EP4526871A1 - Procédé et dispositif d'identification d'un dysfonctionnement dans un modèle d'environnement d'une fonction de conduite automatisée - Google Patents

Procédé et dispositif d'identification d'un dysfonctionnement dans un modèle d'environnement d'une fonction de conduite automatisée

Info

Publication number
EP4526871A1
EP4526871A1 EP23722370.6A EP23722370A EP4526871A1 EP 4526871 A1 EP4526871 A1 EP 4526871A1 EP 23722370 A EP23722370 A EP 23722370A EP 4526871 A1 EP4526871 A1 EP 4526871A1
Authority
EP
European Patent Office
Prior art keywords
deviation
motor vehicle
road
determined
environment model
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
EP23722370.6A
Other languages
German (de)
English (en)
Inventor
Frank Keidel
Alexander Born
Tilman Nowak
Sean Brown
Marco Baumgartl
Christian Unger
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.)
Bayerische Motoren Werke AG
Original Assignee
Bayerische Motoren Werke AG
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 Bayerische Motoren Werke AG filed Critical Bayerische Motoren Werke AG
Publication of EP4526871A1 publication Critical patent/EP4526871A1/fr
Pending legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0108—Measuring and analyzing of parameters relative to traffic conditions based on the source of data
    • G08G1/0112—Measuring and analyzing of parameters relative to traffic conditions based on the source of data from the vehicle, e.g. floating car data [FCD]
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
    • B60W50/0205—Diagnosing or detecting failures; Failure detection models
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/0706—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment
    • G06F11/0736—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment in functional embedded systems, i.e. in a data processing system designed as a combination of hardware and software dedicated to performing a certain function
    • G06F11/0739—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation the processing taking place on a specific hardware platform or in a specific software environment in functional embedded systems, i.e. in a data processing system designed as a combination of hardware and software dedicated to performing a certain function in a data processing system embedded in automotive or aircraft systems
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F11/00—Error detection; Error correction; Monitoring
    • G06F11/07—Responding to the occurrence of a fault, e.g. fault tolerance
    • G06F11/0703—Error or fault processing not based on redundancy, i.e. by taking additional measures to deal with the error or fault not making use of redundancy in operation, in hardware, or in data representation
    • G06F11/079—Root cause analysis, i.e. error or fault diagnosis
    • G—PHYSICS
    • G06—COMPUTING OR CALCULATING; 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
    • G06V20/588—Recognition of the road, e.g. of lane markings; Recognition of the vehicle driving pattern in relation to the road
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/0104—Measuring and analyzing of parameters relative to traffic conditions
    • G08G1/0125—Traffic data processing
    • G08G1/0133—Traffic data processing for classifying traffic situation
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/01—Detecting movement of traffic to be counted or controlled
    • G08G1/04—Detecting movement of traffic to be counted or controlled using optical or ultrasonic detectors
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
    • B60W50/0205—Diagnosing or detecting failures; Failure detection models
    • B60W2050/021—Means for detecting failure or malfunction
    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B60—VEHICLES IN GENERAL
    • B60W—CONJOINT CONTROL OF VEHICLE SUB-UNITS OF DIFFERENT TYPE OR DIFFERENT FUNCTION; CONTROL SYSTEMS SPECIALLY ADAPTED FOR HYBRID VEHICLES; ROAD VEHICLE DRIVE CONTROL SYSTEMS FOR PURPOSES NOT RELATED TO THE CONTROL OF A PARTICULAR SUB-UNIT
    • B60W50/00—Details of control systems for road vehicle drive control not related to the control of a particular sub-unit, e.g. process diagnostic or vehicle driver interfaces
    • B60W50/02—Ensuring safety in case of control system failures, e.g. by diagnosing, circumventing or fixing failures
    • B60W50/0205—Diagnosing or detecting failures; Failure detection models
    • B60W2050/0215—Sensor drifts or sensor failures
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G1/00—Traffic control systems for road vehicles
    • G08G1/16—Anti-collision systems
    • G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection

Definitions

  • the present disclosure relates to a method for detecting a malfunction of an environment model of an automated driving function.
  • a data processing device is provided which is designed to at least partially carry out the method.
  • an automated motor vehicle that at least partially includes the data processing device is provided.
  • a computer program is provided which includes commands which, when the program is executed by a computer, cause it to at least partially carry out the method.
  • a computer-readable medium is provided which includes instructions which, when the instructions are executed by a computer, cause it to at least partially carry out the method.
  • a test driver who notices such motor vehicle misbehavior makes a note of it and manually starts a data recording to record the incident.
  • This data recording can be analyzed by development and it can be determined which part of the automated driving function is responsible for the misconduct responsible for. However, many manual steps are necessary when recording the data and analyzing it.
  • the reason for the malfunction is traditionally carried out by developers through manual visual analysis of the recorded data.
  • the developers visually compare the road geometry calculated by the environment model with reality (trajectory traveled or camera images).
  • the object of the present disclosure is to provide a device and a method which are each suitable for enriching the prior art described above.
  • the task is then solved by a method for detecting a malfunction of an environment model that is used by an automated driving function of a motor vehicle.
  • the method includes determining a first deviation between a target trajectory determined by the environment model and an actual trajectory driven by the motor vehicle. Additionally or alternatively, the method includes determining a second deviation between a road course determined by the environment model and a road course determined by camera software. The method further includes detecting the malfunction based on the first and/or the second deviation.
  • the method described above can be a computer-implemented method, ie one, several or all steps of the method can be carried out by a computer or a data processing device.
  • An automated driving function can be understood as meaning a function of the motor vehicle that is designed to take over certain parts of or the entire driving task, if necessary together with other automated driving functions, at least temporarily.
  • the environment model can be a so-called road environment model.
  • the environment model can be determined based on sensor data from several, in particular different, sensors in the motor vehicle. For this purpose, the sensor data can be merged.
  • the road environment model can include information about the course of the road and the target trajectory of the motor vehicle.
  • a trajectory can be understood as a path along which the motor vehicle should travel (so-called target trajectory) or actually travels (so-called actual trajectory).
  • the trajectory can also have a temporal component, i.e. when should or is the motor vehicle where.
  • the two options for determining the deviation described above have the common inventive idea of automatically detecting and, if necessary, subsequently analyzing a malfunction of the automated driving function in the area of the (road) environment model.
  • Determining the first deviation between the target trajectory determined by the environment model and the actual trajectory driven by the motor vehicle can involve determining a position and/or a curvature of a center line of a roadway using the environment model as the target trajectory, and determining the first Include deviation based on a deviation between the position and/or curvature of the centerline and a position and/or curvature of the actual trajectory.
  • Determining the second deviation between the road course determined by the environment model and the road course determined by the camera software can involve determining a position and/or a curvature of a road marking and/or a center line of a road using the environment model as a road course, determining a position and/or or curvature of the road marking and/or a center line of the road using the camera software as a road course, and determining the second deviation based on a deviation between the position and/or the curvature of the road marking and/or the center line, which was determined using the environment model ( n), and the position and/or curvature of the road marking and/or the center line determined using the camera software.
  • the method includes determining the existence of a predetermined environmental situation and recognizing that there is no malfunction despite the second deviation.
  • the determination of the first and/or the second deviation can take place during a journey in the motor vehicle, i.e. online, and/or after the journey using a data processing device external to the motor vehicle, i.e. offline.
  • Data used by the automated driving function can be stored in a ring memory if the first and/or second deviation is determined while driving, ie online.
  • the data stored in the ring memory can be sent from the motor vehicle to the data processing device external to the motor vehicle when the malfunction is detected based on the first and/or the second deviation.
  • the malfunction can be detected if the first and/or the second deviation exceeds or exceeds a respective predetermined limit value.
  • the quality assessment can take place both online and offline.
  • An online quality assessment self-monitoring
  • the recorded data can be analyzed automatically or automatically and/or included in a training data collection.
  • the disclosure provides for several measures to automatically detect and analyze incorrect behavior of an automated driving function in the area of the road environment model.
  • the disclosure determines a deviation between the course of the road determined by the automated driving function (road environment model) and the trajectory actually driven.
  • the lateral offset between a center line calculated by the environment model and the actual trajectory driven can be used as a deviation.
  • a deviation can also be calculated based on the steering angle.
  • the deviation can be determined both online in the vehicle and offline.
  • the environment model can have several submodels (lane fusion, crowd trajectories, HD map). With the invention, the individual models can be evaluated.
  • the data recording can also take place when other critical values are detected, e.g. excessive curvatures, particularly small or large track widths, very short or unstable detections of the road markings by the camera, etc.
  • a geometry calculated by the road environment model i.e. an output of the road environment model
  • a geometry of a detection provided by a camera software a lane marking and/or estimated centerline (i.e. an input from the road environment model).
  • a detection can therefore be provided which detects certain special situations (ie predetermined scenarios) in which camera false detections are known to frequently occur.
  • current camera detections can be discarded and not taken into account when calculating the road environment model.
  • the deviation may be intentional.
  • the deviations can also arise from incorrect processing in the environment model despite correct input data. This can be an unwanted deviation, which is an indication of a possible processing error in the environment model.
  • the special situations (scenarios) identified can serve as a criterion to determine the reason for the deviation.
  • driver takeover Another indication of malfunctioning of the road environment model can be the driver taking over control (so-called driver takeover).
  • driver can be asked to take control of the vehicle, e.g. if self-monitoring detects a fault, or the driver can take control on his own initiative, e.g. if the driver detects misconduct.
  • the driver takeover can be used as a trigger for data recording. However, it can also, i.e. additionally or alternatively, serve as a criterion when analyzing the reason for the vehicle's misbehavior.
  • the data recording can use a ring buffer so that when a recording starts, the period before the incident or malfunction is also recorded. This enables the initiation of the deviation to be analyzed.
  • the recorded data can be transferred from the motor vehicle, e.g. from the customer's vehicle, to a cloud (e.g. from the vehicle manufacturer). This makes it possible to determine statistics on the abnormalities, in particular worldwide, from all and/or predetermined customer vehicles.
  • the procedure can therefore be used for error analysis.
  • Statistical evaluation can be used to focus on the most common error patterns.
  • Automated recording can be used to identify geographic hotspots of misconduct.
  • the method can be used to detect a faulty component of the automated driving function (e.g. camera software, lane marking fusion, localization, crowd trajectory map, HD map, etc.).
  • a faulty component of the automated driving function e.g. camera software, lane marking fusion, localization, crowd trajectory map, HD map, etc.
  • Object and/or obstacle information can also be included to determine reasons for the detour or the deviating trajectory.
  • the recorded data can be automatically included in a training database for the automated driving function and/or can be supplied to a supplier of the automated driving function and/or the camera software to improve the automated driving function or the camera software.
  • a device for data processing which includes means for carrying out the method described above.
  • the data processing device can also be referred to as a data processing system or a data processing device.
  • the data processing device can be designed to be at least partially installed in and/or on an automated motor vehicle and/or to be at least partially part of a cloud.
  • the data processing device can be part of a driving assistance system or an automated driving function or can represent and/or execute this.
  • the data processing device can have an intelligent processor-controlled unit, which can communicate with other modules, for example via a central gateway (CGW) and, if necessary, via field buses, such as the CAN bus, LIN bus, MOST bus and FlexRay or via automotive -Ethernet, e.g. together with telematics control devices, can form the vehicle on-board network.
  • CGW central gateway
  • field buses such as the CAN bus, LIN bus, MOST bus and FlexRay or via automotive -Ethernet, e.g. together with telematics control devices, can form the vehicle on-board network.
  • the control unit controls functions relevant to the driving behavior of the motor vehicle, such as the engine control, the power transmission, the braking system and/or the tire pressure monitoring system.
  • driver assistance systems such as a parking assistant, an adapted cruise control (ACC), a lane keeping assistant, a lane change assistant, a traffic sign recognition, a light signal recognition, a starting assistant, a night vision assistant, an emergency braking assistant and / or an intersection assistant, can be used by the Control unit can be controlled.
  • the data processing device controls automated driving of the automated motor vehicle at least partially and/or temporarily based on an output of an environment model.
  • an automated motor vehicle can be provided that has the data processing device described above.
  • the motor vehicle can be a passenger car, in particular an automobile.
  • the automated motor vehicle can be designed to provide longitudinal guidance and/or transverse guidance during automated driving of the motor vehicle at least partially and/or at least temporarily by means of the automated driving function.
  • the automated driving can be controlled at least partially and/or temporarily by the data processing device using the automated driving function.
  • Automated driving can be carried out in such a way that the movement of the motor vehicle is (largely) autonomous.
  • the motor vehicle can be a motor vehicle with autonomy level 0, i.e. the driver takes over the dynamic driving task, even if supporting systems (e.g. ABS or ESP) are present.
  • supporting systems e.g. ABS or ESP
  • the motor vehicle can be a motor vehicle with autonomy level 1, i.e. have certain driver assistance systems that support the driver in operating the vehicle, such as adaptive cruise control (ACC).
  • ACC adaptive cruise control
  • the motor vehicle can be a motor vehicle of autonomy level 2, i.e. be partially automated so that functions such as automatic parking, lane keeping or lateral guidance, general longitudinal guidance, acceleration and/or braking are taken over by driver assistance systems.
  • the motor vehicle can be a motor vehicle of autonomy level 3, i.e. conditionally automated so that the driver does not have to continuously monitor the vehicle system.
  • the motor vehicle independently carries out functions such as triggering the turn signal, changing lanes and/or keeping in lane. The driver can turn his attention to other things, but if necessary the system will ask him to take over within a warning period.
  • the motor vehicle can be a motor vehicle of autonomy level 4, ie so highly automated that the vehicle's guidance is permanently controlled by the vehicle system is taken over. If the system can no longer handle the driving tasks, the driver can be asked to take over the lead.
  • the motor vehicle can be a motor vehicle with autonomy level 5, i.e. so fully automated that the driver is not required to complete the driving task. No human intervention is required other than setting the target and starting the system.
  • a computer program is also provided.
  • the computer program is characterized in that it includes commands which, when the program is executed by a computer, cause it to at least partially carry out the method described.
  • a program code of the computer program can be in any code, in particular in a code that is suitable for motor vehicle controls.
  • a computer-readable medium in particular a computer-readable storage medium, is provided.
  • the computer-readable medium is characterized in that it includes instructions which, when the program is executed by a computer, cause it to at least partially carry out the method described above.
  • a computer-readable medium may be provided that includes a computer program as defined above.
  • the computer-readable medium can be any digital data storage device, such as a USB flash drive, hard drive, CD-ROM, SD card, or SSD card.
  • the computer program does not necessarily have to be stored on such a computer-readable storage medium in order to be made available to the motor vehicle but can also be obtained externally via the Internet or elsewhere.
  • FIG. 1 shows schematically a flowchart of a method for detecting a malfunction of an environment model that is used by an automated driving function of a motor vehicle
  • FIG. 2 schematically shows a first plan view of a roadway level to explain the determination of a first deviation between a target trajectory determined by the environment model and an actual trajectory driven by the motor vehicle
  • FIG. 3 schematically shows a second plan view of a roadway level to explain the determination of a first deviation between a target trajectory determined by the environment model and an actual trajectory driven by the motor vehicle.
  • the method for detecting the malfunction of the environment model which is used by the automated driving function of the motor vehicle, essentially has two steps S1 and S2.
  • a first deviation is determined between a target trajectory determined by the environment model and an actual trajectory driven by the motor vehicle. Additionally or alternatively, a second deviation is determined between a road course determined by the environment model and a road course determined by camera software.
  • Determining the first deviation between the target trajectory determined by the environment model and the actual trajectory driven by the motor vehicle can involve determining a position of a center line of a road using the environment model as the target trajectory and determining the first deviation based on a deviation between the position of the center line and the position of the actual trajectory.
  • the respective deviations between the real trajectory or actual trajectory 1 and the calculated center line t1, t2 of the environment model are then determined at different times at the point or position at which the motor vehicle was at the time the center line was determined.
  • the deviation can be the vertical distance between the actual trajectory 1 and the calculated center line t1, t2 or, as shown in Figure 2, the shortest distance d1 or d2 between the actual trajectory 1 and the calculated center line t1, t2.
  • the distance d1, d2 between these can at least be included in the calculation or determination of the first deviation.
  • a curvature of the center line and a curvature of the actual trajectory can be determined at the respective positions of the motor vehicle and the difference between the curvatures can at least be included in the calculation or determination of the first deviation.
  • a steering angle of the motor vehicle can be used, ie the respective curvature of the center line t1, t2 can be at different points Positions are determined and determined with the actual steering angle of the motor vehicle at the respective position.
  • the center lines of the environment model are pre-processed, e.g. creation of a single global center line from the center lines of several time steps or points in time t1, t2, and the deviation, e.g. the maximum distance between the actual trajectory and this global center line, is determined.
  • a coordinate system of the motor vehicle i.e. a relative coordinate system
  • the current position 2 or actual position of the motor vehicle can be compared with a current target position of the motor vehicle, which lies on the center line t1 and thereby the distance between d1 can be determined. This can happen on an ongoing basis. Determining the first deviation via the relative coordinate system can be particularly suitable if this is determined online while driving and can be done continuously.
  • the center line t1 used for this can be determined in the same journal or in an earlier journal.
  • the curvature of the center line t1 and the current steering angle can also be compared analogously to the manner described above.
  • the center line t1, t2 is determined both by the environment model and by camera software.
  • a position and/or a curvature of the center lines t1, t2 can then be determined using both the camera software and the environment model and a deviation between the position and/or the curvature of the center lines can be determined.
  • the deviation between the center lines for example their lateral offset (see above), can at least be included in the calculation or determination of the second deviation. It would also be conceivable to use a road marking in this way in addition to or as an alternative to the center line.
  • a position and/or a curvature of a road marking can then be determined using both the camera software and the environment model can be determined and a deviation between the position and/or the curvature of the road markings can be determined.
  • the deviation between the road markings for example their lateral offset (see above), can at least be included in the calculation or determination of the second deviation.
  • a second step S2 of the method the malfunction is detected based on the first and/or the second deviation, the malfunction being recognized when the first and/or the second deviation exceeds or exceeds a respective predetermined limit value.
  • the second deviation is not taken into account if it is determined that a predetermined environmental situation exists. This means that a detection can be provided in the automated driving function, which detects predetermined environmental situations or special situations (i.e. predetermined scenarios) in which camera false detections are known to occur frequently. In this case, current camera detections can be discarded and not taken into account when calculating the environment model. In this case, the deviation may be intentional. In such a case, it can therefore be recognized that there is no malfunction despite the second deviation exceeding the limit value.
  • the above-described determination of the first and/or the second deviation can take place during a journey in the motor vehicle and/or after the journey using a data processing device external to the motor vehicle. The same applies to the detection of the malfunction described above based on the first and/or the second deviation.
  • Data used by the automated driving function may be stored in a ring buffer when one or both of these steps occurs while driving.
  • the data stored in the ring memory can be sent from the motor vehicle to the data processing device external to the motor vehicle when the malfunction is detected based on the first and/or the second deviation.
  • the data can be recorded with a recording device, in particular a data logger, during the entire journey or parts thereof, regardless of whether a malfunction is detected, and/or sent to the by Motor vehicle external data processing device is sent, in particular streamed into a backend or a cloud.
  • a recording device in particular a data logger

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  • Physics & Mathematics (AREA)
  • Theoretical Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Chemical & Material Sciences (AREA)
  • General Engineering & Computer Science (AREA)
  • Quality & Reliability (AREA)
  • Human Computer Interaction (AREA)
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  • Biomedical Technology (AREA)
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Abstract

La présente invention concerne un procédé d'identification d'un dysfonctionnement dans un modèle d'environnement qui est utilisé par une fonction de conduite automatisée d'un véhicule à moteur, le procédé consistant à : déterminer un premier écart entre une trajectoire cible déterminée par le modèle d'environnement et une trajectoire réelle parcourue par le véhicule à moteur et/ou un second écart entre une trajectoire d'une route déterminée par le modèle d'environnement et une trajectoire d'une route déterminée par un logiciel de caméra ; et identifier le dysfonctionnement sur la base du premier et/ou du second écart.
EP23722370.6A 2022-05-20 2023-04-27 Procédé et dispositif d'identification d'un dysfonctionnement dans un modèle d'environnement d'une fonction de conduite automatisée Pending EP4526871A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102022112745.1A DE102022112745A1 (de) 2022-05-20 2022-05-20 Verfahren und vorrichtung zum erkennen einer fehlfunktion eines umfeldmodells einer automatisierten fahrfunktion
PCT/EP2023/061108 WO2023222357A1 (fr) 2022-05-20 2023-04-27 Procédé et dispositif d'identification d'un dysfonctionnement dans un modèle d'environnement d'une fonction de conduite automatisée

Publications (1)

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EP4526871A1 true EP4526871A1 (fr) 2025-03-26

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US (1) US20250296579A1 (fr)
EP (1) EP4526871A1 (fr)
CN (1) CN119137637A (fr)
DE (1) DE102022112745A1 (fr)
WO (1) WO2023222357A1 (fr)

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DE102023128504A1 (de) 2023-10-18 2025-04-24 Cariad Se Verfahren zum Optimieren einer Fahrsituation

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DE102019217642B4 (de) 2019-11-15 2025-07-03 Volkswagen Aktiengesellschaft Verfahren zur Ermittlung und Archivierung von problematischen Bildausschnitten zur nachträglichen Überprüfung eines bildauswertenden Systems eines Fahrzeugs, Vorrichtung und Fahrzeug zur Verwendung bei dem Verfahren sowie Computerprogramm
DE102019112413B4 (de) * 2019-05-13 2025-10-09 Bayerische Motoren Werke Aktiengesellschaft Verfahren und vorrichtung zur multi-sensor-datenfusion für automatisierte und autonome fahrzeuge
US10984260B2 (en) * 2019-05-23 2021-04-20 GM Global Technology Operations LLC Method and apparatus for controlling a vehicle including an autonomous control system
DE102019211327B4 (de) 2019-07-30 2024-02-08 Zf Friedrichshafen Ag Vorrichtung, Fahrzeug und Verfahren zur verbesserten Multi-Radar-Sensor-Kalibration

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WO2023222357A1 (fr) 2023-11-23
US20250296579A1 (en) 2025-09-25
CN119137637A (zh) 2024-12-13

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