EP1685546A1 - Procede et systeme pour determiner une situation de conduite - Google Patents

Procede et systeme pour determiner une situation de conduite

Info

Publication number
EP1685546A1
EP1685546A1 EP04765557A EP04765557A EP1685546A1 EP 1685546 A1 EP1685546 A1 EP 1685546A1 EP 04765557 A EP04765557 A EP 04765557A EP 04765557 A EP04765557 A EP 04765557A EP 1685546 A1 EP1685546 A1 EP 1685546A1
Authority
EP
European Patent Office
Prior art keywords
driving
driving situation
neural network
output
motor vehicle
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.)
Withdrawn
Application number
EP04765557A
Other languages
German (de)
English (en)
Inventor
Christian Salzmann
Uwe Stolle
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 EP1685546A1 publication Critical patent/EP1685546A1/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/0265—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion
    • G05B13/027—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric the criterion being a learning criterion using neural networks only
    • 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

Definitions

  • the input layer of the neural network or the corresponding computer program in this embodiment of the invention is chronologically in each case a data record with status variables of the motor vehicle is supplied and the driving situation is determined chronologically in each case on the basis of the maximum output signal of all perceptrons of the output layer can be.
  • the method according to the invention is carried out on a software system according to the invention, which recognizes the current driving situation by means of the data which are mostly provided anyway via the electronic vehicle bus system (eg CAN bus).
  • the electronic vehicle bus system eg CAN bus.
  • the optional, exclusive use of data already provided has the advantage that no additional sensors or control units are required - a GPS system is also not absolutely necessary.
  • a second step the current driving situation and the current driving condition in the vehicle are calculated.
  • the second step is divided into: access relevant vehicle data from the CAN bus in appropriate time steps, - preparation of the data for optimal evaluation, calculation of the current driving situation at the current time based on current and past CAN data, and calculation of the current driving status.
  • neural networks are “trainable” and thus adaptable to the specific task.
  • training the network is set by examples using the known, so-called “back propagation” method so that new input values are similar classified as “training values”.
  • a signal pattern to be recognized is created at the input layer of the sigmoid network.
  • the perceptrons or the corresponding software components carry out a calculation according to the sigmoid function.
  • the result of the calculation is in
  • the parameters of the weights of the individual perceptrons are parameterized by means of known learning methods (algorithms), in which examples of input patterns are created and corresponding output patterns are specified.
  • the algorithm then sets the weights in this way that the specified output patterns are calculated or formed for the specified input patterns.

Landscapes

  • Engineering & Computer Science (AREA)
  • Artificial Intelligence (AREA)
  • Evolutionary Computation (AREA)
  • General Physics & Mathematics (AREA)
  • Physics & Mathematics (AREA)
  • Analytical Chemistry (AREA)
  • Chemical & Material Sciences (AREA)
  • Health & Medical Sciences (AREA)
  • Computer Vision & Pattern Recognition (AREA)
  • Medical Informatics (AREA)
  • Software Systems (AREA)
  • Automation & Control Theory (AREA)
  • Traffic Control Systems (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)

Abstract

L'invention concerne un procédé et un système permettant de déterminer la situation de conduite d'un véhicule automobile au moyen de données disponibles dans ledit véhicule, en particulier sur un bus de données (1) du véhicule, ces données indiquant la valeur d'au moins une variable d'état (V, ...) du véhicule. Selon l'invention, pour réduire les sollicitations auxquelles est soumis le conducteur, une première étape consiste à préparer un ensemble de données (V (t3), V (t2), V (t1), V (t0), ) indiquant l'historique de la ou des variables d'état. Une deuxième étape consiste à préparer dans le véhicule un réseau neuronal (3) au moyen d'un ordinateur programmé en conséquence. Ce réseau neuronal (3) présente au moins une couche d'entrée et une couche de sortie, chacune de ces couches comprenant une pluralité de perceptrons. Une troisième étape consiste à fournir à chaque perceptron du réseau neuronal (3) la valeur respective de la ou des variables d'état (V, ...) du moment correspondant, de préférence une valeur normalisée. La situation de conduite actuelle (4) est ensuite fournie par les perceptrons de la couche de sortie du réseau neuronal, après entraînement de ce dernier.
EP04765557A 2003-11-20 2004-09-22 Procede et systeme pour determiner une situation de conduite Withdrawn EP1685546A1 (fr)

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
DE10354322.8A DE10354322B4 (de) 2003-11-20 2003-11-20 Verfahren und System zur Ermittlung der Fahrsituation
PCT/EP2004/010704 WO2005052883A1 (fr) 2003-11-20 2004-09-22 Procede et systeme pour determiner une situation de conduite

Publications (1)

Publication Number Publication Date
EP1685546A1 true EP1685546A1 (fr) 2006-08-02

Family

ID=34609167

Family Applications (1)

Application Number Title Priority Date Filing Date
EP04765557A Withdrawn EP1685546A1 (fr) 2003-11-20 2004-09-22 Procede et systeme pour determiner une situation de conduite

Country Status (4)

Country Link
US (1) US7386389B2 (fr)
EP (1) EP1685546A1 (fr)
DE (1) DE10354322B4 (fr)
WO (1) WO2005052883A1 (fr)

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Also Published As

Publication number Publication date
DE10354322A1 (de) 2005-06-23
US7386389B2 (en) 2008-06-10
US20060271269A1 (en) 2006-11-30
DE10354322B4 (de) 2022-06-09
WO2005052883A1 (fr) 2005-06-09

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