EP4107590A1 - Procédé pour entraîner au moins un algorithme destiné à un appareil de commande d'un véhicule à moteur, procédé pour optimiser un flux de trafic dans une région, produit-programme d'ordinateur et véhicule à moteur - Google Patents

Procédé pour entraîner au moins un algorithme destiné à un appareil de commande d'un véhicule à moteur, procédé pour optimiser un flux de trafic dans une région, produit-programme d'ordinateur et véhicule à moteur

Info

Publication number
EP4107590A1
EP4107590A1 EP21704766.1A EP21704766A EP4107590A1 EP 4107590 A1 EP4107590 A1 EP 4107590A1 EP 21704766 A EP21704766 A EP 21704766A EP 4107590 A1 EP4107590 A1 EP 4107590A1
Authority
EP
European Patent Office
Prior art keywords
motor vehicle
traffic
mission
algorithm
simulation environment
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.)
Pending
Application number
EP21704766.1A
Other languages
German (de)
English (en)
Inventor
Ulrich Eberle
Christoph THIEM
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.)
Stellantis Auto SAS
Original Assignee
PSA Automobiles SA
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
Application filed by PSA Automobiles SA filed Critical PSA Automobiles SA
Publication of EP4107590A1 publication Critical patent/EP4107590A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/02—Neural networks
    • G06N3/08—Learning methods
    • G06N3/088—Non-supervised learning, e.g. competitive learning
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
    • G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
    • G05D1/0088—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots characterized by the autonomous decision making process, e.g. artificial intelligence, predefined behaviours
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
    • G06N3/00—Computing arrangements based on biological models
    • G06N3/004—Artificial life, i.e. computing arrangements simulating life
    • G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]

Definitions

  • a method for training at least one algorithm for a control unit of a motor vehicle, a method for optimizing a traffic flow in a region, a computer program product and a motor vehicle are described here.
  • a method for operating a vehicle is known from DE 102017 007468 A1, measurement data of a road traffic situation being determined and a future road traffic situation being determined in a traffic simulation based on the measurement data.
  • the measurement data are determined by means of the vehicle's own determination units for the vehicle and / or at least one other vehicle involved in the road traffic situation and sent to a central processing unit.
  • the central computing unit is used to carry out the traffic simulation for a predetermined time horizon and, depending on the results of the traffic simulation, the central computing unit determines vehicle parameters and sends them to the vehicle and / or the at least one other vehicle that when the vehicle parameters are set meter a driving style of the vehicle and / or the at least one further vehicle is adapted to promote a traffic flow.
  • a corresponding traffic flow metric can be, for example, an average speed that should correspond to a certain minimum speed as far as possible.
  • a minimum speed can be specified or derived from theoretical considerations, for example taking into account the particularities of a relevant region, for example taking into account applicable maximum speeds. Parameters such as traffic lights can also be taken into account.
  • the route via a corresponding traffic focus depicts a real deployment scenario, namely the routing of traffic by using a corresponding automatically driving lead vehicle.
  • the traffic simulation can map a real traffic situation in the operational area more precisely.
  • the traffic flow metric contains an average speed during the mission.
  • Reinforcing learning algorithms are particularly suitable for optimization tasks like this one.
  • Another independent subject matter relates to a device for training at least one algorithm for a control unit of a motor vehicle, the control unit being provided for implementing an automated or autonomous driving function by intervening in units of the motor vehicle on the basis of input data using the at least one algorithm , the algorithm being trained by a self-learning neural network, the device being designed to carry out the following steps: a) Providing a computer program product module for the automated or autonomous driving function, the computer program product module containing the algorithm to be trained and the self-learning neural Network contains, b) providing a simulation environment with simulation parameters, wherein the simulation environment contains map data of a real existing application area, the motor vehicle, wherein a behavior of the motor vehicle by a Rule set is determined, c) provision of real-time traffic data of the real existing application area as well as re-enactment of the traffic situation in the simulation environment; d) Determining traffic focal points on the basis of the real-time traffic data on the basis of a
  • the device is set up to select another mission and to repeat the method with the other mission when step i) is reached.
  • a mission involves driving a route from at least one starting point to at least one Represents the destination point across a traffic focal point.
  • the real-time traffic data contain infrastructure information.
  • the device is set up to use an optimization algorithm when reproducing the traffic situation in the simulation environment in order to minimize deviations between the simulation environment and the real-time traffic data.
  • the traffic flow metric contains an average speed during the mission.
  • infrastructure information e.g. information on traffic light switching, lane regulation or the like
  • Another independent subject relates to a lead vehicle for optimizing a traffic flow by regulating the driving behavior of vehicles driving behind the lead vehicle in a region, the at least one lead vehicle being an autonomously driving vehicle, the at least one lead vehicle having an algorithm controlling the lead vehicle having, which is trained according to the method described above.
  • the at least one lead vehicle has means for receiving infrastructure information from an infrastructure controller, the lead vehicle being set up to adapt its driving behavior to the received infrastructure information.
  • Another independent subject matter relates to a computer program product with a computer-readable storage medium on which instructions are embedded which, when executed by at least one computing unit, have the effect that the at least computing unit is set up to carry out the method of the aforementioned type.
  • the method can be carried out on one or more processing units distributed so that certain method steps are carried out on one processing unit and other process steps are carried out on at least one other processing unit, with calculated data being able to be transmitted between the processing units if necessary.
  • Another independent subject matter relates to a motor vehicle with a computer program product of the type described above.
  • FIG. 3 shows a road map of a real existing application area
  • FIG. 4 shows a flow chart of a training method.
  • Fig. 1 shows a motor vehicle 2, which is set up for automated or autonomous driving.
  • the motor vehicle 2 is provided as a guide vehicle for regulating traffic at traffic focal points.
  • the control unit 4 is connected, on the one hand, to a number of environmental sensors that allow the current position of the motor vehicle 2 and the respective traffic situation to be recorded. These include environmental sensors 10, 11 at the front of the motor vehicle 2, environmental sensors 12, 13 at the rear of the motor vehicle 2, a camera 14 and a GPS module 16.
  • the environmental sensors 10 to 13 can, for example, radar, lidar and / or Include ultrasonic sensors.
  • the computing unit 6 has loaded the computer program product stored in the memory 8 and executes it. On the basis of an algorithm and the input signals, the computing unit 6 decides on the control of the motor vehicle 2, which the computing unit 6 would achieve by intervening in the steering 22, engine control 24 and brakes 26, which are each connected to the control unit 4.
  • Data from sensors 10 to 18 are continuously temporarily stored in memory 8 and discarded after a predetermined period of time so that these environmental data can be available for further evaluation.
  • the algorithm was trained according to the method described below.
  • FIG. 2 shows a computer program product 28 with a computer program product module 30.
  • the computer program product module 30 has a self-learning neural network 32 that trains an algorithm 34.
  • the self-learning neural network 32 learns according to methods of reinforcement learning, d. H.
  • the algorithm 34 By varying the algorithm 34, the neural network 32 tries to obtain rewards for improved behavior in accordance with one or more metrics or standards, that is to say for improvements to the algorithm 34.
  • the algorithm 34 can essentially consist of a complex filter with a matrix of values, usually called weights by those skilled in the art, that define a filter function that determines the behavior of the algorithm 34 as a function of input variables that are presently recorded by the environmental sensors 10 to 18 are determined and control signals for controlling the motor vehicle 2 are generated.
  • the computer program product module 30 can be used both in the motor vehicle 2 and outside the motor vehicle 2. It is thus possible to train the computer program product module 30 both in a real environment and in a simulation environment. In particular, according to the teaching described here, the training begins in a simulation environment, since this is safer than training in a real environment.
  • the computer program product module 30 is set up to set up and evaluate a metric that is to be improved.
  • such a metric is, for example, an average speed for passing a traffic focal point, i.e. an area in which the traffic flows particularly poorly, at least at certain times.
  • the motor vehicle 2 not only regulates the speed of the following vehicles 42, 44 and 46, but also the other behavior, in particular the braking behavior of the corresponding vehicles 42, 44 and 46, for example by limiting a maximum deceleration of the motor vehicle 2, that is to say by especially Gentle driving. This can reduce the likelihood that the column of vehicles 42, 44 and 46 will brake too hard and the flow of traffic will come to a standstill as a result.
  • the computer program product module contains the algorithm to be trained and a self-learning neural network.
  • a simulation environment is then made available on the basis of real map data.
  • the simulation environment can also contain other road users and their missions.
  • a mission is determined in the simulation environment.
  • the mission can be the driving of a specific route from a starting point to a destination point through a traffic focus.
  • the simulation is then carried out and an average speed is determined.
  • the average speed is compared with an average speed to be achieved as a traffic flow metric.
  • the process can be repeated using other missions and the algorithm can be used more universally.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Health & Medical Sciences (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Automation & Control Theory (AREA)
  • Biophysics (AREA)
  • Mathematical Physics (AREA)
  • Life Sciences & Earth Sciences (AREA)
  • Computational Linguistics (AREA)
  • Data Mining & Analysis (AREA)
  • General Health & Medical Sciences (AREA)
  • Molecular Biology (AREA)
  • Computing Systems (AREA)
  • General Engineering & Computer Science (AREA)
  • Biomedical Technology (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Business, Economics & Management (AREA)
  • Game Theory and Decision Science (AREA)
  • Aviation & Aerospace Engineering (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé pour entraîner au moins un algorithme destiné à un appareil de commande d'un véhicule à moteur par un réseau neuronal à auto-apprentissage, le procédé comprenant les étapes suivantes consistant à : fournir un environnement de simulation comprenant des données cartographiques d'une région d'utilisation réellement existante, le comportement du véhicule à moteur étant défini par un ensemble de règles ; fournir des données de trafic en temps réel de la région d'utilisation réellement existante et régler la situation de conduite dans l'environnement de simulation ; fournir au véhicule à moteur une mission dans laquelle le véhicule à moteur circule à l'avant d'au moins un autre véhicule à moteur simulé ; mettre en oeuvre une simulation de la mission dans l'environnement de simulation ; déterminer la mesure de flux de trafic de la mission ; lorsque la mesure de flux de trafic est inférieure à une valeur seuil, modifier l'au moins un algorithme et/ou l'au moins un ensemble de règles et réitérer la mission ; ou lorsque la mesure de flux de trafic dépasse la valeur seuil, classer la mission comme réussie.
EP21704766.1A 2020-02-17 2021-02-10 Procédé pour entraîner au moins un algorithme destiné à un appareil de commande d'un véhicule à moteur, procédé pour optimiser un flux de trafic dans une région, produit-programme d'ordinateur et véhicule à moteur Pending EP4107590A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102020201931.2A DE102020201931A1 (de) 2020-02-17 2020-02-17 Verfahren zum Trainieren wenigstens eines Algorithmus für ein Steuergerät eines Kraftfahrzeugs, Verfahren zur Optimierung eines Verkehrsflusses in einer Region, Computerprogrammprodukt sowie Kraftfahrzeug
PCT/EP2021/053181 WO2021165113A1 (fr) 2020-02-17 2021-02-10 Procédé pour entraîner au moins un algorithme destiné à un appareil de commande d'un véhicule à moteur, procédé pour optimiser un flux de trafic dans une région, produit-programme d'ordinateur et véhicule à moteur

Publications (1)

Publication Number Publication Date
EP4107590A1 true EP4107590A1 (fr) 2022-12-28

Family

ID=74591993

Family Applications (1)

Application Number Title Priority Date Filing Date
EP21704766.1A Pending EP4107590A1 (fr) 2020-02-17 2021-02-10 Procédé pour entraîner au moins un algorithme destiné à un appareil de commande d'un véhicule à moteur, procédé pour optimiser un flux de trafic dans une région, produit-programme d'ordinateur et véhicule à moteur

Country Status (4)

Country Link
EP (1) EP4107590A1 (fr)
CN (1) CN115136081A (fr)
DE (1) DE102020201931A1 (fr)
WO (1) WO2021165113A1 (fr)

Families Citing this family (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102022106338A1 (de) 2022-03-18 2023-09-21 Joynext Gmbh Anpassen eines Fahrverhaltens eines autonomen Fahrzeugs
DE102022113744A1 (de) 2022-05-31 2023-11-30 ASFINAG Maut Service GmbH Verfahren zum Erzeugen eines Datensatzes

Family Cites Families (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102017200180A1 (de) 2017-01-09 2018-07-12 Bayerische Motoren Werke Aktiengesellschaft Verfahren und Testeinheit zur Bewegungsprognose von Verkehrsteilnehmern bei einer passiv betriebenen Fahrzeugfunktion
DE102017212166A1 (de) 2017-07-17 2019-01-17 Audi Ag Verfahren zum Betrieb von Start-Stopp-Systemen in Kraftfahrzeugen und Kommunikationssystem
DE102017007136A1 (de) 2017-07-27 2019-01-31 Opel Automobile Gmbh Verfahren und Vorrichtung zum Trainieren selbstlernender Algorithmen für ein automatisiert fahrbares Fahrzeug
DE102017007468A1 (de) 2017-08-08 2018-04-19 Daimler Ag Verfahren zum Betrieb eines Fahrzeugs
DE102018216719A1 (de) * 2017-10-06 2019-04-11 Honda Motor Co., Ltd. Schlüsselbildbasierter autonomer Fahrzeugbetrieb
CN109709956B (zh) * 2018-12-26 2021-06-08 同济大学 一种自动驾驶车辆速度控制多目标优化的跟驰算法

Also Published As

Publication number Publication date
WO2021165113A1 (fr) 2021-08-26
CN115136081A (zh) 2022-09-30
DE102020201931A1 (de) 2021-08-19

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Owner name: STELLANTIS AUTO SAS