progression

S ...

Procedural steps

QUOTES INCLUDED IN THE DESCRIPTION

This list of the documents listed by the applicant was generated automatically and is included solely for the better information of the reader. The list is not part of the German patent or utility model application. The DPMA assumes no liability for any errors or omissions.

Patent literature cited

• DE 102013220662 A1 [0003]
• DE 102015003847 A1 [0004]
• DE 102016217558 A1 [0005]

Claims (15)

Method for evaluating traffic light information about a traffic light system (A), with: - (S204) forming all traffic light pairs from traffic lights of the traffic light system (tp ₁ , tp ₂ , tp ₃ , tp ₄ , tp ₅ ); - (S205) determining, for each pair of traffic lights, a correlation factor (ρ _AP ) for a phase curve of a first traffic light of the pair of traffic lights and a phase curve of a second set of traffic lights of the pair of traffic lights; and - (S206) grouping of the traffic lights (tp ₁ , tp ₂ , tp ₃ , tp ₄ , tp ₅ ) taking into account the correlation factors (p _AP ) of the traffic light pairs. Procedure according to Claim 1 , wherein all traffic lights located in a group have correlation factors (ρ _AP ) to one another which exceed a first predetermined minimum correlation value. Procedure according to Claim 1 or 2 , wherein determining the correlation factor (ρ _AP ) for each pair of traffic lights further comprises: - (S205) determining an overlap duration (Tt _AP ) between the phase curves of the traffic lights of the pair of traffic lights, in which the phase curve of the first traffic light and the phase curve of the second traffic light are temporal overlap; - (S205) determining a coincidence duration (Tm _AP ) in which a phase of the first traffic light and a phase of the second traffic light coincide during the overlap period (Tt _AP ). Method according to one of the preceding claims, further comprising: - (S201) calling up traffic light objects (n _{c, t} ) representing traffic light information, each traffic light object being assignable to a corresponding traffic light (tp ₁ , tp ₂ , tp ₃ , tp ₄ , tp ₅ ) is. Procedure according to Claim 4 , whereby the traffic light objects (n _{c, t} ) are recorded specific to the crossing. Procedure according to Claim 4 or 5 wherein the determination of the correlation factor for each pair of traffic lights further comprises: - (S205) Formation of overlap durations (Tt _n ) and / or coincidence durations (Tm _n ) between all traffic light objects (n _{c, t} representing the first traffic light) ₁ ) and all traffic light objects representing the second traffic light (n _{c, t 2} ) and - (S205) adding up the overlap durations (Tt _n ) and / or adding up the correspondence durations (Tm _n ) between all the traffic light objects (n _{c, t} ) representing the first traffic light ₁ ) and all traffic light objects representing the second traffic light (n _{c, t 2} ). Procedure according to Claim 6 , the formation of the overlap durations (Tt _n ) and / or the coincidence durations (Tm _n ) between all the traffic light objects (n _{c, t 1} ) and all traffic light objects representing the second traffic light (n _{c, t 2} ) specific to the crossing. Method according to one of the Claims 4 to 7th , wherein the formation of all traffic light pairs from the traffic lights (tp ₁ , tp ₂ , tp ₃ , tp ₄ , tp ₅ ) further comprises: - (S202) Forming all traffic light object pairs from traffic light objects (n _{c, t} ) which are assigned to one of the traffic lights ; - (S202) determining, for each traffic light object pair, a correlation _factor (ρ _obj ) for a phase curve of the first traffic light object (n _{c, t} ) of the traffic light object pair and a phase curve of a second traffic light object (n'c _{, t} ) of the traffic light object pair; and - (S202) grouping the traffic light objects taking into account the correlation factors of the traffic light object pairs; - (S202) recognizing the one traffic light (tp ₁ , tp ₂ , tp ₃ , tp ₄ , tp ₅ ) as inconsistent if a number of traffic light object groups exceeds a predetermined maximum value; and - (S202) excluding the inconsistent traffic light (t *) from the formation of all traffic light pairs. Procedure according to Claim 8 , which further comprises: - (S207) assigning the inconsistent traffic light (t *) to a corresponding traffic light group (g) taking into account correlation factors between the traffic light objects (n _{c, t *} ) of the inconsistent traffic light (t *) and the traffic light objects (n _{c, t G} ) of the corresponding traffic light group (g). Method according to one of the Claims 4 to 9 which further comprises: - (S203) checking the phase progressions represented by the traffic light objects (n _{c, t} ) for plausibility; and - (S203) discarding implausible traffic light objects (n _{c, t} ) Method according to one of the Claims 4 to 10 , each traffic light object comprising the following information for the corresponding phase profile: start time of the corresponding phase profile with a corresponding phase; - Change times of the corresponding phase course with a corresponding phase; and - the end time of the corresponding phase progression. Method according to one of the preceding claims, wherein the traffic light objects (n _c ) are detected by a plurality of vehicles (1) with detection devices (3). Method according to one of the preceding claims, wherein the grouping of the traffic lights and / or the traffic light objects by means of a hierarchical cluster algorithm. Apparatus for processing data via a traffic light system (A) controlling a lane section with at least one traffic light (tp ₁ , tp ₂ , tp ₃ , tp ₄ , tp ₅ ), the apparatus being designed and set up, a method according to one of the Claims 1 to 13 execute. Computer program for processing data on a traffic light system (A) controlling a lane section with at least one traffic light (tp ₁ , tp ₂ , tp ₃ , tp ₄ , tp ₅ ), the computer program being designed to cause a data processing device when it is executed Method according to one of the Claims 1 to 13 execute.

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