WO2023099066A1 - Simulation pour valider une fonction d'automatisation de la conduite d'un véhicule - Google Patents

Simulation pour valider une fonction d'automatisation de la conduite d'un véhicule Download PDF

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Publication number
WO2023099066A1
WO2023099066A1 PCT/EP2022/078537 EP2022078537W WO2023099066A1 WO 2023099066 A1 WO2023099066 A1 WO 2023099066A1 EP 2022078537 W EP2022078537 W EP 2022078537W WO 2023099066 A1 WO2023099066 A1 WO 2023099066A1
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WO
WIPO (PCT)
Prior art keywords
road users
paths
trajectories
road
database
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Application number
PCT/EP2022/078537
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German (de)
English (en)
Inventor
Nico Weber
Christoph THIEM
Ulrich Eberle
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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.)
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Publication date
Application filed by Psa Automobiles Sa filed Critical Psa Automobiles Sa
Publication of WO2023099066A1 publication Critical patent/WO2023099066A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01C—MEASURING DISTANCES, LEVELS OR BEARINGS; SURVEYING; NAVIGATION; GYROSCOPIC INSTRUMENTS; PHOTOGRAMMETRY OR VIDEOGRAMMETRY
    • G01C21/00—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00
    • G01C21/26—Navigation; Navigational instruments not provided for in groups G01C1/00 - G01C19/00 specially adapted for navigation in a road network
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring

Definitions

  • the invention relates to a method for adapting, verifying or validating an automated driving function for a vehicle.
  • DE 10 2019 219 241 A1 relates to a computer-implemented method for creating a road user algorithm for computer simulation of road users, the road users belonging to a class of poorly protected road users, with data from a plurality of different, real road users of the class in a real traffic environment using an sensors attached to the road users are detected during the implementation of at least one mission, with the data being used to determine movement trajectories of the road users, with the movement trajectories being used to calculate an average movement trajectory for the mission and bandwidths for deviations from the average movement trajectory.
  • movement trajectories of road users are recorded, with these being detected by sensors that are attached to the road users.
  • the observed road users are typically pedestrians, motorcyclists and cyclists.
  • the recorded data is used to train an algorithm for the control unit of an autonomous vehicle.
  • An environment based on real map data is simulated for training purposes.
  • US 2021/0155266 A1 further relates to an object trajectory prediction system of a vehicle, comprising: one or more sensors configured to generate sensory data corresponding to one or more objects within a region of the vehicle; one or more processors; and memory coupled to the one or more processors and containing instructions that when executed by the one or more processors cause the one or more processors to: identify a first person of interest , POI) based on the sensory data; Estimating a three-dimensional (3D) pose of the first POI from the sensory ones Data; calculating a trajectory of the first POI according to at least the 3D posture of the first POI; and determining a navigation path of the vehicle according to the trajectory of the first POI.
  • POI first person of interest
  • 3D three-dimensional
  • DE 10 2017 217 056 A1 also relates to a method for operating a driver assistance system of a motor vehicle, in which a movement of at least one living object in an area surrounding the motor vehicle is predicted, comprising the following steps: a) storing movement models, with a respective movement model being one of describes a change in the movement of the living object that is dependent on at least one further object, the living object and the at least one further object each belonging to an object class and the movement models for combinations of the object classes being stored, b) receiving measurement data relating to the surroundings of the motor vehicle; c) detecting the at least one living object and the at least one other object in the area surrounding the motor vehicle and determining a relative position of the objects to one another on the basis of the measurement data received; d) identifying the object classes of the detected objects; e) for the at least one detected living object: i.
  • EP 3 621 052 A1 also relates to a method for analyzing the driving behavior of motor vehicles, including autonomous vehicles, comprising the following method steps: detecting at least one vehicle on a predefined route section, determining the outer contour of the at least one detected vehicle and deriving a 3D model of the detected vehicle from the outer contour, recording the trajectory of the at least one detected vehicle and providing trajectory data for the vehicle, and creating a vehicle model of the at least one detected vehicle for later simulation of the at least one vehicle in a simulation environment, based on at least 3D model and the trajectory data.
  • DE 10 2017 217 443 A1 relates to a method for providing training data for machine learning for a control model of an automatic vehicle control; in which a vehicle is controlled along a trajectory; where through Sensors of the vehicle sensor data sets are recorded; wherein the sensor data records include position data and time data as well as control data of the vehicle; and based on the sensor data sets, transmission data are generated and transmitted to a processing unit external to the vehicle; Additional information is recorded by the vehicle-external processing unit based on the transmission data; and the training data are generated using the transmission data and the additional information; classification information being assigned to the training data; and the training data are stored with the classification information associated with them.
  • DE 10 2016 220 308 A1 relates to a method for creating a digital road model for at least one road section, comprising: receiving at least one trajectory of a vehicle for the at least one road section in a vehicle-external database, receiving at least one image, which contains at least parts of the at least one Road section shows, the image having a perspective that corresponds to an image taken from an elevated position essentially vertically downwards, superimposing the at least one trajectory with the at least one image in such a way that the at least one trajectory corresponds to the course of a road in the at least matches an image, analyzing the at least one image in a corridor that extends along and encloses the trajectory, and recognizing driving- or positioning-relevant features of the road segment in the corridor, generating the digital road model from the driving- or positioning-relevant features that in the at least one image aligned using the at least one trajectory and in the corridor enclosing the trajectory.
  • a first aspect of the invention relates to a method, in particular a computer-implemented method, for adapting, verifying or validating an automated driving function for a vehicle, having the steps:
  • Simulation-based methods are used to support the development and validation of automated vehicles and driving functions, especially in complex operating areas such as within a traffic jam or urban traffic areas.
  • B. Scenario-based testing can already use the digital twin to assess which traffic situations are relevant or even critical.
  • the selected actually existing traffic section is in particular part of a Urban area, but can also be outside the urban area.
  • a real existing traffic section is selected that is particularly relevant for the adaptation, verification or validation of the automated driving function.
  • the automated driving function of a vehicle for example a driver assistance system or a module for a highly automated driving control function of the vehicle, often actively intervenes in the safety-relevant driving control of the vehicle, it must be ensured that this driving function masters a large number of possible scenarios and functions correctly. This includes, for example, correct reaction by applying the brakes, by accelerating, by steering, etc.; the output variables of the driving control function are, in particular, actuator commands in order to operate, for example, a steering system, a brake, an accelerator pedal or other functions of the vehicle.
  • the driving function that intervenes is safety-critical and must therefore be designed with a correspondingly high level of safety. Therefore, such driving functions of automated vehicles are tested particularly in complex operating areas. This applies to scenarios such as reaching the end of a traffic jam or driving through busy urban traffic sections.
  • a highly accurate and realistic simulation is generated in order to simulate such challenging areas and to use the simulation to either adapt the driving function or to finally verify or validate it.
  • the use of the digital twin of the real vehicle therefore not only allows a damage-free test strategy for evaluating the driving function, it can also be used to assess which traffic situations and traffic sections are relevant or critical with regard to the functionality of the driving function.
  • the important entities such as other road users and local conditions (for example the marking of a pedestrian crossing or the presence of a traffic light) are therefore advantageously modeled with high precision in the simulation according to the invention for real scenarios.
  • the simulation therefore advantageously includes not only the image of the driving function, which is also carried out when the simulation is run, but also a dynamic environment in which not only static road users are present, but also, in the sense of an agent model, also includes the changes in behavior and interactions between the road users .
  • the social forces model is used for this purpose, in particular to model the movement (caused by the tendency of road users to move away from one another or from objects or to seek proximity to one another or to objects).
  • the categorization of road users and elements of the road section advantageously allows properties that are also stored in the respective database to be used for the respective previously saved entries in order to obtain a more precise simulation.
  • the social forces model is an abstract analogue of electrical charges, which can either repel or attract each other depending on their charge.
  • An example of this is the behavior between mother and child, which leads to an attractive movement tendency towards one another.
  • Cyclists and trucks on the other hand, have a repulsive tendency, since the truck basically poses a danger to the cyclist, so that the cyclist will try to avoid the immediate vicinity of the truck, at least for a longer period of time.
  • each road user is advantageously viewed as an independent agent who reacts to the behavior of other road users (in the sense of other agents) and whose own behavior in turn influences the behavior of other road users.
  • the social forces model can also be applied to objects such as obstacles or logical units such as possible targets for road users.
  • Road users and their movements are recorded on the actually existing traffic section. This is preferably done by an unmanned aircraft and a camera unit arranged on it. For this purpose, the camera data is post-processed and analyzed with appropriate algorithms for machine vision.
  • the road users are categorized into predetermined groups, for example pedestrians, cyclists, cars, trucks, agricultural vehicles, construction site vehicles, etc.; When capturing the movements of the road users on the actually existing traffic section, at least the movement paths of the road users are recorded, but in particular the trajectories as well.
  • paths and trajectories are purely geometric in nature and indicate a history of the swept locations, while a trajectory also contains time information, ie each location is also assigned a point in time, from which the speed of the road users and for each point in time a relative position of the road users among each other can be specified.
  • This makes it possible to generate a data set with road user-specific individual trajectories in the form of multi-variant time series.
  • the position-time profile for example, there is also the time profile of the orientation in the space of the respective road user before.
  • the data on the movements of road users determined from reality can be augmented with synthetically generated data from other tests or completely freely generated data.
  • the granularity of the present data set can be completely recorded by the digital model, so that a significant increase in the degree of reality can be achieved compared to established vehicle following models.
  • the highly realistic representation of road users as surrounding traffic allows statements about the implications of automated vehicles in future mixed traffic scenarios when several automated vehicles are used during a test case execution.
  • a preferred field of application of the invention is therefore in the area of commercial traffic, particularly in a city area such as e.g. B. fully automated delivery and courier services, robotic taxis or public transport, or the like.
  • the first database and the second database are respective data sets of a common database.
  • elements of the traffic segment are categorized by performing a similarity analysis, so that elements of the traffic segment are the same within certain similarity limits Entries of the first database are assigned.
  • the road users are categorized by performing a similarity analysis, so that road users on the road section are assigned to the same entries in the second database within certain similarity limits.
  • road users and their paths, in particular trajectories are detected using an unmanned aerial vehicle on which the sensor unit for detecting road users and their paths, in particular trajectories, is arranged.
  • the unmanned aircraft is preferably hovered at a predetermined height above the selected traffic section in order to be able to detect the road users and their movements with the sensor unit.
  • An optical sensor is preferably used, for example a camera, a stereo camera or the like;
  • the unmanned aircraft is preferably placed somewhat next to the traffic section in order to minimize the risk for road users in the event of an error.
  • Quadrocopters or other aircraft that are particularly suitable for hovering can be used.
  • the detected paths, in particular the trajectories, of the road users are combined into a reduced, relevant number of trajectories.
  • recorded paths, in particular trajectories, of road users within a respective category are reduced to paths, in particular trajectories, with common patterns.
  • An example of such a grouping on paths or trajectories with common patterns are cyclists who take paths with very similar locations on a cycle path, the similarity being determined in particular by comparing a limit value with the sum of deviations from a standardized path, in particular trajectories becomes.
  • recorded paths, in particular trajectories, of road users are reduced to paths, in particular trajectories, with common patterns via different categories.
  • An example of such a grouping of paths or trajectories of road users of different categories is the meeting of vehicles and pedestrians on a pedestrian crossing.
  • such a scenario leads to one and the same outcome, namely that the vehicle stops in front of the pedestrian crossing to give the pedestrian priority.
  • one and the same behavior pattern of road users from different categories does not have to be modeled again for the simulation, but can also be reduced to similar reactions between the two.
  • the options mentioned in the two previous embodiments for implementing the combination of the detected paths, in particular trajectories, of the road users to a reduced, relevant number of paths or trajectories can be carried out using various methods and dissimilarity metrics. Examples include a rule-based approach using the metric aggregates via synchronous elements or clustering methods from the field of unsupervised machine learning using the metric dynamic time warping. The choice of the above options determines the type of result of this process step in the form of relevant agent trajectories.
  • a non-linear optimization method is used to create the digital model in order to minimize the difference between the movements of the detected road users and their paths, in particular trajectories, and the movements of the simulation modeled by the social forces model.
  • a power algorithm I can be used to train the digital model based on a previously defined validity criterion until the required degree of reality is reached.
  • a genetic algorithm from the group of stochastic optimization methods is mentioned here as an example, which minimizes the deviation between real trajectories and modeled road user trajectories using a cost function based on the Euclidean distance.
  • a highly realistic model of traffic agents (the modeled other road users) is thus advantageously obtained.
  • These highly realistic agent models allow a very precise, real data-based modeling of the other road users to provide plausible input signals when testing the driving function.
  • the recorded paths, in particular trajectories, of the road users are supplemented by synthetic paths, in particular synthetic trajectories, of further hypothetical road users.
  • the social forces model is extended by additional synthetic repulsive or attractive forces.
  • Fig. 2 A traffic segment, which is used in the method according to Fig. 1.
  • FIG. 1 shows a method, in particular a computer-implemented method, for the optionally selected adaptation, verification or validation of an automated driving function for a vehicle, having the steps:
  • FIG. 2 An exemplary traffic section is shown in FIG. 2, for which the method according to FIG. 1 is carried out.
  • the traffic section is an intersection area.
  • other road users 3 are shown, as well as a pedestrian crossing as a structural element 1 of the traffic section.
  • the traffic section is observed by an unmanned aerial vehicle 5 with a sensor unit 7 in order to record the movements of road users 3 .

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  • Engineering & Computer Science (AREA)
  • Radar, Positioning & Navigation (AREA)
  • Remote Sensing (AREA)
  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Quality & Reliability (AREA)
  • General Engineering & Computer Science (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé d'adaptation, de vérification ou de validation d'une fonction d'automatisation de la conduite d'un véhicule, comprenant les étapes suivantes : sélection (S1) d'un tronçon routier existant réellement, catégorisation (S2) d'éléments d'aménagement et situationnels (1) du tronçon routier, détection (S3) d'usagers de la route (3) et de parcours, en particulier de trajectoires, des usagers de la route (3), la détection des usagers de la route (3) et de leurs parcours s'effectuant au moyen d'une unité de détection externe (7), catégorisation (S4) des usagers de la route (3) détectés, création (S5) d'un modèle numérique du tronçon routier existant réellement, les mouvements des usagers de la route (3) étant décrits dans le modèle numérique par l'intermédiaire d'un modèle de forces sociales, et exécution (S6) d'une simulation.
PCT/EP2022/078537 2021-11-30 2022-10-13 Simulation pour valider une fonction d'automatisation de la conduite d'un véhicule WO2023099066A1 (fr)

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DE102021213538.2 2021-11-30
DE102021213538.2A DE102021213538A1 (de) 2021-11-30 2021-11-30 Simulation zur Validierung einer automatisierenden Fahrfunktion für ein Fahrzeug

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CN117057656A (zh) * 2023-08-17 2023-11-14 广东飞翔云计算有限公司 基于数字孪生的智慧城市管理方法及系统

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CN117057656B (zh) * 2023-08-17 2024-05-31 广东飞翔云计算有限公司 基于数字孪生的智慧城市管理方法及系统

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