EP3866135B1 - Procédé de commande d'une installation de signalisation lumineuse - Google Patents

Procédé de commande d'une installation de signalisation lumineuse Download PDF

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Publication number
EP3866135B1
EP3866135B1 EP21151471.6A EP21151471A EP3866135B1 EP 3866135 B1 EP3866135 B1 EP 3866135B1 EP 21151471 A EP21151471 A EP 21151471A EP 3866135 B1 EP3866135 B1 EP 3866135B1
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EP
European Patent Office
Prior art keywords
traffic condition
traffic
control signals
threshold value
signal system
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Active
Application number
EP21151471.6A
Other languages
German (de)
English (en)
Other versions
EP3866135C0 (fr
EP3866135A1 (fr
Inventor
David Borst
Florian Fanderl
Markus Mauder
Evren Pamir
Konrad Vowinckel
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Yunex GmbH
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Yunex GmbH
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
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Publication of EP3866135A1 publication Critical patent/EP3866135A1/fr
Application granted granted Critical
Publication of EP3866135C0 publication Critical patent/EP3866135C0/fr
Publication of EP3866135B1 publication Critical patent/EP3866135B1/fr
Active legal-status Critical Current
Anticipated expiration legal-status Critical

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Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0116—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from roadside infrastructure, e.g. beacons
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0129—Traffic data processing for creating historical data or processing based on historical data
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0137—Measuring and analyzing of parameters relative to traffic conditions for specific applications
    • G08G1/0145—Measuring and analyzing of parameters relative to traffic conditions for specific applications for active traffic flow control
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/07—Controlling traffic signals
    • G08G1/08—Controlling traffic signals according to detected number or speed of vehicles

Definitions

  • the invention relates to a method for controlling a traffic signal system.
  • the invention relates to a device, a computer program and a machine-readable storage medium.
  • EP 3 425 608 A1 discloses a method for controlling a traffic signal system.
  • EP 1 628 274 A1 discloses a method for determining traffic information and controlling traffic.
  • Traffic signal systems are usually operated or controlled based on a signal schedule.
  • a signal schedule is fixed and not changed while the traffic signal system is in operation.
  • the signal schedule is changed during ongoing operation. This, for example, based on a traffic condition in an area around the traffic signal system.
  • machine learning algorithms can be used to change a signal schedule based on traffic conditions in the area around the traffic signal system.
  • Such algorithms are usually trained using training data.
  • different traffic conditions in the area around the traffic signal system can be simulated. These simulated traffic conditions can be used as training data for machine learning.
  • the object on which the invention is based is to provide a concept for efficiently controlling a traffic signal system, which makes it possible to efficiently determine training data for machine learning with regard to predicting the behavior of the traffic signal system.
  • a method for controlling a traffic signal system is provided, which is specified in claim 1.
  • a device which is set up to carry out all steps of the method according to the first aspect.
  • a computer program which comprises instructions which, when the computer program is executed by a computer, for example by the device according to the second aspect, cause the computer to carry out a method according to the first aspect.
  • a machine-readable storage medium is provided on which the computer program according to the third aspect is stored.
  • the invention is based on and includes the knowledge that the above object can be achieved by changing or replacing a current signal schedule during ongoing operation of the traffic signal system. This brings about the particular technical advantage that the traffic signal system can or will be controlled based on the changed signal schedule or based on the replaced signal schedule.
  • This recorded traffic condition can advantageously be used as training data for machine learning be, for example, to predict or predict the behavior of the traffic signal system, for example to predict a signal time of the traffic signal system.
  • the current traffic condition is compared with a historical traffic condition in order to determine a deviation of the current traffic condition from the historical traffic condition.
  • the control signals are generated based on the determined deviation.
  • the determined deviation is too large, it can be assumed that there is a problem in the area around the traffic signal system, for example an accident or a traffic jam. In such a situation, the already critical traffic condition should not be potentially negatively influenced by experiments regarding changed or replaced signal schedules.
  • taking the historical traffic condition into account also has the advantage of finding particularly suitable ones To recognize traffic conditions with regard to changing or replacing the current signal schedule in order to obtain or determine training data for different real traffic conditions.
  • the particular technical advantage is that a concept for efficiently controlling a traffic signal system is provided, which makes it possible to efficiently determine training data for machine learning, for example in order to determine behavior during ongoing operation of the traffic signal system based on a correspondingly trained algorithm of traffic responsive to a change or replacement of the signal schedule.
  • signal schedule can also be used for the term “signal schedule”.
  • deviation threshold signals are received, which represent a deviation threshold, the determined deviation being compared with the deviation threshold, the control signals being generated depending on the comparison of the determined deviation with the deviation threshold.
  • control signals are not generated if the determined deviation is greater than or equal to the deviation threshold value.
  • the deviation determined is a percentage.
  • the deviation threshold is a percentage.
  • the percentage can, for example, refer to the historical traffic condition.
  • control signals are only generated if the determined deviation is less than or equal to the deviation threshold value.
  • the deviation can be zero, for example.
  • control signals are such that the changed signal schedule or the replaced signal schedule can cause a deterioration or an improvement in a current traffic flow through the traffic signal system.
  • traffic condition threshold signals are received which represent at least one traffic condition threshold, the control signals being generated based on the at least one traffic condition threshold.
  • the at least one traffic condition threshold is an element from the following group of traffic condition thresholds: a minimum and/or maximum number of vehicles per green second and/or a minimum and/or maximum number of vehicles per rotation time unit and/or a minimum and/or maximum detector occupancy per rotation time unit.
  • the current traffic condition is compared with the at least one traffic condition threshold, the control signals being generated based on the comparison of the current traffic condition with the at least one traffic condition threshold.
  • control signals are such that the changed signal time plan or the replaced signal time plan correspond to a signal time base plan.
  • control signals before the control signals are output, it is checked whether changing or replacing the signal schedule would cause the current traffic condition to exceed the traffic condition threshold, the control signals being output depending on a result of the testing.
  • control signals will not be output.
  • control signals are only output if the result of the check indicates that the current traffic condition remains within the predetermined threshold values.
  • the current and the historical traffic status each include a number of vehicles per green second and/or number of vehicles per rotation time unit and/or a detector occupancy per rotation time unit.
  • the orbital period unit is an orbital second or a value which is smaller or greater than one orbital second.
  • a detector occupancy represents a number of vehicles that are detected by a detector in the vicinity of the traffic signal system.
  • a detector includes, for example, a video camera and/or an induction coil, which is embedded or arranged, for example, within a roadway.
  • a detector includes, for example, an environment sensor or several environment sensors.
  • An environment sensor is, for example, one of the following environment sensors: radar sensor, ultrasonic sensor, magnetic field sensor, lidar sensor, magnetic field sensor, infrared sensor, video sensor, in particular video sensor of a video camera.
  • training data signals are generated and output, which represent training data for machine learning, the training data comprising one or more of the following data: traffic status in the area around the traffic signal system, while the traffic signal system is operating based on the changed signal schedule or is operated on the replaced signal schedule, at least one parameter of the changed signal schedule or the replaced signal schedule, the currently applied change to the signal schedule, the resulting signal schedule, Detection times of vehicles, vehicle numbers derived therefrom, static information about the traffic signal system, in particular topology of a junction, in particular intersection, whose traffic the traffic signal system regulates, and / or static information about a system assigned to the traffic signal system, occupancy data, the occupancy data in particular one or more elements the following group of occupancy data include: position of a detection of a vehicle, trajectory of the detected vehicle, occupancy values of a detector per rotation time unit, number of vehicles per detector per rotation time unit, rotation time.
  • the above exemplary data for training data relates to the traffic signal system.
  • one or more corresponding analog data related to a direct or indirect neighboring light signal system of the light signals are included.
  • a traffic condition in the sense of the description includes, for example, a lane-specific or lane-dependent traffic condition.
  • the traffic condition or the traffic condition threshold is or are defined in relation to a lane.
  • the historical traffic condition includes an averaged historical traffic condition.
  • the method includes controlling the traffic signal system based on the output control signals.
  • the method according to the first aspect is a computer-implemented method.
  • the environment of the traffic signal system refers in particular to an area around the traffic signal system up to a maximum distance of, for example, 1 km, in particular 500 m, in particular 200 m, in particular 100 m, in particular 50 m, in particular 20 m.
  • the method according to the first aspect is carried out or carried out by means of the device according to the second aspect.
  • Machine learning includes in particular a neural network and/or reinforcement learning.
  • changing the current signal schedule includes changing at least one parameter of the current signal schedule.
  • the at least one parameter of the signal plan is an element selected from the following group of parameters: start time of a signal state, duration of a signal state, start point, switch-on time, rotation time.
  • FIG 2 shows a device 201.
  • the device 201 is set up to carry out all steps of the method according to the first aspect.
  • the device 201 includes an input 203, which is set up to receive the first traffic status data.
  • the input 203 is further set up to receive the second traffic status data.
  • the device 201 includes a processor 205, which is set up to determine a deviation of the current traffic condition from the historical traffic condition.
  • the processor 205 is further set up to generate control signals for controlling the traffic signal system based on the determined deviation in such a way that when controlling the traffic signal system based on the Control signals, a current signal schedule is changed or replaced.
  • the device 201 includes an output 207, which is set up to output the generated control signals.
  • the device 201 comprises several processors instead of the one processor 205.
  • FIG 3 shows a machine-readable storage medium 301.
  • a computer program 303 is stored on the machine-readable storage medium 301.
  • the computer program 303 includes commands that occur when the computer program 303 is executed by a computer, for example by the device 201 according to FIG 2 , cause this to carry out a method according to the first aspect.
  • changing the current signal schedule includes changing at least one parameter of the current signal schedule.
  • the concept described here is based, among other things, on researching traffic system behavior in real time by changing or replacing a current signal schedule of a traffic signal system. This brings about the particular technical advantage that the resulting traffic conditions can be examined or recorded for different signal schedules.
  • traffic detectors also simply referred to as detectors
  • Such traffic detectors include, for example, video cameras and/or induction loops that are embedded or embedded in a roadway.
  • the current traffic status can include, for example, a number of vehicles per green second and/or number of vehicles per rotation time unit and/or a detector occupancy per rotation time unit.
  • the current traffic condition is compared in particular with a historical traffic condition in the area around the traffic signal system.
  • the historical traffic condition may include, for example, a number of vehicles per green second and/or number of vehicles per rotation time unit and/or a detector occupancy per rotation time unit.
  • the historical traffic condition is or the historical traffic condition includes an averaged historical traffic condition.
  • the historical traffic condition includes an average number of vehicles per green second and/or an average number of vehicles per rotation time unit and/or an average detector occupancy per rotation time unit.
  • a current date is assigned to the current traffic status.
  • the historical traffic condition is assigned a historical date corresponding to the current date.
  • a date includes in particular the specification of a day of the week and/or the specification of a time and/or the specification of a year.
  • a deviation of the current traffic condition from the historical traffic condition is determined. For example, a deviation of the current number of vehicles per green second and/or number of vehicles per rotation time unit and/or a detector occupancy per rotation time unit with a historical number of vehicles per green second and/or number of vehicles per rotation time unit and/or a detector occupancy per rotation time unit , in particular an averaged historical number of vehicles per green second and/or an averaged historical number of vehicles per rotation time unit and/or an average historical detector occupancy per rotation time unit.
  • a decision is made in particular as to whether the current signal schedule of the traffic signal system should be changed or replaced.
  • the deviation can be a percentage, for example.
  • a deviation threshold value is specified, with the control signals being generated depending on a comparison of the determined deviation with the deviation threshold value.
  • the determined deviation is less than or less than or equal to the predetermined deviation threshold value, provision is made in particular to change or replace the current signal schedule.
  • the current signal schedule has not been changed or replaced.
  • the background to such a procedure is in particular that if a deviation is greater than or equal to the predetermined deviation threshold value, it can be assumed that there is an atypical traffic situation, i.e. an atypical traffic condition, in the area around the traffic signal system.
  • an atypical traffic condition can occur, for example, due to an accident or a traffic jam.
  • the key figures of the traffic i.e. the traffic condition, at the intersection, in particular at the intersection, are compared with predetermined traffic condition threshold values.
  • the limits which may not be exceeded by changing or maintaining a signal plan are defined in particular by, for example, a minimum and/or maximum number of vehicles per green second and/or a minimum and/or maximum Number of vehicles per rotation time unit and/or a minimum and/or maximum detector occupancy per rotation time unit.
  • the control signals are generated in particular depending on a result of this comparison.
  • the concept described here offers a variety of advantages in controlling the traffic signal system and its impact on traffic conditions, primarily by improving efficiency, reducing costs and expanding the possibilities of the traffic signal system to control or operate based on a variety of changed or replaced signal schedules.
  • the limiting factor of well-known concepts is the amount of time an engineer needs to simulate a traffic system or to monitor the state of the traffic system in order to avoid negative influences on the traffic system. Neglecting negative influences on the traffic system would limit the collection of training data to traffic situations in which only limited negative effects on traffic would be expected. This takes into account the obligation of local governments to optimally manage their networks. Therefore, it is expected that severe negative traffic impacts will not be accepted by the customer, limiting real-world exploration to scenarios with low traffic volumes and low potential for negative impacts. However, this also dramatically limits the ability to accumulate training data sets. This disadvantage is overcome by the concept described here.
  • an AI artificial intelligence
  • the proposed solution represents a compromise between the need for large, diverse training data sets and the expected negative impact on the traffic situation by automatically monitoring the traffic system and generating the control signals based on the comparison of the historical with the current traffic status.
  • the current traffic volume as well as the historical traffic volume is advantageously taken into account, in particular in relation to different lanes of a junction, in particular a road intersection, so that the change or replacement of the current signal schedule is automatically reduced or deactivated when the current control of the Traffic signal system based on the current signal schedule could have a significant negative impact on the traffic system.
  • the concept advantageously enables a machine learning algorithm to optimize the traffic system, for example.
  • the concept also allows the application of machine learning algorithms to be transferred to a variety of road intersections.

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  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Chemical & Material Sciences (AREA)
  • Analytical Chemistry (AREA)
  • Traffic Control Systems (AREA)

Claims (9)

  1. Procédé de commande d'une installation de signalisation lumineuse, comprenant les étapes suivantes :
    recevoir (101) des premières données d'état de trafic représentant un état de trafic actuel dans un environnement de l'installation de signalisation lumineuse,
    recevoir (103) des deuxièmes données d'état de trafic représentant un état de trafic historique dans l'environnement de l'installation de signalisation lumineuse,
    déterminer (105) un écart entre l'état de trafic actuel et l'état de trafic historique,
    générer (107) des signaux de commande pour commander l'installation de signalisation lumineuse sur la base de l'écart déterminé de telle sorte que, lors d'une commande de l'installation de signalisation lumineuse sur la base des signaux de commande, un chronogramme de signalisation actuel est modifié ou remplacé,
    émettre (109) les signaux de commande générés, des signaux de données d'apprentissage étant générés et émis, lesquels représentent des données d'apprentissage pour un apprentissage automatique, les données d'apprentissage comprenant une ou plusieurs des données suivantes : état de la circulation dans l'environnement de l'installation de signalisation lumineuse pendant que l'installation de signalisation lumineuse fonctionne sur la base du chronogramme de signalisation modifié ou du chronogramme de signalisation remplacé, au moins un paramètre du chronogramme de signalisation modifié ou du chronogramme de signalisation remplacé, la modification du chronogramme de signalisation actuellement appliqué, le chronogramme de signalisation qui en résulte, les temps de détection des véhicules, les nombres de véhicules qui en sont déduits, des informations statiques sur l'installation de signalisation lumineuse, en particulier la topologie d'un carrefour, en particulier d'une intersection, dont le trafic est régulé par l'installation de signalisation lumineuse, et/ou sur une installation associée à l'installation de signalisation lumineuse, des données d'occupation, les données d'occupation comprenant en particulier un ou plusieurs des éléments du groupe de données d'occupation suivant : position d'une détection d'un véhicule, trajectoire du véhicule détecté, valeurs d'occupation d'un détecteur par unité de temps de circulation, nombre de véhicules par détecteur par unité de temps de circulation, durée de circulation,
    caractérisé en ce que
    des signaux de valeur seuil d'état de trafic sont reçus, lesquels représentent au moins une valeur seuil d'état de trafic, les signaux de commande étant générés sur la base de ladite au moins une valeur seuil d'état de trafic, l'état actuel du trafic est comparé à ladite au moins une valeur seuil de l'état du trafic, les signaux de commande sont générés sur la base de la comparaison de l'état actuel du trafic avec ladite au moins une valeur seuil de l'état du trafic ;
    avant d'émettre les signaux de commande, il est vérifié si, en modifiant ou en remplaçant le chronogramme des signaux, l'état actuel du trafic dépasserait ladite au moins une valeur seuil de l'état du trafic, les signaux de commande étant émis en fonction d'un résultat de la vérification.
  2. Procédé selon la revendication 1, dans lequel des signaux de seuil d'écart sont reçus, lesquels représentent un seuil d'écart, l'écart déterminé étant comparé au seuil d'écart, les signaux de commande étant générés en fonction de la comparaison de l'écart déterminé avec le seuil d'écart.
  3. Procédé selon la revendication 1 ou 2, dans lequel les signaux de commande sont tels que le chronogramme de signalisation modifié ou le chronogramme de signalisation remplacé soit apte à entraîner une détérioration ou une amélioration d'un flux de trafic actuel par le système de signalisation lumineuse.
  4. Procédé selon l'une des revendications précédentes, dans lequel ledit au moins un seuil d'état de trafic est respectivement un élément du groupe de seuils d'état de trafic suivant :
    un nombre minimal et/ou maximal de véhicules par seconde de feu vert et/ou un nombre minimal et/ou maximal de véhicules par unité de temps de circulation et/ou une occupation minimale et/ou maximale du détecteur par unité de temps de circulation.
  5. Procédé selon l'une des revendications précédentes, dans lequel, lorsque l'état actuel du trafic dépasse ledit au moins un seuil d'état du trafic, les signaux de commande sont tels que le chronogramme de signal modifié ou le chronogramme de signal remplacé correspond à un chronogramme de base de signal.
  6. Procédé selon l'une des revendications précédentes, dans lequel l'état de trafic actuel et l'état de trafic historique comprennent chacun un nombre de véhicules par seconde de feu vert et/ou un nombre de véhicules par unité de temps de circulation et/ou une occupation de détecteur par unité de temps de circulation.
  7. Dispositif (201) adapté pour mettre en oeuvre toutes les étapes du procédé selon l'une des revendications précédentes.
  8. Programme d'ordinateur (303) comprenant des instructions qui, lorsque le programme d'ordinateur (303) est exécuté par un ordinateur, amènent ce dernier à mettre en oeuvre un procédé selon l'une des revendications 1 à 6.
  9. Support de stockage (301) lisible par machine, sur lequel est stocké le programme d'ordinateur (303) selon la revendication 8.
EP21151471.6A 2020-02-14 2021-01-14 Procédé de commande d'une installation de signalisation lumineuse Active EP3866135B1 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
DE102020201878.2A DE102020201878A1 (de) 2020-02-14 2020-02-14 Verfahren zum Steuern einer Lichtsignalanlage

Publications (3)

Publication Number Publication Date
EP3866135A1 EP3866135A1 (fr) 2021-08-18
EP3866135C0 EP3866135C0 (fr) 2024-03-06
EP3866135B1 true EP3866135B1 (fr) 2024-03-06

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DE (1) DE102020201878A1 (fr)

Family Cites Families (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE1516616A1 (de) 1965-12-17 1969-06-12 Peat Marwick Mitchell & Co System zur Steuerung von Verkehrssignalen
DE10146398A1 (de) 2001-09-20 2003-04-17 Siemens Ag System zum Steuern von Lichtsignalgebern an Kreuzungen
DE102004039854A1 (de) 2004-08-17 2006-03-09 Siemens Ag Verfahren zum Ermitteln von Verkehrsinformationen, Verfahren zum Steuern des Verkehrs, sowie System zum Durchführen der Verfahren
EP3425608B1 (fr) * 2017-07-03 2020-03-25 Fujitsu Limited Commande de signal de circulation utilisant de multiples catégories de q-learning

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EP3866135C0 (fr) 2024-03-06
EP3866135A1 (fr) 2021-08-18
DE102020201878A1 (de) 2021-08-19

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