EP4052178A1 - Procédé d'apprentissage d'au moins un algorithme pour un dispositif de commande d'un véhicule automobile, produit programme informatique et véhicule automobile - Google Patents

Procédé d'apprentissage d'au moins un algorithme pour un dispositif de commande d'un véhicule automobile, produit programme informatique et véhicule automobile

Info

Publication number
EP4052178A1
EP4052178A1 EP20796785.2A EP20796785A EP4052178A1 EP 4052178 A1 EP4052178 A1 EP 4052178A1 EP 20796785 A EP20796785 A EP 20796785A EP 4052178 A1 EP4052178 A1 EP 4052178A1
Authority
EP
European Patent Office
Prior art keywords
motor vehicle
algorithm
simulation
computer program
program product
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
EP20796785.2A
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 EP4052178A1 publication Critical patent/EP4052178A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/50—Context or environment of the image
    • G06V20/56—Context or environment of the image exterior to a vehicle by using sensors mounted on the vehicle
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/21—Design or setup of recognition systems or techniques; Extraction of features in feature space; Blind source separation
    • G06F18/214—Generating training patterns; Bootstrap methods, e.g. bagging or boosting
    • G06F18/2155—Generating training patterns; Bootstrap methods, e.g. bagging or boosting characterised by the incorporation of unlabelled data, e.g. multiple instance learning [MIL], semi-supervised techniques using expectation-maximisation [EM] or naïve labelling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/70—Arrangements for image or video recognition or understanding using pattern recognition or machine learning
    • G06V10/82—Arrangements for image or video recognition or understanding using pattern recognition or machine learning using neural networks

Definitions

  • a method for training at least one algorithm for a control unit of a motor vehicle, a computer program product and a motor vehicle are described here.
  • a method for training self-learning algorithms for an automated drivable vehicle is known with a predetermined automation module by generating learning situations, the learning situations being generated as follows: Carrying out a traffic simulation in which a virtual Ego- Vehicle with the automation module of the real vehicle is set in a virtual scenario, the scenario comprising a route structure with a specified route, furthermore, further virtual moving objects generated comprehensively in an automated manner with individually specifiable object properties and behavior models, with the objects independently and with each other in the course of the ongoing simulation interact adaptively on the basis of the respective object properties and behavior models, - Carrying out a driving dynamics simulation on the basis of the automation module as well as virtual sensor signals of the moving objects of a virtual sensor system assigned to the ego vehicle, which corresponds to a sensor system of the real vehicle in which Reactions of the ego vehicle are generated, - Identification of a relevant learning situation using selection criteria that are determined on the basis of predeterminable metrics.
  • the task arises of developing methods, computer program products and motor vehicles of the type mentioned at the outset such that a trained algorithm can be better adapted to real traffic situations.
  • the object is achieved by a method for training at least one algorithm for a control unit of a motor vehicle according to claim 1, a computer program product according to the independent claim 12 and a motor vehicle according to the independent claim 13. Further refinements and developments are the subject of the dependent claims .
  • a method for training at least one algorithm for a control unit of a motor vehicle is described below, the control unit being provided for implementing an automated or autonomous driving function with intervention in units of the motor vehicle on the basis of input data using the at least one algorithm, with the algorithm is trained by a self-learning neural network, comprising 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, b) providing a simulation environment with simulation parameters , where the simulation environment contains map data of a real existing area of use, the motor vehicle and at least one other simulated road user, with a behavior of the motor vehicle as well as the few At least one other road user is determined by a rule set, the rule set containing behavior parameters determining permissible limits, c) providing a mission for the motor vehicle, d) modifying at least one behavior parameter of the motor vehicle so that the at least one behavior parameter is beyond the permissible limits, e ) Perform a simulation of the mission.
  • Corresponding behavior parameters can be, for example, the permissible speed, a distance to be observed, thresholds for exceeding the prohibition (e.g. a period within which a traffic light that changes to red can be passed, risk parameters at which a solid line can be passed, and / or when despite no right of way) may continue to be driven), a permitted variance of the position of the motor vehicle in the lane, a permitted overtaking side (in right-hand traffic only on the left or on both sides) and the like.
  • the permissible speed e.g. a period within which a traffic light that changes to red can be passed, risk parameters at which a solid line can be passed, and / or when despite no right of way
  • thresholds for exceeding the prohibition e.g. a period within which a traffic light that changes to red can be passed, risk parameters at which a solid line can be passed, and / or when despite no right of way
  • a permitted variance of the position of the motor vehicle in the lane e.g. a period within
  • a corresponding mission can be, for example, to get from a starting point in the shortest possible time or in an energy-efficient manner to a specified destination.
  • a suitably trained algorithm has a different driving behavior than a conventionally trained algorithm, even within narrower parameter limits, such as would then be used for use in a real motor vehicle.
  • the driving behavior of such an algorithm is more natural, so it corresponds more closely to the driving behavior of a person, which on the one hand benefits the occupants and on the other hand has a more natural effect on other road users.
  • An example of this is the passing of a delivery vehicle parked in the second row with the necessary crossing of a solid line.
  • An algorithm that was absolutely compliant with the rules would stop the motor vehicle and wait for the delivery vehicle to continue.
  • An algorithm trained in accordance with the method described here which may exceed applicable rules within narrow limits, e.g. if this is possible without risk due to the lack of oncoming traffic, continues at such a point.
  • the neural network learns through reinforcement learning processes (also known as the RFL algorithm.
  • RFL stands for: "reinforcement learning”
  • reinforcement learning also known as the RFL algorithm.
  • a further metric can be that it is not an indirect trigger of accidents with other road users, e.g. through sudden, unexpected hard braking.
  • the neural network learns better and better strategies for completing a given mission in successive simulations.
  • the at least one abnormal road user is a motor vehicle, motorcyclist or pedestrian.
  • the vehicles are driven by people, some of whom do not comply with the rules.
  • a simulation with such abnormally behaving road users is therefore particularly realistic.
  • the computer program product module has an algorithm that has already been trained with road users that conform to the rules.
  • the at least one behavior parameter is exceeded or fallen short of by a predetermined percentage.
  • the simulation is repeated several times, with at least one simulation parameter being changed in each case.
  • Such a simulation parameter can be a behavior parameter, for example.
  • a behavior parameter for example.
  • the variation can take place, for example, through modifications within the same traffic area (e.g. by changing road widths, right of way, traffic lights, road blocks, etc.) or by changing the traffic area as a whole.
  • At least one behavior parameter is varied.
  • Such behavior parameters cover the usual driving behavior of different types of drivers, for example drivers who tend to drive too fast, drivers whose driving precision is less, etc.
  • provision can be made for the number, positioning and / or missions of the other road users to be varied.
  • the algorithm is further trained by a self-learning neural network, comprising the following steps: a) Provision of the 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 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 and at least one other simulated road user, the other simulated road user being simulated by an algorithm that was trained according to the method described above, c) providing a mission for the motor vehicle, d) performing a simulation of the mission.
  • a self-learning neural network comprising the following steps: a) Provision of the 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 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 and at least one other simulated road user, the other simulated road user being simulated by an algorithm
  • the algorithm can also be applied to other road users, for example pedestrians or cyclists, in which case no computer program product module for an automated or autonomous driving function, but a computer program product module for a movement behavior simulation is used.
  • these agents can be designed more realistically and then used in future training missions of the type described above, which increases the quality of the simulation.
  • a first independent subject 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 aggregates 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 following being provided: a) means for 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, b) means to provide a simulation environment with simulation parameters, the simulation environment map data of a real area of use, the motor vehicle and at least one other si- contains mulated road user, a behavior of the motor vehicle and the at least one other road user is determined by a rule set, the rule set containing permissible limits defining behavior parameters, c) means for providing a mission for the motor vehicle, d) means for modifying at least a behavior parameter of the motor vehicle, so that the at least one behavior parameter lies beyond the permissible limits, e)
  • the neural network has means for learning through reinforcing learning processes, with at least one of the time to complete the mission and / or the number of accidents in which the motor vehicle is involved as the reward metric, during the mission, means are provided for repeating the simulation so that the simulation is repeated until a minimum metric is reached.
  • the at least one abnormal road user is a motor vehicle, motorcycle or pedestrian.
  • the computer program product module has an algorithm that has already been trained with road users that conform to the rules.
  • provision can be made for means to be provided for exceeding or falling below the at least one behavior parameter by a predetermined percentage.
  • provision can be made for means for varying the number, positioning and / or missions of the other road users to be provided.
  • means are provided for training the algorithm by a self-learning neural network, the following being provided: a) means for providing the computer program product module for the automated or autonomous driving function, the computer program product module providing the algorithm to be trained and the self-learning neural network contains, b) means for providing a simulation environment with simulation parameters, wherein the simulation environment contains map data of a real existing operational area, the motor vehicle and at least one other simulated road user, the other simulated road user being simulated by an algorithm, which has been trained according to the method described above, c) means for providing a mission for the motor vehicle, d) means for performing a simulation of the mission.
  • 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 processing unit, have the effect that the at least one processing unit is set up to execute the method of the type described above.
  • the method can be carried out distributed over one or more processing units, so that certain method steps are carried out on the one processing unit and other process steps are carried out on at least one further processing unit, with calculated data can 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. 1 shows a motor vehicle which is set up for automated or autonomous driving
  • FIG. 2 shows a computer program product for the motor vehicle from FIG. 1;
  • FIG. 3 shows a simulation environment for the motor vehicle from FIG. 1, and FIG. 4 shows a flow chart of the method.
  • Fig. 1 shows a motor vehicle 2, which is set up for automated or autonomous driving.
  • the motor vehicle 2 has a control device 4 with a computing unit 6 and a memory 8.
  • a computer program product is stored in the memory 8 and is described in more detail below in connection with FIGS.
  • the control unit 4 is connected, on the one hand, to a number of environmental sensors which 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 ultrasonic sensors.
  • sensors for detecting the state of the motor vehicle 2 are provided, including wheel speed sensors 16, acceleration sensors 18 and pedal sensors 20, which are connected to the control unit 4. With the aid of this motor vehicle sensor system, the current state of motor vehicle 2 can be reliably detected.
  • 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 20 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.
  • known learning methods of supervised and unsupervised learning as well as combinations of these learning methods can be used.
  • 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 20 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 a metric that is to be improved.
  • a metric can, for example, be a time until reaching a predetermined mission, for example a destination. If the metric has exceeded a certain threshold, e.g. a time less than a limit time, the metric can be considered fulfilled and the algorithm can be frozen in this regard. It can then either be optimized with regard to another metric and trained further, or the algorithm can be tested in a real environment.
  • FIG. 3 shows a simulation environment 36 for motor vehicle 2 from FIG. 1.
  • a road intersection 38 is provided in the simulation environment 36, at which a road 40 and a road 42 intersect.
  • the intersection 38 is based on real existing map data, so that the behavior of the algorithm 34 at this intersection 38 is simulated specifically.
  • a motor vehicle 44 is parked at the edge of the road 40 in such a way that it is not possible to drive past without crossing a solid line 46.
  • a motorcyclist 48 would like to turn from road 42 into road 40.
  • a pedestrian 50 moves without paying attention to the traffic at high speed in the direction of movement 52 towards the street 40 in order to apparently cross it.
  • the algorithm 34 has to make a large number of complex decisions.
  • the first decision that has to be made is whether the solid line 46 may be crossed at all. Since it is not possible to pass the parked motor vehicle 44 without crossing the solid line 46, the decision of the algorithm 34 will have to be answered with yes, but the question arises as to the driving parameters with which. For this purpose, the algorithm 34 must make a prediction of how the motorcyclist 48 will behave, possibly on his normal trajectory would come relatively close to the motor vehicle 2. In everyday life, however, it is often the case that corresponding motorcyclists can easily evade or drive further to the right in their lane due to the small width of the motorcycle and the low speed in the intersection area.
  • the speed of the motor vehicle 2 must be taken into account. If the motor vehicle 2 accelerates slightly in order to overtake the parked motor vehicle 44, the probability that the motor vehicle 2 will impair the planned trajectory of the motorcyclist 48 is reduced. However, this could lead to the motor vehicle 2 crossing the trajectory of the possibly inattentive pedestrian 50 who is just about to cross the street 40, which could result in an accident.
  • the algorithm 34 could therefore first try to pass the motor vehicle 44 without stopping.
  • the motor vehicle 2 could first of all increase its speed above the permitted maximum speed in order to pass the motor vehicle 44. However, this could lead to a minimum distance between the pedestrian 50 and the motor vehicle 2 being undershot.
  • the algorithm 34 could move the motor vehicle 2 more slowly, which, however, could pose a danger to the motorcyclist 48.
  • the algorithm 34 could then initially accelerate the motor vehicle 2 to pass the parked motor vehicle 44 and then brake it again.
  • This solution is to be preferred because on the one hand it enables the parked motor vehicle 44 to be passed and the present mission to be completed quickly and, on the other hand, it optimizes the metrics of the endangerment of other road users 48, 50.
  • the computer program product module is made available.
  • 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. In addition to roads and certain rules, the simulation environment can also contain other road users and their missions.
  • a set of rules for the ego vehicle can be varied, which contains rules of behavior, for example maintaining speeds, driving over solid lines, position in the lane, etc.
  • the simulation can then be carried out, using reinforcement learning methods to attempt to achieve individual metrics. As long as this is not the case, the strategy or the algorithm is varied and the simulation is carried out again until a certain individual metric is reached. This process is repeated for all metrics.
  • the rule set of the ego vehicle is varied and the process is repeated until the algorithm has matured sufficiently.
  • the algorithm can then be frozen.
  • the algorithm can be used, for example, in traffic simulations for simulated vehicles other than that of the motor vehicle to be trained.
  • the method can also be applied to other road users.
  • the training can be continued in a real environment that is fully or mixed real.

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Abstract

L'invention concerne un procédé d'entraînement d'au moins un algorithme pour un dispositif de commande d'un véhicule automobile, ledit algorithme étant entraîné au moyen d'un réseau neuronal à auto-apprentissage. Le procédé comprend les étapes suivantes : a) la fourniture d'un module de produit programme informatique pour une fonction d'entraînement automatisée ou autonome, b) la fourniture d'un environnement de simulation avec des paramètres de simulation, l'environnement de simulation contenant des données cartographiques d'une zone de fonctionnement actuelle existante, le véhicule automobile et au moins un autre participant au trafic simulé, et le comportement du véhicule automobile et du ou des participants au trafic supplémentaire étant déterminé par un ensemble de règles ayant des paramètres de comportement, c) la fourniture d'une mission pour le véhicule automobile, d) la modification d'au moins un paramètre de comportement du véhicule automobile de telle sorte que le ou les paramètres de comportement se trouvent dans des limites admissibles à tout moment, et e) la réalisation d'une simulation de la mission.
EP20796785.2A 2019-10-31 2020-10-22 Procédé d'apprentissage d'au moins un algorithme pour un dispositif de commande d'un véhicule automobile, produit programme informatique et véhicule automobile Pending EP4052178A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102019216836.1A DE102019216836A1 (de) 2019-10-31 2019-10-31 Verfahren zum Trainieren wenigstens eines Algorithmus für ein Steuergerät eines Kraftfahrzeugs, Computerprogrammprodukt sowie Kraftfahrzeug
PCT/EP2020/079764 WO2021083785A1 (fr) 2019-10-31 2020-10-22 Procédé d'apprentissage d'au moins un algorithme pour un dispositif de commande d'un véhicule automobile, produit programme informatique et véhicule automobile

Publications (1)

Publication Number Publication Date
EP4052178A1 true EP4052178A1 (fr) 2022-09-07

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EP20796785.2A Pending EP4052178A1 (fr) 2019-10-31 2020-10-22 Procédé d'apprentissage d'au moins un algorithme pour un dispositif de commande d'un véhicule automobile, produit programme informatique et véhicule automobile

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EP (1) EP4052178A1 (fr)
CN (1) CN114667545A (fr)
DE (1) DE102019216836A1 (fr)
WO (1) WO2021083785A1 (fr)

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CN113345227B (zh) * 2021-05-31 2022-09-20 上海涵润汽车电子有限公司 一种随机交通流生成方法及装置
CN113392467A (zh) * 2021-06-15 2021-09-14 广东洪裕智能制造研究院有限公司 一种基于hlath_rti的汽车零部件复杂产品协同仿真平台
DE102021206737A1 (de) * 2021-06-29 2022-12-29 Psa Automobiles Sa Auslegen eines Automatisierungsalgorithmus eines Fahrzeugs
CN114333493B (zh) * 2021-12-31 2022-10-14 江苏普旭科技股份有限公司 驾驶模拟器的人感仿真系统与方法
DE102022208519A1 (de) 2022-08-17 2024-02-22 STTech GmbH Computerimplementiertes Verfahren und Computerprogramm zur Bewegungsplanung eines Ego-Fahrsystems in einer Verkehrssituation, computerimplementiertes Verfahren zur Bewegungsplanung eines Ego-Fahrsystems in einer realen Verkehrssituation Steuergerät für ein Ego-Fahrzeug
CN116424332B (zh) * 2023-04-10 2023-11-21 重庆大学 深度强化学习型混合动力汽车能量管理策略增强更新方法
CN117246345A (zh) * 2023-11-06 2023-12-19 镁佳(武汉)科技有限公司 一种生成式车辆控制方法、装置、设备及介质

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Publication number Priority date Publication date Assignee Title
DE102017007136A1 (de) * 2017-07-27 2019-01-31 Opel Automobile Gmbh Verfahren und Vorrichtung zum Trainieren selbstlernender Algorithmen für ein automatisiert fahrbares Fahrzeug
DE102018217004A1 (de) * 2017-10-12 2019-04-18 Honda Motor Co., Ltd. Autonome Fahrzeugstrategiegenerierung
WO2019191306A1 (fr) * 2018-03-27 2019-10-03 Nvidia Corporation Apprentissage, test et vérification de machines autonomes à l'aide d'environnements simulés

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DE102019216836A1 (de) 2021-05-06
CN114667545A (zh) 2022-06-24

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