EP4073778A1 - Procédé de création d'un algorithme d'usager de la route permettant la simulation informatique d'usagers de la route, procédé de formation d'au moins un algorithme pour une unité de commande d'un véhicule automobile, produit programme informatique et véhicule automobile - Google Patents

Procédé de création d'un algorithme d'usager de la route permettant la simulation informatique d'usagers de la route, procédé de formation d'au moins un algorithme pour une unité de commande d'un véhicule automobile, produit programme informatique et véhicule automobile

Info

Publication number
EP4073778A1
EP4073778A1 EP20820809.0A EP20820809A EP4073778A1 EP 4073778 A1 EP4073778 A1 EP 4073778A1 EP 20820809 A EP20820809 A EP 20820809A EP 4073778 A1 EP4073778 A1 EP 4073778A1
Authority
EP
European Patent Office
Prior art keywords
algorithm
road users
motor vehicle
traffic
data
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.)
Granted
Application number
EP20820809.0A
Other languages
German (de)
English (en)
Other versions
EP4073778B1 (fr
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 EP4073778A1 publication Critical patent/EP4073778A1/fr
Application granted granted Critical
Publication of EP4073778B1 publication Critical patent/EP4073778B1/fr
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/161—Decentralised systems, e.g. inter-vehicle communication
    • G08G1/162—Decentralised systems, e.g. inter-vehicle communication event-triggered
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/166—Anti-collision systems for active traffic, e.g. moving vehicles, pedestrians, bikes

Definitions

  • a computer-implemented method for creating a traffic user algorithm for computer simulation of traffic users a computer-implemented 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.
  • the first partially automated vehicles (corresponding to SAE Level 2 in accordance with SAE J3016) have reached series production readiness in recent years.
  • the disadvantage of the known method is that the simulation has so far been carried out with rule-compliant road users. In reality, however, it often happens that road users do not behave in accordance with the rules, e.g. drive too fast, drive over lane markings for no apparent reason, are inattentive, overtake to the right or move on non-obvious trajectories, etc.
  • the algorithm is therefore less prepared for human driving behavior. This leads to an unnatural driving behavior of a motor vehicle equipped with a suitably trained algorithm, since the motor vehicle can react less flexibly and critical situations can arise if the algorithm did not correctly anticipate the behavior of the other road user.
  • a traffic simulation is known from JP 2009 019920 A which generates a realistic route based on the prediction of pedestrian or bicycle behavior.
  • the route is optimized from the perspective of the pedestrian or the cyclist and also includes the risk of a collision with another vehicle and possibly other parameters such as weather.
  • the resulting models integrate a large number of routes, depending on the overall risk, with which real traffic conditions are simulated.
  • the task arises of computer-implemented methods for training at least one algorithm for a control unit of a motor vehicle, computer program products and motor vehicles of the type mentioned at the beginning Road user algorithm for computer simulation of road users, computer-implemented methods for training at least one algorithm for a control device of a motor vehicle, computer program products and motor vehicles of the type mentioned above to the effect that they are better set up to map real traffic conditions.
  • the object is achieved by a computer-implemented method for creating a road user algorithm for computer simulation of road users according to claim 1, a computer-implemented method for training at least one algorithm for a control unit of a motor vehicle according to the independent claim 6, a computer program product according to the independent claim 9 and a motor vehicle according to the independent claim 10. Further designs and developments are the subject of the dependent claims.
  • the following is a computer-implemented method for creating a traffic participant 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 existing road users of the class in a real traffic environment with the help of sensors attached to the road users are detected during the implementation of at least one mission, with movement trajectories of the road users being determined from the data, with an average movement trajectory for the mission and bandwidths for deviations from the average movement trajectory being calculated from the movement trajectories.
  • Badly protected road users are those whose passive safety equipment offers no, marginal or only little protection, e.g. through safety clothing and / or body-worn protectors and / or helmets.
  • This class includes, for example, pedestrians, skateboarders, roller skaters, cyclists, motorcyclists, quad riders, scooter riders, wheelchair users and the like.
  • the mission can, among other things, be a mission presented to actually existing road users, or the mission can be derived from data obtained from movement data of the road users who move between a common starting point or area and a common end point or end without an explicit mission. move area.
  • a corresponding mission can also be formulated in a more complex manner, for example to take a certain one of several possible routes or it can have intermediate destinations.
  • Possible sources can be, for example, position recording devices such as cell phones or smart watches. Many road users carry around cell phones, which are usually suitable for recording relevant data.
  • the bandwidths can cover, for example, 95 or 99% of the individual trajectories, so that only trajectories that are very far away from the mean trajectory are excluded.
  • the mean value can be determined using various known mean value methods, e.g. as a geometric mean.
  • a corresponding road user algorithm can thus generate different trajectories for a given mission within the bandwidths, which are closer to a real behavior of road users than a fixed trajectory for the same mission.
  • the differences can be achieved in a possible embodiment through various parameters.
  • the traffic user algorithm can be designed in such a way that it generates randomized trajectories within the bandwidths. Such randomized trajectories lead to different behavioral patterns in different training cycles or in different road users within a simulation that are simulated using the same algorithm. As a result, new and unpredictable situations can be created that are closer to reality than uniformly repetitive situations.
  • the traffic participants represent vehicles of a given, poorly protected vehicle class, with at least one sensor for recording data being attached to the vehicles or assigned to the vehicles, the data being evaluated by the traffic participant algorithm become.
  • sensors are often arranged on or in the corresponding motor vehicles anyway, e.g. acceleration sensors. If the data generated from this are evaluated, the number of additionally required sensors can be reduced or additional sensors can be completely avoided.
  • the data Before the data is evaluated by the traffic user algorithm, it can be converted and / or converted according to a further development so that more usable sensor data, e.g. acceleration, can be generated from raw sensor data, e.g. voltage values.
  • more usable sensor data e.g. acceleration
  • raw sensor data e.g. voltage values
  • the at least one sensor arranged on the road user or the vehicle is a camera, a GPS sensor, a lidar and / or a radar sensor.
  • At least one sensor which detects the road users is arranged in the traffic environment, the data being evaluated and incorporated into the road user algorithm.
  • a sensor arranged in the infrastructure which can be static, for example, for A traffic surveillance camera, for example, can record the entire traffic situation and thus provide additional data for checking the plausibility of the behavior of road users. In this way, among other things, anticipatory behavior of the road users concerned can be derived.
  • provision can be made for a self-learning neural network to be provided, the data being provided to the self-learning neural network, the traffic user algorithm being trained by the self-learning neural network.
  • a traffic user algorithm can be created that can react to unknown situations in a human-like manner. It is thus possible to create a universally replaceable algorithm for traffic simulations, with the aid of which a simulation of a corresponding traffic participant who belongs to a poorly protected class can be generated.
  • a first independent subject relates to a computer-implemented method 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, 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 , wherein the simulation environment contains map data of a real existing operational area, the motor vehicle and, as an agent, at least one further simulated road user, where e in the behavior of the at least one other road user is determined by a road user algorithm that was generated according to the type described above; c) providing a mission for the motor vehicle, and d) carrying out the mission and training the algorithm.
  • At least one of the further simulated road users generates a trajectory that is randomized or stochastically parameter-varied.
  • a randomized or stochastically parameter-varied trajectory is newly generated in situ for each simulation so that the behavior of the relevant road user or agent is not determined in advance. This leads to particularly complex challenges for the algorithm to be trained and to a more robust algorithm.
  • provision can be made for a plurality of further simulated road users to be provided, who move at least partly along the central trajectory and partly within the bandwidths.
  • Another independent subject relates to a device for creating a traffic user algorithm for computer simulation of road users, the road users belonging to a class of poorly protected road users, with sensors being attached to a plurality of different, real existing road users of the class, with which data is transferred to a real Traffic environment can be detected during the implementation of at least one mission, means for determining movement trajectories from the data are provided, with the means being set up to generate a mean movement trajectory from the movement trajectories. torie for the mission and bandwidths for deviations from the mean movement trajectory.
  • the traffic participants represent vehicles of a given, poorly protected vehicle class, with at least one sensor for recording data being attached to the vehicles or assigned to the vehicles, the traffic participant algorithm being designed to do this To evaluate data from the at least one sensor.
  • the at least one sensor arranged on the road user or the vehicle is a camera, a GPS sensor, a lidar and / or a radar sensor.
  • At least one sensor which detects the road users is arranged in the traffic environment, with means for evaluating the data and for introducing it into the road user algorithm.
  • a self-learning neural network can be provided, the data being provided to the self-learning neural network, the self-learning neural network being set up to train the road user algorithm.
  • 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 , wherein a self-learning neural network is provided for training the algorithm, wherein: a) a computer program product module for the automated or autonomous driving function is available, the computer program product module containing the algorithm to be trained and the self-learning neural network, b) a simulation environment with simulation parameters stands, wherein the simulation environment contains map data of a real existing operational area, the motor vehicle and, as an agent, at least one further simulated road user, with a behavior of the at least one further road users are determined by a road user algorithm that was generated according to the type described above; c) a mission is ready for the motor vehicle, and d) means for performing the mission and training the algorithm are provided.
  • provision can be made for means for randomizing or for stochastically varying parameters of the trajectory of the at least one of the further simulated road users to be provided.
  • means for providing a plurality of further simulated road users can be provided, which are set up to move at least partially along the central trajectory, partially within the bandwidths.
  • 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 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.
  • 1 shows a motor vehicle which is set up for autonomous driving
  • FIG. 2 shows a computer program product for the motor vehicle from FIG. 1;
  • FIG. 3 shows a location with the motor vehicle from FIG. 1 and other traffic participants
  • FIG. 7 shows a schematic diagram of the generation of a virtual agent, as well as
  • FIG. 8 shows a schematic diagram of the generation of an algorithm for controlling a
  • FIG. 1 shows a motor vehicle 2 which is set up for autonomous driving.
  • the motor vehicle 2 has a motor vehicle 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 particular in connection with FIGS. 2, 3 and 8.
  • the motor vehicle control device 4 is connected, on the one hand, to a series 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, 12 at the front of the motor vehicle 2, environmental sensors 14, 16 at the rear of the motor vehicle 2, a camera 18 and a GPS module 20. Depending on the configuration, further sensors can be provided, for example wheel speed sensors, acceleration sensors, etc., which are connected to the motor vehicle control unit 4.
  • 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 can achieve by intervening in the steering 22, engine control 24 and brakes 26, which are each connected to the motor vehicle control unit 4.
  • FIG. 2 shows a computer program product 28 with a computer program product module 30.
  • the computer program product 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 criteria or standards, i.e. for improvements to the algorithm 34.
  • known learning methods of monitored and unsupervised learning and combinations can also be used this learning method can be used.
  • the algorithm 34 can essentially consist of a complex filter with a matrix of values, often called weights, which 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 and control signals for controlling the motor vehicle 2 are generated.
  • the quality of the algorithm 34 is monitored by a further computer program product module 36, which monitors input variables and output variables, determines metrics therefrom and controls compliance with the quality by the functions on the basis of the metrics.
  • the computer program product module 36 can give negative as well as positive rewards for the neural network 32.
  • the motor vehicle 2 travels on a road 38 which intersects with a road 40 at a street intersection 42.
  • motorcyclists such as motorcyclist 44 shown in FIG. 3 have a mission 50 to drive from a starting point 46 on the road 40 to a destination point 48 on the road 38.
  • the route requires turning from road 40 onto road 38.
  • Motorcyclist 44 will drive a certain trajectory 52.1 during mission 50.
  • Mission 50 is repeated several times by different motorcyclists who all have the task of driving from starting point 46 to destination point 48.
  • the start and finish points can be defined as areas or corridors, e.g. as a two-dimensional area or as start and finish lines.
  • a mean trajectory 54 and bandwidth limits 56.1, 56.2 are generated therefrom, which describe a usual behavior and usual deviations from the mean trajectory 54.
  • the bandwidth limits 56.1, 56.2 cover most of the trajectories driven in each section, i.e. it is not necessary for a single one of the trajectories 52.1 to 52.4 to lie completely within the bandwidth limits 56.1, 56.2 from start to finish.
  • a stationary traffic surveillance camera 58 can also be provided at the intersection 42, which observes the traffic situation and thus records the behavior of the motorcyclist 44 and his driven trajectory 52.1 as well as other traffic events, for example the movement of a pedestrian 60.
  • the algorithm 34 for the control unit 4 of the motor vehicle 2 is trained in a simulation of the location 36, the motorcyclist 44 being simulated as an agent who can have the same mission 50 as part of the simulation.
  • the motorcyclist 44 has a GPS sensor 62 which continuously records data about the position of the motorcyclist 44.
  • the GPS sensor 62 can be installed, for example, in a mobile phone carried by the motorcyclist 44.
  • a controller 64 which, for example, controls a motorcycle 65 of the motorcyclist 44 and which can acquire data.
  • a camera 66 is connected to the controller 64 and records the traffic happening in front of the motorcyclist 44.
  • the controller 64 can also acquire data about the motorcycle, for example a throttle valve position, gear engaged, speed, lean angle and the like.
  • 5 shows a flow chart for generating the traffic user algorithm.
  • the software is provided.
  • a neural network and an algorithm are then provided.
  • a mission is then determined, for example the mission 50 shown in FIG. 3, to drive as a motorcyclist from the starting point 46 to the destination point 48.
  • each sensor data is recorded, for example from the GPS sensor 62, the controller 64 as well as the camera 66 and the traffic surveillance camera 58.
  • the sensor data is fed into the neural network, there an average trajectory and bandwidths determined.
  • FIG. 6 shows a flow chart of the method for training the algorithm 34.
  • a computer program product module (software) to be trained is provided for the control device 4 of the motor vehicle 2.
  • map data of the location 38 are provided.
  • a mission is then defined for the algorithm 34, for example as a motor vehicle 2 to get from a starting point to a destination in a certain traffic situation.
  • the algorithm is then trained in the control unit of the motor vehicle on the basis of the situations posed.
  • the relevant information can be used to influence the simulation.
  • a large number of different road users can be mapped, for example cars, trucks, motorcycles, cyclists, pedestrians.
  • Real infrastructure for example signs, traffic lights, street symbols, guidelines, etc. can also be displayed.
  • complex traffic maneuvers can be realistically examined within the framework of cooperative mobility, for example driving at an intersection where there are several road users and communication between road users, for example so as not to endanger any road user.
  • FIG. 7 shows a further schematic diagram of the generation of an agent 70, here a simulation of the motorcyclist 44.
  • the algorithm 72 which runs in a computer 74, is supplied with the data from the GPS sensor 62, the controller 64, the camera 66 and the traffic monitoring camera 58.
  • a self-learning neural network 76 optimizes the algorithm 62 by varying the same and specifying an algorithm 72 'and checking whether the modified algorithm 72' works better than the original algorithm 72. As soon as certain quality metrics are met, the algorithm 72 is frozen and used a compiler 78, the virtual agent 70 is created, which can be used in simulation environments.
  • the simulation environment 80 is provided by map data of the location 34 as well as simulations of the motor vehicle 2 and other road users such as the pedestrian 60 and the virtual agent 70.
  • a mission is then set up, which algorithm 34, which controls motor vehicle 2, is to carry out. This is, as described in connection with FIG. 7, processed by the neural network 32, which varies the algorithm 34 until certain quality standards are met.
  • the computer program product module 30 for the control device 4 of the motor vehicle 2 is then generated with the aid of a compiler 82.

Landscapes

  • Physics & Mathematics (AREA)
  • General Physics & Mathematics (AREA)
  • Traffic Control Systems (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

L'invention concerne un procédé mis en œuvre par ordinateur permettant de créer un algorithme d'usager de la route pour la simulation informatique d'usagers de la route, les usagers de la route appartenant à une classe d'usagers de la route mal protégés ; des données d'une pluralité de différents usagers de la route actuellement existants de la classe dans un environnement de trafic authentique sont détectées pendant la mise en œuvre d'au moins une mission à l'aide de capteurs fixés aux usagers de la route ; des trajectoires de mouvement des usagers de la route sont déterminées à partir des données ; une trajectoire de déplacement moyen pour la mission et des plages d'écarts par rapport aux trajectoires moyennes sont calculées à partir des trajectoires de mouvement. L'invention concerne en outre un procédé mis en œuvre par ordinateur pour la formation d'au moins un algorithme pour une unité de commande d'un véhicule automobile, un produit programme informatique et un véhicule automobile.
EP20820809.0A 2019-12-10 2020-12-03 Procédé de formation d'au moins un algorithme pour une unité de commande d'un véhicule automobile, produit programme informatique et véhicule automobile Active EP4073778B1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102019219241.6A DE102019219241A1 (de) 2019-12-10 2019-12-10 Verfahren zum Erstellen eines Verkehrsteilnehmeralgorithmus zur Computersimulation von Verkehrsteilnehmern, Verfahren zum Trainieren wenigstens eines Algorithmus für ein Steuergerät eines Kraftfahrzeugs, Computerprogrammprodukt sowie Kraftfahrzeug
PCT/EP2020/084464 WO2021115918A1 (fr) 2019-12-10 2020-12-03 Procédé de création d'un algorithme d'usager de la route permettant la simulation informatique d'usagers de la route, procédé de formation d'au moins un algorithme pour une unité de commande d'un véhicule automobile, produit programme informatique et véhicule automobile

Publications (2)

Publication Number Publication Date
EP4073778A1 true EP4073778A1 (fr) 2022-10-19
EP4073778B1 EP4073778B1 (fr) 2024-07-24

Family

ID=73748039

Family Applications (1)

Application Number Title Priority Date Filing Date
EP20820809.0A Active EP4073778B1 (fr) 2019-12-10 2020-12-03 Procédé de formation d'au moins un algorithme pour une unité de commande d'un véhicule automobile, produit programme informatique et véhicule automobile

Country Status (4)

Country Link
EP (1) EP4073778B1 (fr)
CN (1) CN114793460B (fr)
DE (1) DE102019219241A1 (fr)
WO (1) WO2021115918A1 (fr)

Families Citing this family (5)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102021104077B3 (de) 2021-02-22 2022-05-25 Dr. Ing. H.C. F. Porsche Aktiengesellschaft Verfahren, System und Computerprogrammprodukt zur automatisierten Generierung von Verkehrsdaten
DE102021213538A1 (de) 2021-11-30 2023-06-01 Psa Automobiles Sa Simulation zur Validierung einer automatisierenden Fahrfunktion für ein Fahrzeug
DE102022203422A1 (de) 2022-04-06 2023-10-12 Psa Automobiles Sa Test einer automatischen Fahrsteuerfunktion mittels semi-realer Verkehrsdaten
DE102022206238A1 (de) 2022-06-22 2023-12-28 Psa Automobiles Sa Auslegen eines automatischen Fahrsteuersystems in massiv parallelen Simulationen
DE102022131178B3 (de) 2022-11-24 2024-02-08 Cariad Se Verfahren zum automatisierten Führen eines Fahrzeugs sowie Verfahren zum Erzeugen eines hierzu fähigen Modells des Maschinellen Lernens sowie Prozessorschaltung und Fahrzeug

Family Cites Families (13)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
JP4980810B2 (ja) * 2007-07-10 2012-07-18 株式会社豊田中央研究所 交通シミュレーション装置及びプログラム
JP5895926B2 (ja) * 2013-12-09 2016-03-30 トヨタ自動車株式会社 移動案内装置及び移動案内方法
DE102014008353B4 (de) * 2014-06-04 2016-09-15 Audi Ag Verfahren zum Betrieb eines Fahrerassistenzsystems zur automatisierten Führung eines Kraftfahrzeugs sowie zugeordnetes Kraftfahrzeug
EP3371023A4 (fr) * 2015-11-04 2019-05-08 Zoox, Inc. Système et procédés de simulation pour véhicules autonomes
DE102016212700A1 (de) * 2016-07-13 2018-01-18 Robert Bosch Gmbh Verfahren und System zur Steuerung eines Fahrzeugs
DE102016215314A1 (de) * 2016-08-17 2018-02-22 Bayerische Motoren Werke Aktiengesellschaft Fahrerassistenzsystem, Fortbewegungsmittel und Verfahren zur Prädiktion einer Verkehrssituation
DE102017200842B4 (de) * 2017-01-19 2020-06-18 Audi Ag Verfahren zum Betrieb einer Verkehrsleitinfrastruktur und Verkehrsleitinfrastruktur
US10324469B2 (en) * 2017-03-28 2019-06-18 Mitsubishi Electric Research Laboratories, Inc. System and method for controlling motion of vehicle in shared environment
CN107247961B (zh) * 2017-05-10 2019-12-24 西安交通大学 一种应用模糊轨迹序列的轨迹预测方法
DE102017114876A1 (de) * 2017-07-04 2019-01-10 Iav Gmbh Ingenieurgesellschaft Auto Und Verkehr Fahrerassistenzsystem zur Kollisionsvermeidung mittels Warn- und Interventionskaskade
US10579063B2 (en) * 2017-07-21 2020-03-03 Uatc, Llc Machine learning for predicting locations of objects perceived by autonomous vehicles
DE102017007136A1 (de) * 2017-07-27 2019-01-31 Opel Automobile Gmbh Verfahren und Vorrichtung zum Trainieren selbstlernender Algorithmen für ein automatisiert fahrbares Fahrzeug
CN108389430B (zh) * 2018-01-12 2021-02-26 南京理工大学 一种基于视频检测的交叉口行人与机动车碰撞预测方法

Also Published As

Publication number Publication date
CN114793460B (zh) 2024-09-10
DE102019219241A1 (de) 2021-06-10
EP4073778B1 (fr) 2024-07-24
WO2021115918A1 (fr) 2021-06-17
CN114793460A (zh) 2022-07-26

Similar Documents

Publication Publication Date Title
EP3970077B1 (fr) Procédé pour l'entraînement d'au moins un algorithme pour un appareil de commande d'un véhicule automobile, produit de programme informatique, véhicule automobile ainsi que système
EP4073778B1 (fr) Procédé de formation d'au moins un algorithme pour une unité de commande d'un véhicule automobile, produit programme informatique et véhicule automobile
DE102006044086B4 (de) System und Verfahren zur Simulation von Verkehrssituationen, insbesondere unfallkritischen Gefahrensituationen, sowie ein Fahrsimulator
DE102019203712B4 (de) Verfahren zum Trainieren wenigstens eines Algorithmus für ein Steuergerät eines Kraftfahrzeugs, Computerprogrammprodukt, Kraftfahrzeug sowie System
WO2021083785A1 (fr) Procédé d'apprentissage d'au moins un algorithme pour un dispositif de commande d'un véhicule automobile, produit programme informatique et véhicule automobile
DE102007053501A1 (de) Verfahren zur Entwicklung und/oder zum Testen wenigstens eines Sicherheits- und/oder Fahrerassistenzsystems für ein Kraftfahrzeug und Simulationsumgebung
DE102019209736A1 (de) Verfahren zur Bewertung möglicher Trajektorien
DE102018220865A1 (de) Verfahren zum Trainieren wenigstens eines Algorithmus für ein Steuergerät eines Kraftfahrzeugs, Computerprogrammprodukt sowie Kraftfahrzeug
AT523834B1 (de) Verfahren und System zum Testen eines Fahrerassistenzsystems
DE102016214574A1 (de) Verfahren und Vorrichtung zum Schätzen von Reibwerten eines Rades eines Fahrzeugs gegenüber einem Untergrund
WO2021170580A1 (fr) Procédé d'apprentissage d'au moins un algorithme pour un dispositif de commande d'un véhicule automobile, produit de programme informatique et véhicule automobile
AT524822A1 (de) Verfahren zum Testen eines Fahrerassistenzsystems eines Fahrzeugs
DE102019208233A1 (de) Verfahren und Vorrichtung zum automatischen Ausführen einer Steuerfunktion eines Fahrzeugs
EP4107590A1 (fr) 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
EP4022589A1 (fr) Identification automatisée d'un comportement anormal d'un usager de la route
WO2022263175A1 (fr) Prédiction de mouvement pour usagers de la route
DE102022102501B3 (de) Verfahren, System und Computerprogrammprodukt zur Ermittlung einer Bewertung über die Funktionsfähigkeit einer Komponente eines Kraftfahrzeugs
WO2022184363A1 (fr) Procédé mis en œuvre par ordinateur pour l'entrainement d'au moins un algorithme pour une unité de commande d'un véhicule à moteur, produit programme d'ordinateur, unité de commande et véhicule à moteur
DE102019212830A1 (de) Analyse und Validierung eines neuronalen Netzes für ein Fahrzeug
DE102023208690B3 (de) Verfahren zum Erzeugen eines Längsführungs-Modells für ein Fahrzeug zur Ermittlung wenigstens einer Längsführungsgröße zur Längsführung des Fahrzeugs und Längsführungsprofil-Ermittlungseinrichtung
WO2024179733A1 (fr) Simulation adaptative basée sur des données réelles d'une zone de trafic coordonnée centralement
DE102023000578A1 (de) Verfahren zum Ermitteln von potentiellen, zukünftigen Positionen eines potentiellen Kollisionsobjekts mittels eines lernfähigen Systems, sowie Verfahren zum Trainieren eines entsprechenden lernfähigen Systems
DE102021208191A1 (de) Verfahren zum zumindest teilautomatisierten Führen eines Kraftfahrzeugs
EP4451159A1 (fr) Simulation de trafic hybride utilisant des modèles d'agent pour tester des véhicules à opératrice
WO2023094071A1 (fr) Modèles de force sociale pour la prédiction de trajectoire d'autres usagers de la route

Legal Events

Date Code Title Description
STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: UNKNOWN

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: THE INTERNATIONAL PUBLICATION HAS BEEN MADE

PUAI Public reference made under article 153(3) epc to a published international application that has entered the european phase

Free format text: ORIGINAL CODE: 0009012

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: REQUEST FOR EXAMINATION WAS MADE

17P Request for examination filed

Effective date: 20220406

AK Designated contracting states

Kind code of ref document: A1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

DAX Request for extension of the european patent (deleted)
RAV Requested validation state of the european patent: fee paid

Extension state: MA

Effective date: 20220406

RAP3 Party data changed (applicant data changed or rights of an application transferred)

Owner name: STELLANTIS AUTO SAS

GRAP Despatch of communication of intention to grant a patent

Free format text: ORIGINAL CODE: EPIDOSNIGR1

STAA Information on the status of an ep patent application or granted ep patent

Free format text: STATUS: GRANT OF PATENT IS INTENDED

INTG Intention to grant announced

Effective date: 20240306

GRAS Grant fee paid

Free format text: ORIGINAL CODE: EPIDOSNIGR3

AK Designated contracting states

Kind code of ref document: B1

Designated state(s): AL AT BE BG CH CY CZ DE DK EE ES FI FR GB GR HR HU IE IS IT LI LT LU LV MC MK MT NL NO PL PT RO RS SE SI SK SM TR

REG Reference to a national code

Ref country code: GB

Ref legal event code: FG4D

Free format text: NOT ENGLISH

REG Reference to a national code

Ref country code: CH

Ref legal event code: EP

REG Reference to a national code

Ref country code: IE

Ref legal event code: FG4D

Free format text: LANGUAGE OF EP DOCUMENT: GERMAN

Ref country code: DE

Ref legal event code: R096

Ref document number: 502020008703

Country of ref document: DE