KR960013760A - Shift pattern determination device and method suitable for driver's driving tendency using neural network - Google Patents

Shift pattern determination device and method suitable for driver's driving tendency using neural network Download PDF

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KR960013760A
KR960013760A KR1019940026748A KR19940026748A KR960013760A KR 960013760 A KR960013760 A KR 960013760A KR 1019940026748 A KR1019940026748 A KR 1019940026748A KR 19940026748 A KR19940026748 A KR 19940026748A KR 960013760 A KR960013760 A KR 960013760A
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South Korea
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driving
driver
shift pattern
operation amount
amount
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KR1019940026748A
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Korean (ko)
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KR100227893B1 (en
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정수용
강훈
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전성원
현대자동차 주식회사
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    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16H—GEARING
    • F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
    • F16H61/02—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used
    • F16H61/0202—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used the signals being electric
    • F16H61/0204—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used the signals being electric for gearshift control, e.g. control functions for performing shifting or generation of shift signal
    • F16H61/0213—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used the signals being electric for gearshift control, e.g. control functions for performing shifting or generation of shift signal characterised by the method for generating shift signals
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16H—GEARING
    • F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
    • F16H2061/0075—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by a particular control method
    • F16H2061/0084—Neural networks
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16H—GEARING
    • F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
    • F16H2061/0075—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by a particular control method
    • F16H2061/0087—Adaptive control, e.g. the control parameters adapted by learning
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F16—ENGINEERING ELEMENTS AND UNITS; GENERAL MEASURES FOR PRODUCING AND MAINTAINING EFFECTIVE FUNCTIONING OF MACHINES OR INSTALLATIONS; THERMAL INSULATION IN GENERAL
    • F16H—GEARING
    • F16H61/00—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing
    • F16H61/02—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used
    • F16H61/0202—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used the signals being electric
    • F16H61/0204—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used the signals being electric for gearshift control, e.g. control functions for performing shifting or generation of shift signal
    • F16H61/0213—Control functions within control units of change-speed- or reversing-gearings for conveying rotary motion ; Control of exclusively fluid gearing, friction gearing, gearings with endless flexible members or other particular types of gearing characterised by the signals used the signals being electric for gearshift control, e.g. control functions for performing shifting or generation of shift signal characterised by the method for generating shift signals
    • F16H2061/022—Calculation or estimation of optimal gear ratio, e.g. best ratio for economy drive or performance according driver preference, or to optimise exhaust emissions

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  • Engineering & Computer Science (AREA)
  • General Engineering & Computer Science (AREA)
  • Mechanical Engineering (AREA)
  • Control Of Transmission Device (AREA)

Abstract

이 발명은 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정 방법에 관한 것으로서, 더욱 상세히 말하자면 자동 변속기 제어의 변속단 결정시 운전자의 운전 조작량을 산출하여 각각에 대하여 조합하여 그 운전 상황에 대한 운전 성향, 습관 및 도로 상황을 추론하여 운전자의 운전 성향을 고려한 뉴랄 네트워크(NEURAL NETWORK) 학습에 의한최적의 쉬프트 패턴을 결정하는 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정 장치 및 방법에 관한 것이다.The present invention relates to a shift pattern determination method suitable for a driver's driving tendency using neural networks, and more specifically, to calculate a driving stage of the automatic transmission control when the shift stage is determined, combining the driving operation amount of the driver for each driving situation for the driving situation. The present invention relates to an apparatus and method for determining a shift pattern suitable for a driver's driving tendency using a neural network, which determines an optimal shift pattern by learning a neural network considering a driver's driving tendency by inferring propensity, habits and road conditions. .

Description

뉴랄 네트 워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정장치 및 방법Shift pattern determination device and method suitable for driver's driving tendency using neural network

본 내용은 요부공개 건이므로 전문내용을 수록하지 않았음As this is a public information case, the full text was not included.

제1도는 이 발명의 실시예에 따른 뉴랄 네트워크를 이용한 운전자이 운전 성향에 적합한 쉬프트 패턴 결정방법의 순서를 나타낸 블럭도이고,FIG. 1 is a block diagram illustrating a procedure of determining a shift pattern suitable for a driving tendency of a driver using a neural network according to an embodiment of the present invention.

제2도는 이 발명의 실시예에 따른 뉴랄 네트워크를 이용한 운전자이 운전성향에 적합한 쉬프트 패턴 결정 장치를 나타낸 블럭도이다.2 is a block diagram showing an apparatus for determining a shift pattern suitable for driving tendency of a driver using a neural network according to an embodiment of the present invention.

Claims (12)

운전자의 운전 조작량을 입력하는 단계와; 상기 입력된 운전 조작량에 의해서 운전자의 운전 성향을 판단하는 단계와; 상기 판단된 운전 성향에 의해서 최적의 쉬프트 패턴을 결정하는 단계로 이루어진 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정 방법.Inputting a driving operation amount of the driver; Determining a driving tendency of a driver based on the input driving manipulation amount; The shift pattern determination method according to the driving tendency of the driver using a neural network comprising the step of determining an optimal shift pattern according to the determined driving tendency. 제1항에 있어서, 상기 운전자의 운전 조작량을 입력하는 단계에 있어서 상기 운전 조작량은 차량에 대한 악셀러레이터 조작량과 브레이크 조작량과 스티어링 휠 조작량으로 정의되는 것을 특징으로 하는 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정 방법.The driving propensity of a driver using a neural network according to claim 1, wherein in the step of inputting a driving operation amount of the driver, the driving operation amount is defined as an accelerator operation amount, a brake operation amount, and a steering wheel operation amount for the vehicle. Method for determining shift pattern suitable for. 제2항에 있어서, 상기 차량에 대한 악셀러레이터 조작량은 스로틀 개도량과 스로틀 개도 속도의 합으로 정의되는 것을 특징으로 하는 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정 방법.3. The method of claim 2, wherein the accelerator operation amount for the vehicle is defined as the sum of the throttle opening amount and the throttle opening amount. 제2항에 있어서, 상기 차량에 대한 브레이크 조작량은 브레이크 조작 강도와 브레이크 조작에 따른 차량감가속도 정의되는 것을 특징으로 하는 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴결정 방법.The shift pattern determination method according to claim 2, wherein the brake operation amount for the vehicle is defined by the brake operation intensity and the vehicle deceleration according to the brake operation. 제2항에 있어서, 상기 차량에 대한 스티어링 휠 조작량은 휠엔진과 휠엔진 속도의 절대값의 합으로 정의되는 것을 특징으로 하는 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정 방법.The method of claim 2, wherein the steering wheel manipulation amount for the vehicle is defined as a sum of an absolute value of a wheel engine and a wheel engine speed. 제1항에 있어서, 상기 입력된 운전 조작량에 의해서 운전자의 운전 성향을 판단하는 단계에 있어서, 상기 운전 성향 판단은 뉴랄 네트워크 학습에 의하여 이루어지는 것을 특징으로 하는 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정 방법.The shift suitable for the driving tendency of the driver using the neural network according to claim 1, wherein in the step of determining the driving tendency of the driver based on the input driving manipulation amount, the driving tendency determination is performed by neural network learning. Pattern determination method. 제5항에 있어서, 상기 뉴랄 네트워크 학습은 학습 네트워크 입력단계와 학습 네트워트 출력단계와 추론단계로 이루어져 있는 것을 특징으로 하는 뉴랄 네트워크를 이용한 운전자의 운전 성행에 적합한 쉬프트 패턴 결정 방법.The method of claim 5, wherein the neural network learning comprises a learning network input step, a learning network output step, and an inference step. 제6항에 있어서, 상기 학습 네트워크 입력단계에서 입력은 상기한 차량에 대한 악셀러레이터 조작량과 브레이크 조작량과 스트어링 휠 조작량의 이동 평균값으로 하는 것을 특징으로 하는 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정 방법.The method of claim 6, wherein the input in the learning network input step is a moving average value of the accelerator operation amount, the brake operation amount, and the steering wheel operation amount for the vehicle. How to determine shift pattern. 제7항에 있어서, 상기 이동 평균값은 이동 평균 디지탈 필터(MOVING AVERAGE DIGITAL FILTER : FIR)를 사용하는 것을 특징으로 하는 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정 방법.The shift pattern determination method of claim 7, wherein the moving average value uses a moving average digital filter (FIR). 제6항에 있어서, 상기 학습 네트워크 출력단계에 있어서 출력은 멀티 쉬프트 패턴으로 하는 것을 특징으로 하는 것을 특징으로 하는 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정 방법.The shift pattern determination method according to claim 6, wherein the output of the learning network output step is a multi shift pattern. 제9항에 있어서, 상기 멀티 쉬프트 패턴은 상기 학습 네트워크의 출력을 입력으로 하여 퍼지 추론 루틴을 통하여 수행함을 특징으로 하는 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정 방법.10. The method of claim 9, wherein the multi-shift pattern is performed through a fuzzy inference routine using the output of the learning network as an input. 운전자의 운전 조작량을 입력하는 운전 조작량 입력부와; 상기 입력된 운전 조작량을 판단하고 운전자의 운전 성향을 추론하는 제어부와; 상기 제어부에 결과에 의해 구동되는 변속단 구동부로 이루어지는 것을 특징으로 하는 뉴랄 네트워크를 이용한 운전자의 운전 성향에 적합한 쉬프트 패턴 결정 장치.A driving manipulation amount input unit for inputting a driving manipulation amount of the driver; A controller which determines the input driving manipulation amount and infers a driving tendency of a driver; And a shift stage driving unit driven by a result in the control unit. ※ 참고사항 : 최초출원 내용에 의하여 공개하는 것임.※ Note: The disclosure is based on the initial application.
KR1019940026748A 1994-10-19 1994-10-19 Shift pattern control device and method for neural net-work KR100227893B1 (en)

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