WO2022214452A1 - Procédé de génération d'un signal de commande pour un dispositif de commande latérale d'un véhicule à moteur fonctionnant de manière au moins partiellement assistée et système d'assistance - Google Patents

Procédé de génération d'un signal de commande pour un dispositif de commande latérale d'un véhicule à moteur fonctionnant de manière au moins partiellement assistée et système d'assistance Download PDF

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Publication number
WO2022214452A1
WO2022214452A1 PCT/EP2022/058931 EP2022058931W WO2022214452A1 WO 2022214452 A1 WO2022214452 A1 WO 2022214452A1 EP 2022058931 W EP2022058931 W EP 2022058931W WO 2022214452 A1 WO2022214452 A1 WO 2022214452A1
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WO
WIPO (PCT)
Prior art keywords
steering
motor vehicle
assistance system
driver
control signal
Prior art date
Application number
PCT/EP2022/058931
Other languages
German (de)
English (en)
Inventor
Daniel Münning
Laura Jasmin Witt
Original Assignee
Volkswagen Aktiengesellschaft
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 Volkswagen Aktiengesellschaft filed Critical Volkswagen Aktiengesellschaft
Publication of WO2022214452A1 publication Critical patent/WO2022214452A1/fr

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Classifications

    • B—PERFORMING OPERATIONS; TRANSPORTING
    • B62—LAND VEHICLES FOR TRAVELLING OTHERWISE THAN ON RAILS
    • B62D—MOTOR VEHICLES; TRAILERS
    • B62D15/00—Steering not otherwise provided for
    • B62D15/02—Steering position indicators ; Steering position determination; Steering aids
    • B62D15/025—Active steering aids, e.g. helping the driver by actively influencing the steering system after environment evaluation
    • 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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/10—Path keeping
    • B60W30/12—Lane keeping
    • 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
    • B60W40/00—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models
    • B60W40/08—Estimation or calculation of non-directly measurable driving parameters for road vehicle drive control systems not related to the control of a particular sub unit, e.g. by using mathematical models related to drivers or passengers
    • B60W40/09—Driving style or behaviour
    • 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
    • B60W50/08—Interaction between the driver and the control system
    • B60W50/10—Interpretation of driver requests or demands
    • 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
    • B60W2050/0062—Adapting control system settings
    • B60W2050/0075—Automatic parameter input, automatic initialising or calibrating means
    • B60W2050/0083—Setting, resetting, calibration
    • B60W2050/0088—Adaptive recalibration
    • 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
    • B60W2710/00—Output or target parameters relating to a particular sub-units
    • B60W2710/20—Steering systems
    • B60W2710/202—Steering torque

Definitions

  • the invention relates to a method for generating a control signal for a lateral control device of a motor vehicle operated at least partially with assistance by means of an assistance system of the motor vehicle, in which the control signal is generated as a function of at least one detected environmental parameter by means of an adaptive neural network of an electronic computing device of the assistance system. Furthermore, the invention relates to a computer program product and an assistance system.
  • Motor vehicles are already known from the prior art which can be operated at least partially with assistance.
  • these motor vehicles can have a so-called Travel Assist, which has a so-called classic controller that stops the vehicle on the basis of recognized lanes, for example the middle of the lane.
  • an environment detection device for example a camera, has a method which generates a control signal for the controller and uses this control signal to specify a steering torque and a steering direction for the steering.
  • US 10,377,303 B2 discloses that a least restrictive allowable driving condition of a semi-autonomous driving system is determined based on one or more threats and sensor performance.
  • a current driving state and a future driving state are determined based on a driver's attention state and steering state. Warnings are provided to the driver to adapt the current driving condition to a future driving condition. Driver interaction and attention are enforced if the driver fails to respond to the warnings.
  • CN 108520155 B belongs to the technical field of neural network algorithms and traffic simulation and relates to a method for simulating vehicle behavior based on a neural network.
  • the method comprises the following steps: Extracting a personalized real traffic trajectory for the vehicle and converting the raw data into a data set that can be identified by a neural network; providing a behavioral model capable of representing the driving characteristics of each vehicle from the traffic trajectory for each vehicle using the neural network.
  • the method is dedicated to describing the relationship between the traffic condition the vehicle faces and the vehicle's behavior using driving data.
  • the behavior of the vehicle is mainly influenced by the vehicles in front and behind, which is a regression problem.
  • the input to the model is the traffic condition the vehicle is facing and the output is the vehicle's behavior.
  • CN 111332362 A presents an intelligent steer-by-wire control method that integrates a driver's personality.
  • the characteristic parameters of a steering system are extracted through collected data information; then, each based on the corresponding characteristic parameters, the driver personalities are identified by a car mean clustering analysis and a BP neural network method; the operational risk data is assessed through logical operations; then, according to the data obtained, a decision method is used to make control decisions for intervention steering, non-intervention steering or takeover steering, finally corresponding decisions are executed, and when a control instruction of intervention steering is executed, the vehicle control unit continues to integrate the driver's personality and operational risk and to correct the steering angle transmission ratio.
  • the object of the present invention is to create a method, a computer program product and an assistance system, by means of which improved, at least partially assisted driving of the motor vehicle can be implemented.
  • One aspect of the invention relates to a method for generating a control signal for a lateral control device of a motor vehicle operated at least partially with assistance by means of an assistance system of the motor vehicle, in which the control signal is generated as a function of at least one detected environmental parameter by means of an adaptive neural network of an electronic computing device of the assistance system.
  • a steering request detection device of the assistance system detects a driver's steering request in relation to the currently set control signal and, depending on a parameter characterizing the steering request, a learning rate for the adaptive neural network for generating a future control signal is determined using the electronic computing device.
  • an improved, at least partially assisted drive can thus be implemented.
  • the learning behavior for the neural network for controlling the transverse control device can be improved accordingly.
  • the invention thus solves the problem compared to the prior art that classic controllers are applied accordingly during development, so that the function is the best possible on the delivery date.
  • influences from changed factors, such as friction or steering cannot be taken into account in this approach.
  • a parallel control method is therefore proposed that can be adapted over the course of the vehicle's life and acquires new knowledge through driving.
  • the major prerequisite for this new learning method is to decide how much influence a newly observed behavior of the user/driver has on the control behavior. For example, if a vehicle has driven 200 kilometers on the freeway and then driven another short stretch, for example 1 kilometer, with different driving behavior, it is not certain that the behavior will be taken into account "strongly" enough in the learning process for this new kilometer or so shall be.
  • a capacitive sensor on a steering device of the motor vehicle as a steering request detection device detects an intensity value of a touch as the parameter characterizing the steering request on the steering device by the driver.
  • the steering device can be a steering wheel, for example.
  • a value at the capacitive steering device can be divided into four areas. For example, no touch, a light touch, a light grip, or a strong grip can be detected. On the basis of this value range, the desired steering and the associated learning rate can then be reliably determined.
  • an intensity value of a steering torque is detected by the driver as the parameter characterizing the steering request on the steering device using a steering torque sensor on a steering device of the motor vehicle as a steering request detection device.
  • the driver's steering torque can be divided into three areas.
  • the steering torque can be classified as low, medium, or high.
  • the range of the steering torque can be between -8 Nm and +8 Nm.
  • the intensity of the desired steering can be determined on the basis of the detected steering torque. On the basis of this, the learning rate can then in turn be adjusted accordingly.
  • a steering movement of the driver is recorded as the parameter characterizing the steering request on a steering device of the motor vehicle by means of a vehicle monitoring camera as a steering request recording device.
  • the driver's steering request can thus be determined by means of the camera by means of a visual evaluation. For example, it can be determined how many fingers the driver is gripping the steering wheel with.
  • tense muscles or the like as well as a reaction to the current control signal in the face can also be detected. In this way, a steering request can be identified in relation to the currently set control signal, which in turn influences the learning rate.
  • a gradient of a touch is detected as the parameter characterizing the desired steering on a steering device of the motor vehicle by means of a capacitive sensor of a steering device of the motor vehicle as the steering request detection device.
  • a correspondingly higher learning rate can be set if the driver normally steers with just one finger on the steering wheel and suddenly steers with two hands. This is a clear indication that the driver is not satisfied with the current control signal and is making an appropriate adjustment would like.
  • the rapid (temporal) change in the gripping which corresponds in particular to the gradient, can then be used to reliably infer the desired steering.
  • the learning rate in the neural network can then be set correspondingly high.
  • a specific driving profile of the driver is taken into account when determining the learning rate, for example by means of the assistance system.
  • a factor for determining the learning rate can then in turn be generated for this purpose, for example.
  • this can also be adapted to the specific environment. For example, a speed of the motor vehicle can be taken into account, in which case it can then be taken into account what the corresponding driving behavior of the driver is at the corresponding speed.
  • the learning rate of the neural network can thus be reliably determined.
  • a currently recorded environment and/or a currently recorded driving scenario are taken into account when determining the learning rate.
  • a city environment or a freeway or country road can be taken into account accordingly.
  • different driving behaviors, in particular with regard to the handling of the steering device, of the driver can be recorded in different environments or driving scenarios. This can now be taken into account in order to adjust the learning rate accordingly.
  • the learning rate determined and/or the neural network learned by means of the learning rate determined is transmitted to an electronic computing device external to the motor vehicle for future use.
  • the vehicle-external electronic computing device can be designed, for example, as a backend server or as a cloud server.
  • the correspondingly determined learning rate or the trained neural network can be stored outside the vehicle in order to be able to be correspondingly adjusted, for example, when another vehicle is taken over.
  • a corresponding exchange between the vehicles can take place, so that the trained network is available both in one vehicle and in the other vehicle.
  • the method presented is in particular a computer-implemented method.
  • a further aspect of the invention therefore relates to a computer program product with program code means which, when processed by the electronic computing device, cause the electronic computing device to carry out a method according to the preceding aspect.
  • Another aspect of the invention therefore also relates to a computer-readable storage medium with a corresponding computer program product.
  • Yet another aspect of the invention relates to an assistance system for an at least partially assisted motor vehicle, with at least one electronic computing device having an adaptive neural network and with a steering request detection device, the assistance system being designed to carry out a method according to the preceding aspect.
  • the method is carried out using the assistance system.
  • the electronic computing device has in particular electronic components, such as in particular integrated circuits, processors or other electronic components, which are necessary for processing the program code means.
  • yet another aspect of the invention relates to a motor vehicle with an assistance system according to the preceding aspect.
  • the motor vehicle is at least partially operated with assistance.
  • the invention also includes developments of the assistance system according to the invention and of the motor vehicle according to the invention, which have features as have already been described in connection with the developments of the method according to the invention. For this reason, the corresponding developments of the assistance system according to the invention and the motor vehicle according to the invention are not described again here.
  • the invention also includes the combinations of features of the described embodiments.
  • 1 shows a schematic plan view of an embodiment of a motor vehicle with an embodiment of an assistance system
  • 2 shows a schematic block diagram of an embodiment according to the method.
  • the exemplary embodiments explained below are preferred exemplary embodiments of the invention.
  • the described components each represent individual features of the invention that are to be considered independently of one another, which also develop the invention independently of one another and are therefore also to be regarded as part of the invention individually or in a combination other than that shown.
  • the exemplary embodiments described can also be supplemented by further features of the invention already described.
  • the assistance system 2 has at least one electronic computing device 3 with a neural network 4 capable of learning. Furthermore, the assistance system 2 has at least one steering request detection device 5 .
  • Steering request detection device 5 can be embodied as a steering wheel, for example.
  • the motor vehicle 1 also has a transverse control device 6 and an environment detection device 7 .
  • the motor vehicle 1 is in particular operated at least partially with assistance.
  • the lateral control device 6 can be used to provide a lane departure warning system for the motor vehicle 1, for example.
  • the lane departure warning system can be provided in such a way that the motor vehicle 1 can drive at least partially assisted in the middle of a lane 8 for the motor vehicle 1 .
  • the environment detection device 7 can be designed, for example, as a camera sensor, radar sensor, ultrasonic sensor or also as a swarm data receiving device.
  • the motor vehicle 1 has the lateral control device 6, which can be, for example, a lateral assistance system 2, which can also be referred to as Travel Assist.
  • the assistance system 2 is designed in particular in such a way that the motor vehicle 1 can be laterally controlled on the basis of the environmental data.
  • the neural network 4 is provided, which a corresponding driving behavior of a driver not shown can learn.
  • the difficulty that arises is the extent to which the neural network 4 is trained on the basis of the driving behavior. In other words, what influence the current driving behavior should have on the knowledge of the neural network 4 and should thereby influence the future regulation.
  • FIG. 2 shows a schematic block diagram according to an embodiment of the method.
  • FIG. 2 shows a method for generating a control signal 9 for the transverse control device 6 of the at least partially assisted motor vehicle
  • the control signal 9 is generated by means of the adaptive neural network 4 of the electronic computing device 3 as a function of at least one detected environmental parameter 10.
  • the steering request detection device 5 of the assistance system 2 detects a steering request L of the driver in relation to the currently set control signal 9 and, depending on a parameter 11 characterizing the steering request L, a learning rate 12 for the adaptive neural network 4 to generate a future Control signal 9 is determined by means of the electronic computing device 3.
  • a capacitive sensor 21 on a steering device of the motor vehicle 1 as a steering request detection device 5 detects an intensity value 13 of a touch as the parameter characterizing the steering request L
  • the capacitive sensor 21 may detect no touch, light touch, light grip, or strong grip. This is purely exemplary and by no means exhaustive. Other touches can also be identified.
  • the derivation of the parameter 11 for training the neural network 4 can then in turn be carried out on the basis of this contact.
  • an intensity value of a steering torque 15 can be recorded as the parameter 11 characterizing the steering request L on the steering device by the driver.
  • the steering torque 15 can be subdivided, for example, into a light steering torque, a medium steering torque or a high steering torque.
  • Corresponding limit values can be set for this purpose, for example. This is purely exemplary and likewise not to be regarded as exhaustive.
  • the range of the steering torque 15 can be divided between 8 Nm and -8 Nm, for example.
  • a steering movement of the driver is recorded as the parameter 11 characterizing the steering request L on a steering device of the motor vehicle 1 by means of a driver monitoring camera 16 as a steering request recording device 5 .
  • the parameter 11 for training the neural network 4 is derived in particular from the desired steering L.
  • the so-called learning rate 12 which was initially defined, is adjusted by a factor F for training the neural network.
  • the learning rate 12 can be derived from the product of factor F and the initial learning rate. Proceeding from this, the driver-dependent learning rate 12 can then in turn be generated.
  • a corresponding training method 17 is then used in turn to adapt the edge weights in the neural network 4 and thus for learning in the neural network 4.
  • the learning rate 12 influences how strongly these are adapted and correspondingly how strongly the current behavior is adapted to the driver's request.
  • a driver's driving profile 18 determined by means of the assistance system 2 is taken into account when determining the learning rate 12 .
  • a currently recorded environment 8 and/or a currently recorded driving scenario 19 can be taken into account when determining the learning rate 12 .
  • the vehicle-external electronic computing device 20 can also be referred to as a cloud server or backend server.

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  • Engineering & Computer Science (AREA)
  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Human Computer Interaction (AREA)
  • Physics & Mathematics (AREA)
  • Mathematical Physics (AREA)
  • Chemical & Material Sciences (AREA)
  • Combustion & Propulsion (AREA)
  • Steering Control In Accordance With Driving Conditions (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

L'invention concerne un procédé de génération d'un signal de commande (9) pour un dispositif de commande latérale (6) d'un véhicule à moteur (1) fonctionnant de manière au moins partiellement assistée au moyen d'un système d'assistance (2) du véhicule à moteur (1), dans lequel un réseau neuronal adaptatif (4) d'un dispositif informatique électronique (3) du système d'assistance (2) est utilisé pour générer le signal de commande (9) sur la base d'au moins un paramètre environnemental capturé (10), un dispositif de capture d'exigence de direction (5) du système d'assistance (2) étant utilisé pour capturer une exigence de direction (L) du conducteur par rapport au signal de commande actuellement établi (9) et un taux d'apprentissage (12) pour le réseau neuronal adaptatif (4) afin de générer un signal de commande futur (9) est déterminé au moyen du dispositif informatique électronique (3) sur la base d'un paramètre (11) caractérisant l'exigence de direction (L). L'invention concerne également un produit-programme d'ordinateur et un système d'assistance (2).
PCT/EP2022/058931 2021-04-09 2022-04-05 Procédé de génération d'un signal de commande pour un dispositif de commande latérale d'un véhicule à moteur fonctionnant de manière au moins partiellement assistée et système d'assistance WO2022214452A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE102021203520.5 2021-04-09
DE102021203520.5A DE102021203520B3 (de) 2021-04-09 2021-04-09 Verfahren zum Erzeugen eines Steuersignals für eine Querregeleinrichtung eines zumindest teilweise assistiert betriebenen Kraftfahrzeugs, sowie Assistenzsystem

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WO2022214452A1 true WO2022214452A1 (fr) 2022-10-13

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WO (1) WO2022214452A1 (fr)

Families Citing this family (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102022106924A1 (de) 2022-03-24 2023-09-28 Bayerische Motoren Werke Aktiengesellschaft Verfahren für ein automatisiertes Kraftfahrzeug mit einer Innenraumkamera, Computerprogramm, Steuergerät und Kraftfahrzeug
DE102022116650A1 (de) * 2022-07-05 2024-01-11 Valeo Schalter Und Sensoren Gmbh Verbesserte Bestimmung eines in einem elektrischen Servolenksystem anzulegenden Drehmoments
DE102022209634A1 (de) 2022-09-14 2024-03-14 Volkswagen Aktiengesellschaft Verfahren zum Betreiben eines lernenden Systems, Computerprogrammprodukt sowie Fahrzeug

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
DE102016217772A1 (de) * 2016-09-16 2018-03-22 Bayerische Motoren Werke Aktiengesellschaft Vorrichtung, Betriebsverfahren und elektronische Steuereinheit zur Steuerung eines zumindest teilweise automatisiert fahrbaren Fahrzeugs
DE102016121691A1 (de) * 2016-11-11 2018-05-17 Automotive Safety Technologies Gmbh Verfahren und System zum Betreiben eines Kraftfahrzeugs
CN108520155A (zh) 2018-04-11 2018-09-11 大连理工大学 基于神经网络的车辆行为模拟方法
US10377303B2 (en) 2014-09-04 2019-08-13 Toyota Motor Engineering & Manufacturing North America, Inc. Management of driver and vehicle modes for semi-autonomous driving systems
US20190332109A1 (en) * 2018-04-27 2019-10-31 GM Global Technology Operations LLC Systems and methods for autonomous driving using neural network-based driver learning on tokenized sensor inputs
CN111332362A (zh) 2020-03-10 2020-06-26 吉林大学 一种融合驾驶员个性的智能线控转向控制方法

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US10377303B2 (en) 2014-09-04 2019-08-13 Toyota Motor Engineering & Manufacturing North America, Inc. Management of driver and vehicle modes for semi-autonomous driving systems
DE102016217772A1 (de) * 2016-09-16 2018-03-22 Bayerische Motoren Werke Aktiengesellschaft Vorrichtung, Betriebsverfahren und elektronische Steuereinheit zur Steuerung eines zumindest teilweise automatisiert fahrbaren Fahrzeugs
DE102016121691A1 (de) * 2016-11-11 2018-05-17 Automotive Safety Technologies Gmbh Verfahren und System zum Betreiben eines Kraftfahrzeugs
CN108520155A (zh) 2018-04-11 2018-09-11 大连理工大学 基于神经网络的车辆行为模拟方法
US20190332109A1 (en) * 2018-04-27 2019-10-31 GM Global Technology Operations LLC Systems and methods for autonomous driving using neural network-based driver learning on tokenized sensor inputs
CN111332362A (zh) 2020-03-10 2020-06-26 吉林大学 一种融合驾驶员个性的智能线控转向控制方法

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