JP7072133B2 - Driver control operation quantification method and device based on the minimum action amount principle - Google Patents

Driver control operation quantification method and device based on the minimum action amount principle Download PDF

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JP7072133B2
JP7072133B2 JP2020541837A JP2020541837A JP7072133B2 JP 7072133 B2 JP7072133 B2 JP 7072133B2 JP 2020541837 A JP2020541837 A JP 2020541837A JP 2020541837 A JP2020541837 A JP 2020541837A JP 7072133 B2 JP7072133 B2 JP 7072133B2
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王建強
鄭訊佳
黄荷葉
***
許慶
李升波
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Tsinghua University
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    • 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
    • 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
    • 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
    • B60W2556/00—Input parameters relating to data
    • B60W2556/10—Historical data

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  • Automation & Control Theory (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
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Description

本発明は、インテリジェントの車両応用技術分野に関し、特に最小アクション量原理に
基づく運転者制御動作定量化方法及び装置に関する。 The present invention relates to the field of intelligent vehicle application technology, and particularly to a driver control motion quantification method and device based on the minimum action quantity principle.

道路交通安全は人間－車両－環境閉ループシステムに関連し、交通事故を構成する人間
、車両、環境の３つの要素において、通常、人間要素の割合が最も大きく、即ちほとんど
の交通事故が人的要因によって引き起こされ、したがって、車両に対する運転者の制御動
作が交通安全を確保する重要な要因になる。現在、急速に発展しているインテリジェント
交通、インテリジェント運転技術とインテリジェント自動車に対して、自動車インテリジ
ェントシステムの安全性能と運転者の受け入れ程度が自動車インテリジェント化を制約す
る重要な要因であり、その中の運転者の受け入れ程度とは自動車インテリジェントシステ
ムが運転者の運転制御動作に適合する必要があることを指す。 Road traffic safety is related to the human-vehicle-environment closed loop system, and among the three components of human, vehicle, and environment that make up a traffic accident, the proportion of the human component is usually the largest, that is, most traffic accidents are human factors. Therefore, the driver's control action over the vehicle is an important factor in ensuring traffic safety. For intelligent traffic, intelligent driving technology and intelligent automobiles, which are currently developing rapidly, the safety performance of automobile intelligent systems and the degree of acceptance of drivers are important factors that limit the intelligentization of automobiles. The degree of acceptance of a person means that the automobile intelligent system needs to be adapted to the driving control operation of the driver.

従来技術では、通常、統計分析法を使用して運転者の運転制御動作を研究することがで
き、この研究結果が自動車インテリジェントシステムを最適化して安全性能と運転者の受
け入れ程度を調整することに役立つ。従来の統計分析法では例えば確率統計、ファジー数
学、ラフ集合理論などの研究方法又は実車実験データ統計分析に基づく運転者動作特性化
記述方法を用いる。しかし、運転者の運転制御動作自体に個人差、年齢差、性差と地域差
などの様々な違い影響があるため、従来の研究方法を用いると大量のデータサンプルが必
要になることが多く、研究に大きな困難をもたらす。 In the prior art, statistical analysis methods can usually be used to study the driver's driving control behavior, and the results of this study are to optimize automotive intelligent systems to adjust safety performance and driver acceptance. Useful. In the conventional statistical analysis method, for example, a research method such as probability statistics, fuzzy mathematics, or rough set theory, or a driver motion characterization description method based on actual vehicle experimental data statistical analysis is used. However, since the driver's driving control operation itself is affected by various differences such as individual differences, age differences, gender differences and regional differences, a large amount of data samples are often required when using conventional research methods. Brings great difficulty to.

上述した異なる運転者の運転制御動作に違いが存在する以外、現在の自動車インテリジ
ェントシステムは、さらに道路環境の複雑さ、走行動作の違い、走行状況の変動性などに
よって制御され、実際の応用において誤警報率が高く、受け入れ可能性が悪いなどの問題
が依然として存在する。 Apart from the differences in driving control behaviors of different drivers mentioned above, current automotive intelligent systems are further controlled by the complexity of the road environment, differences in driving behavior, variability of driving conditions, etc., and are erroneous in actual applications. Problems such as high alert rates and poor acceptability still exist.

したがって、運転者個人の動作変動及び違いに対する自動車インテリジェントシステム
の適応性を向上させるため、運転者の運転制御メカニズムを深く研究する必要がある。し
たがって、運転者の運転室運転制御メカニズムの定量的記述方法に対して新しい設計を専
門に行う必要がある。 Therefore, it is necessary to deeply study the driving control mechanism of the driver in order to improve the adaptability of the automobile intelligent system to the movement fluctuation and the difference of the individual driver. Therefore, it is necessary to specialize in new designs for the quantitative description method of the driver's cab operation control mechanism.

本発明の目的は、最小アクション量原理に基づく運転者制御動作定量化方法及び装置を
提供することにあり、該方法が運転者の車両及び交通環境における情報の収集に従って、
最小アクション量原理を利用し、運転者が運転プロセスにおいて効率を最大化しながらリ
スクを回避するという運転制御メカニズムを記述することができる。 An object of the present invention is to provide a driver control motion quantification method and device based on the principle of minimum action quantity, the method according to the collection of information in the driver's vehicle and traffic environment.
Using the minimum amount of action principle, it is possible to describe a driving control mechanism in which the driver avoids risks while maximizing efficiency in the driving process.

上記目的を達成するために、本発明は、最小アクション量原理に基づく運転者制御動作
定量化方法を提供し、前記最小アクション量原理に基づく運転者制御動作定量化方法は以
下のステップを含み、
ステップＳ１、自走式車両の電子制御ユニットに運転者制御メカニズム定量的記述モジ
ュールを予め設置し、前記運転者制御メカニズム定量的記述モジュールが運転者の運転制
御動作の交通リスクと通行効率を同時に反映するアクション量

Figure 0007072133000001
を含み、
Figure 0007072133000002
の数式が式（１）であり、
Figure 0007072133000003
式中、
Figure 0007072133000004
が前記予め設定された交通プロセスにおける自走式車両のアクション量を表し、
Figure 0007072133000005
が前記予め設定された交通プロセスの開始時点であり、
Figure 0007072133000006
が前記予め設定された交通プロセスの終了時点であり、
Figure 0007072133000007
が予め設定された交通プロセスにおける自走式車両のラグランジュ量を表し、
Figure 0007072133000008
の式が次のとおりであり、
Figure 0007072133000009
であり、
式中、
Figure 0007072133000010
が自走式車両の運動エネルギーを表し、
Figure 0007072133000011
が、自走式車両が受けるポテンシャルエネルギーを表し、該ポテンシャルエネルギーが予
め設定された、車の流れの方向に沿って存在する一定の力場と抵抗場によって決定される
。 In order to achieve the above object, the present invention provides a driver control motion quantification method based on the minimum action quantity principle, and the driver control motion quantification method based on the minimum action quantity principle includes the following steps.
Step S1, a driver control mechanism quantitative description module is installed in advance in the electronic control unit of the self-propelled vehicle, and the driver control mechanism quantitative description module simultaneously reflects the traffic risk and traffic efficiency of the driver's driving control operation. Amount of action to do
Figure 0007072133000001
Including
Figure 0007072133000002
The formula of is formula (1),
Figure 0007072133000003
During the ceremony
Figure 0007072133000004
Represents the amount of action of the self-propelled vehicle in the preset traffic process.
Figure 0007072133000005
Is the start time of the preset transportation process,
Figure 0007072133000006
Is the end of the preset transportation process,
Figure 0007072133000007
Represents the amount of self-propelled vehicle lagrange in a preset traffic process.
Figure 0007072133000008
The formula is as follows:
Figure 0007072133000009
And
During the ceremony
Figure 0007072133000010
Represents the kinetic energy of a self-propelled vehicle
Figure 0007072133000011
Represents the potential energy received by a self-propelled vehicle, which is determined by a preset force field and resistance field existing along the direction of the vehicle flow.

ステップＳ２、自走式車両の情報収集装置により、時間と同期した自走式車両情報及び
交通環境情報を取得する。 Step S2, the information collecting device of the self-propelled vehicle acquires the self-propelled vehicle information and the traffic environment information synchronized with the time.

ステップＳ３、時間と同期した前記自走式車両情報及び交通環境情報に基づき、運転者
制御メカニズム定量的記述モジュールにおける

Figure 0007072133000012
で現在の走行指導速度を取得し、前記アクション量
Figure 0007072133000013
の値を最小にする。 Step S3, in the driver control mechanism quantitative description module based on the self-propelled vehicle information and the traffic environment information synchronized with the time.
Figure 0007072133000012
Obtain the current driving guidance speed with, and the above-mentioned action amount
Figure 0007072133000013
Minimize the value of.

さらに、前記アクション量

Figure 0007072133000014
の取得方法は、
テスト車両に交通環境情報収集装置を取り付けることで、車両プラットフォームを構築
するステップＳ１１と、
異なる運転者が前記車両プラットフォームを運転して異なる環境において自由走行テス
トを行うことで、時間と同期した自走式車両及び環境に関連するテストデータを収集する
ステップＳ１２と、
前記テストデータに基づき、任意の交通環境における前記アクション量
Figure 0007072133000015
の数式を取得するステップＳ１３とを含む。 Furthermore, the action amount
Figure 0007072133000014
How to get
Step S11 to build a vehicle platform by attaching a traffic environment information collection device to the test vehicle,
Step S12, in which different drivers drive the vehicle platform and perform free driving tests in different environments to collect time-synchronized self-propelled vehicles and environment-related test data.
Based on the test data, the amount of action in any traffic environment
Figure 0007072133000015
Includes step S13 to obtain the formula of.

さらに、Ｓ１３ステップでの「交通環境」は、単一の車両が直線道路を走行することで
あり、車の流れの方向に沿って一定の力場及び転がり抵抗、坂道抵抗、加速抵抗及び空気
抵抗に起因する抵抗場が存在することを予め設定し、前記ラグランジュ量

Figure 0007072133000016
が次のように表され、
Figure 0007072133000017

Figure 0007072133000018

Figure 0007072133000019
、
式中、
Figure 0007072133000020
が車両の質量であり、
Figure 0007072133000021
が車両の縦方向の変位であり、
Figure 0007072133000022
が車両の縦方向の速度であり、
Figure 0007072133000023
が車両の縦方向の加速度であり、
Figure 0007072133000024
が重力加速度であり、
Figure 0007072133000025
が転がり抵抗係数であり、
Figure 0007072133000026
が勾配であり、
Figure 0007072133000027
が車両の風抵抗係数であり、
Figure 0007072133000028
が車両の風上面積であり、
Figure 0007072133000029
が車両回転質量換算係数である。 Further, the "traffic environment" in the S13 step is that a single vehicle travels on a straight road, and has a constant force field and rolling resistance, slope resistance, acceleration resistance and air resistance along the direction of the vehicle flow. It is set in advance that there is a resistance field due to the above, and the amount of Lagrange
Figure 0007072133000016
Is expressed as
Figure 0007072133000017

Figure 0007072133000018

Figure 0007072133000019
,
During the ceremony
Figure 0007072133000020
Is the mass of the vehicle,
Figure 0007072133000021
Is the vertical displacement of the vehicle,
Figure 0007072133000022
Is the vertical speed of the vehicle,
Figure 0007072133000023
Is the vertical acceleration of the vehicle,
Figure 0007072133000024
Is the gravitational acceleration,
Figure 0007072133000025
Is the rolling resistance coefficient,
Figure 0007072133000026
Is a gradient,
Figure 0007072133000027
Is the drag coefficient of the vehicle,
Figure 0007072133000028
Is the windward area of the vehicle,
Figure 0007072133000029
Is the vehicle rotation mass conversion coefficient.

さらに、Ｓ１３ステップでの「交通環境」は、単一の車両が車線

Figure 0007072133000030
又は道路境界のある直線道路を走行することであり、車の流れの方向に沿って一定の力場
及び転がり抵抗、坂道抵抗、加速抵抗及び空気抵抗に起因する抵抗場が存在することを予
め設定し、前記ラグランジュ量
Figure 0007072133000031
が次のように表され、
Figure 0007072133000032

Figure 0007072133000033
Figure 0007072133000034
Figure 0007072133000035
Figure 0007072133000036
式中、
Figure 0007072133000037
が車両の質量であり、
Figure 0007072133000038
が車両の縦方向の変位であり、
Figure 0007072133000039
が車両の縦方向の速度であり、
Figure 0007072133000040
が車両の縦方向の加速度であり、
Figure 0007072133000041
が車両の横方向の変位であり、
Figure 0007072133000042
が
Figure 0007072133000043
の一次導関数を表し、車両の横方向の速度であり、
Figure 0007072133000044
が重力加速度であり、
Figure 0007072133000045
が転がり抵抗係数であり、
Figure 0007072133000046
が勾配であり、
Figure 0007072133000047
が車両の風抵抗係数であり、
Figure 0007072133000048
が車両の風上面積であり、
Figure 0007072133000049
が車両の回転質量換算係数であり、
Figure 0007072133000050
が
Figure 0007072133000051
に位置する車線
Figure 0007072133000052
又は道路境界によって形成されたポテンシャルエネルギー場の
Figure 0007072133000053
でのベクトル場の強さであり、
Figure 0007072133000054
が車線
Figure 0007072133000055
又は道路境界のタイプを表し、
Figure 0007072133000056
が車線
Figure 0007072133000057
又は道路境界の道路影響因子を表し、
Figure 0007072133000058
が車線の幅を表し、
Figure 0007072133000059
が車線
Figure 0007072133000060
又は道路境界から車両の重心
Figure 0007072133000061
までの距離ベクトルを表し、
Figure 0007072133000062
が調整係数であり、
Figure 0007072133000063
が車両の等価質量を表し、
Figure 0007072133000064
が自走式車両が位置する場所の道路影響因子を表し、
Figure 0007072133000065
が運転者影響因子を表す。 Furthermore, in the "traffic environment" in step S13, a single vehicle is in the lane.
Figure 0007072133000030
Or, it is to drive on a straight road with a road boundary, and it is preset that there is a constant force field and a resistance field due to rolling resistance, slope resistance, acceleration resistance and air resistance along the direction of the flow of the vehicle. And the amount of Lagrange
Figure 0007072133000031
Is expressed as
Figure 0007072133000032

Figure 0007072133000033
Figure 0007072133000034
Figure 0007072133000035
Figure 0007072133000036
During the ceremony
Figure 0007072133000037
Is the mass of the vehicle,
Figure 0007072133000038
Is the vertical displacement of the vehicle,
Figure 0007072133000039
Is the vertical speed of the vehicle,
Figure 0007072133000040
Is the vertical acceleration of the vehicle,
Figure 0007072133000041
Is the lateral displacement of the vehicle,
Figure 0007072133000042
But
Figure 0007072133000043
Represents the first derivative, the lateral speed of the vehicle,
Figure 0007072133000044
Is the gravitational acceleration,
Figure 0007072133000045
Is the rolling resistance coefficient,
Figure 0007072133000046
Is a gradient,
Figure 0007072133000047
Is the drag coefficient of the vehicle,
Figure 0007072133000048
Is the windward area of the vehicle,
Figure 0007072133000049
Is the rotational mass conversion coefficient of the vehicle,
Figure 0007072133000050
But
Figure 0007072133000051
Lane located in
Figure 0007072133000052
Or the potential energy field formed by the road boundary
Figure 0007072133000053
Is the strength of the vector field in
Figure 0007072133000054
Is in the lane
Figure 0007072133000055
Or represents the type of road boundary,
Figure 0007072133000056
Is in the lane
Figure 0007072133000057
Or, it represents the road influence factor of the road boundary,
Figure 0007072133000058
Represents the width of the lane,
Figure 0007072133000059
Is in the lane
Figure 0007072133000060
Or the center of gravity of the vehicle from the road boundary
Figure 0007072133000061
Represents the distance vector to
Figure 0007072133000062
Is the adjustment factor,
Figure 0007072133000063
Represents the equivalent mass of the vehicle,
Figure 0007072133000064
Represents the road impact factor where the self-propelled vehicle is located.
Figure 0007072133000065
Represents the driver influence factor.

さらに、Ｓ１３ステップでの「交通環境」は、車追従モードにおける単一の車両が直線
道路を走行することであり、車の流れの方向に沿って重力に類似する一定の力場及び転が
り抵抗、坂道抵抗、加速抵抗及び空気抵抗に起因する抵抗場が存在すると仮定すると、前
記ラグランジュ量

Figure 0007072133000066
が次のように表され、
Figure 0007072133000067
Figure 0007072133000068
Figure 0007072133000069
Figure 0007072133000070
Figure 0007072133000071
式中、
Figure 0007072133000072
が車両の質量であり、
Figure 0007072133000073
が車両の縦方向の変位であり、
Figure 0007072133000074
が車両の縦方向の速度であり、
Figure 0007072133000075
が車両の縦方向の加速度であり、
Figure 0007072133000076
が車両の横方向の変位であり、
Figure 0007072133000077
が車両の横方向の速度であり、
Figure 0007072133000078
が重力加速度であり、
Figure 0007072133000079
が転がり抵抗係数であり、
Figure 0007072133000080
が勾配であり、
Figure 0007072133000081
が車両の風抵抗係数であり、
Figure 0007072133000082
が車両の風上面積であり、
Figure 0007072133000083
が車両の回転質量換算係数であり、
Figure 0007072133000084
が
Figure 0007072133000085
に位置する車線
Figure 0007072133000086
又は道路境界によって形成されたポテンシャルエネルギー場の
Figure 0007072133000087
でのベクトル場の強さであり、
Figure 0007072133000088
が車線
Figure 0007072133000089
又は道路境界のタイプを表し、
Figure 0007072133000090
が
Figure 0007072133000091
車線又は道路境界の道路影響因子を表し、
Figure 0007072133000092
が車線の幅を表し、
Figure 0007072133000093
が車線
Figure 0007072133000094
又は道路境界から車両の重心
Figure 0007072133000095
までの距離ベクトルを表し、
Figure 0007072133000096
が調整係数であり、
Figure 0007072133000097
が車両の等価質量を表し、
Figure 0007072133000098
が自走式車両が位置する場所の道路影響因子を表し、
Figure 0007072133000099
が運転者影響因子を表し、
Figure 0007072133000100
が
Figure 0007072133000101
番目の車両に対して物体jによって生成されたポテンシャルエネルギーを表し、
Figure 0007072133000102
が車線
Figure 0007072133000103
を表し、
Figure 0007072133000104
が合計
Figure 0007072133000105
本の車線があることを表し、
Figure 0007072133000106
がｎ個の道路利用者がいることを表し、
Figure 0007072133000107
が１個の調整係数である。 Further, the "traffic environment" in the S13 step is that a single vehicle in the vehicle following mode travels on a straight road, and has a constant force field and rolling resistance similar to gravity along the direction of the vehicle flow. Assuming that there is a resistance field due to slope resistance, acceleration resistance and air resistance, the amount of Lagrange
Figure 0007072133000066
Is expressed as
Figure 0007072133000067
Figure 0007072133000068
Figure 0007072133000069
Figure 0007072133000070
Figure 0007072133000071
During the ceremony
Figure 0007072133000072
Is the mass of the vehicle,
Figure 0007072133000073
Is the vertical displacement of the vehicle,
Figure 0007072133000074
Is the vertical speed of the vehicle,
Figure 0007072133000075
Is the vertical acceleration of the vehicle,
Figure 0007072133000076
Is the lateral displacement of the vehicle,
Figure 0007072133000077
Is the lateral speed of the vehicle,
Figure 0007072133000078
Is the gravitational acceleration,
Figure 0007072133000079
Is the rolling resistance coefficient,
Figure 0007072133000080
Is a gradient,
Figure 0007072133000081
Is the drag coefficient of the vehicle,
Figure 0007072133000082
Is the windward area of the vehicle,
Figure 0007072133000083
Is the rotational mass conversion coefficient of the vehicle,
Figure 0007072133000084
But
Figure 0007072133000085
Lane located in
Figure 0007072133000086
Or the potential energy field formed by the road boundary
Figure 0007072133000087
Is the strength of the vector field in
Figure 0007072133000088
Is in the lane
Figure 0007072133000089
Or represents the type of road boundary,
Figure 0007072133000090
But
Figure 0007072133000091
Represents a road impact factor for lanes or road boundaries
Figure 0007072133000092
Represents the width of the lane,
Figure 0007072133000093
Is in the lane
Figure 0007072133000094
Or the center of gravity of the vehicle from the road boundary
Figure 0007072133000095
Represents the distance vector to
Figure 0007072133000096
Is the adjustment factor,
Figure 0007072133000097
Represents the equivalent mass of the vehicle,
Figure 0007072133000098
Represents the road impact factor where the self-propelled vehicle is located.
Figure 0007072133000099
Represents the driver impact factor
Figure 0007072133000100
But
Figure 0007072133000101
Represents the potential energy generated by the object j for the second vehicle,
Figure 0007072133000102
Is in the lane
Figure 0007072133000103
Represents
Figure 0007072133000104
Is the total
Figure 0007072133000105
Indicates that there is a book lane
Figure 0007072133000106
Indicates that there are n road users,
Figure 0007072133000107
Is one adjustment coefficient.

本発明は、最小アクション量原理に基づく運転者制御動作定量化装置を提供し、前記運
転者制御動作定量化装置は、
自走式車両に設置され、時間と同期した自走式車両情報及び交通環境情報を取得するこ
とに用いられる情報収集装置と、
電子制御ユニットであって、前記電子制御ユニットに運転者制御メカニズム定量的記述モ
ジュールを予め設置し、前記運転者制御メカニズム定量的記述モジュールが運転者の運転
制御動作の交通リスクと通行効率を同時に反映するアクション量

Figure 0007072133000108
を含み、
Figure 0007072133000109
の数式が式（１）であり、
Figure 0007072133000110
（1）

式中、
Figure 0007072133000111
が前記予め設定された交通プロセスにおける自走式車両のアクション量を表し、
Figure 0007072133000112
が前記予め設定された交通プロセスの開始時点であり、
Figure 0007072133000113
が前記予め設定された交通プロセスの終了時点であり、
Figure 0007072133000114
が予め設定された交通プロセスにおける自走式車両のラグランジュ量を表し、
Figure 0007072133000115
の式が次のとおりであり、
Figure 0007072133000116
式中、
Figure 0007072133000117
が自走式車両の運動エネルギーを表し、
Figure 0007072133000118
が車両が受けるポテンシャルエネルギーを表し、該ポテンシャルエネルギーが予め設定さ
れた、車の流れの方向に沿って存在する一定の力場と抵抗場によって決定される電子制御
ユニットとを備え、
前記電子制御ユニットは、時間と同期した前記自走式車両情報及び交通環境情報に基づ
き、
Figure 0007072133000119
で現在の走行指導速度を取得し、
Figure 0007072133000120
アクション量の値を最小にすることに用いられる。 The present invention provides a driver-controlled motion quantifying device based on the principle of minimum action quantity, and the driver-controlled motion quantifying device is a device.
An information collection device installed in a self-propelled vehicle and used to acquire time-synchronized self-propelled vehicle information and traffic environment information.
In the electronic control unit, a driver control mechanism quantitative description module is installed in advance in the electronic control unit, and the driver control mechanism quantitative description module simultaneously reflects the traffic risk and traffic efficiency of the driver's operation control operation. Amount of action to do
Figure 0007072133000108
Including
Figure 0007072133000109
The formula of is formula (1),
Figure 0007072133000110
(1)

During the ceremony
Figure 0007072133000111
Represents the amount of action of the self-propelled vehicle in the preset traffic process.
Figure 0007072133000112
Is the start time of the preset transportation process,
Figure 0007072133000113
Is the end of the preset transportation process,
Figure 0007072133000114
Represents the amount of self-propelled vehicle lagrange in a preset traffic process.
Figure 0007072133000115
The formula is as follows:
Figure 0007072133000116
During the ceremony
Figure 0007072133000117
Represents the kinetic energy of a self-propelled vehicle
Figure 0007072133000118
Represents the potential energy that the vehicle receives, and is equipped with an electronic control unit in which the potential energy is predetermined and is determined by a constant force field and resistance field existing along the direction of the vehicle flow.
The electronic control unit is based on the self-propelled vehicle information and the traffic environment information synchronized with the time.
Figure 0007072133000119
Get the current driving guidance speed at
Figure 0007072133000120
It is used to minimize the value of the amount of action.

本発明は、インテリジェント車両をさらに提供し、インテリジェント車両は前記運転者
制御動作定量化装置を備える。 The present invention further provides an intelligent vehicle, which comprises the driver control motion quantifier.

本発明の有益な効果は、本発明に係る最小アクション量原理に基づく運転者運転制御メ
カニズム定量化記述方法及びその装置では、６４線レーザーレーダー、ミリ波レーダー及
びビジョンセンサーで構成されたマルチセンサー検知システムを用いてインテリジェント
車両プラットフォームを構築し、周囲の移動物体、静止物体の位置情報と状態情報を識別
でき、大量のデータを収集し、データベースを構築し、車両走行中の動的交通システムの
特徴を分析し、運転者の運転制御メカニズムを識別し、そして運転者の運転制御メカニズ
ムを定量的に記述することで、さらに運転者の運転動作を定量的に分析することができる
ことである。 The beneficial effect of the present invention is the multi-sensor detection composed of a 64-line laser radar, a millimeter-wave radar, and a vision sensor in the driver driving control mechanism quantification description method and the device thereof based on the minimum action quantity principle according to the present invention. Using the system to build an intelligent vehicle platform, it can identify the position and state information of surrounding moving and stationary objects, collect a large amount of data, build a database, and feature dynamic traffic systems while the vehicle is running. By analyzing, identifying the driver's driving control mechanism, and quantitatively describing the driver's driving control mechanism, it is possible to further quantitatively analyze the driver's driving behavior.

本発明における車両プラットフォームの側面図である。It is a side view of the vehicle platform in this invention. 図１に示す車両プラットフォームの平面図である。It is a top view of the vehicle platform shown in FIG. 本発明に係る単一の自走式車両の交通システムの概略図である。It is a schematic diagram of the traffic system of a single self-propelled vehicle which concerns on this invention. 本発明に係る車線の車両に対する制約ポテンシャルエネルギーモデルを説明する概略図である。It is a schematic diagram explaining the constraint potential energy model for the vehicle of the lane which concerns on this invention. 本発明に係る車両追跡シーンの概略図である。It is a schematic diagram of the vehicle tracking scene which concerns on this invention.

図面において、同じ又は類似の素子又は同じ又は類似の機能を備えた素子を同じ又は類
似の記号で表す。以下、図面を参照しながら本発明の実施例を詳しく説明する。 In the drawings, the same or similar elements or elements having the same or similar functions are represented by the same or similar symbols. Hereinafter, embodiments of the present invention will be described in detail with reference to the drawings.

本実施例に係る最小アクション量原理に基づく運転者制御動作定量化方法は、主に外部
環境の全ての要因の影響を受ける自走式車両を考慮し、以下のステップを含み、Ｓ１ステ
ップ、自走式車両の電子制御ユニットに運転者制御メカニズム定量的記述モジュールを予
め設置し、前記運転者制御メカニズム定量的記述モジュールが運転者の運転制御動作の交
通リスクと通行効率を同時に反映するアクション量

Figure 0007072133000121
を含み、
Figure 0007072133000122
の数式が式（１）であり、
Figure 0007072133000123
式中、
Figure 0007072133000124
が前記予め設定された交通プロセスにおける自走式車両のアクション量を表し、即ち予め
設定された交通プロセスにおける自走式車両の経時的なラグランジュ量の積分として表さ
れてもよく、
Figure 0007072133000125
が前記予め設定された交通プロセスの開始時点であり、
Figure 0007072133000126
が前記予め設定された交通プロセスの終了時点であり、
Figure 0007072133000127
が予め設定された交通プロセスにおける自走式車両のラグランジュ量を表し、
Figure 0007072133000128
の式が次のとおりであり、

Figure 0007072133000129
式中、
Figure 0007072133000130
が自走式車両の運動エネルギーを表し、
Figure 0007072133000131
は自走式車両が受けるポテンシャルエネルギーを表し、該ポテンシャルエネルギーを予め
設定した、車の流れの方向に沿って存在する一定の力場と抵抗場によって決定される。 The driver control operation quantification method based on the minimum action amount principle according to this embodiment mainly considers a self-propelled vehicle affected by all factors of the external environment, and includes the following steps, S1 step, self. A driver control mechanism quantitative description module is installed in advance in the electronic control unit of the running vehicle, and the driver control mechanism quantitative description module simultaneously reflects the traffic risk and traffic efficiency of the driver's driving control operation.
Figure 0007072133000121
Including
Figure 0007072133000122
The formula of is formula (1),
Figure 0007072133000123
During the ceremony
Figure 0007072133000124
Represents the amount of action of the self-propelled vehicle in the preset traffic process, that is, may be expressed as the integral of the amount of lagrange of the self-propelled vehicle over time in the preset traffic process.
Figure 0007072133000125
Is the start time of the preset transportation process,
Figure 0007072133000126
Is the end of the preset transportation process,
Figure 0007072133000127
Represents the amount of self-propelled vehicle lagrange in a preset traffic process.
Figure 0007072133000128
The formula is as follows:

Figure 0007072133000129
During the ceremony
Figure 0007072133000130
Represents the kinetic energy of a self-propelled vehicle
Figure 0007072133000131
Represents the potential energy received by a self-propelled vehicle, and the potential energy is determined by a predetermined force field and resistance field existing along the direction of the vehicle flow.

ステップＳ２ステップ、自走式車両の情報収集装置により、時間と同期した自走式車両
情報及び交通環境情報を取得する。ステップＳ２により、情報収集装置は、自走式車両の
交通環境情報を検出及び識別することができ、該交通環境情報が周囲の障害物（車両、サ
イクリスト、歩行者、フェンス、バリケード）と交通情報（信号機、速度制限標識、車線
）を含む。自走式車両情報は自走式車両ＣＡＮデータを含み、具体的には、エンジン回転
数、ハンドル角度、車両速度、ギア、加速及び減速とＧＰＳ情報である。 Step S2 Step, the information collecting device of the self-propelled vehicle acquires the self-propelled vehicle information and the traffic environment information synchronized with the time. By step S2, the information collecting device can detect and identify the traffic environment information of the self-propelled vehicle, and the traffic environment information is the surrounding obstacles (vehicle, cyclist, pedestrian, fence, barricade) and traffic information. Includes (traffic lights, speed limit signs, lanes). The self-propelled vehicle information includes self-propelled vehicle CAN data, specifically, engine speed, steering wheel angle, vehicle speed, gear, acceleration and deceleration, and GPS information.

ステップＳ３ステップ、時間と同期した前記車両情報及び交通環境情報に基づき、運転
者制御メカニズム定量的記述モジュールにおける

Figure 0007072133000132
で現在の走行指導速度を取得し、前記アクション量
Figure 0007072133000133
の値を最小にする。 Step S3 In the driver control mechanism quantitative description module based on the vehicle information and traffic environment information synchronized with the time.
Figure 0007072133000132
Obtain the current driving guidance speed with, and the above-mentioned action amount
Figure 0007072133000133
Minimize the value of.

本実施例に係る方法では、インテリジェント車両が走行するプロセスにおいて、情報収
集装置が自走式車両の周辺環境での障害物又は交通情報を検出した場合、前記運転者制御
メカニズム定量的記述モジュールは予め設定された交通プロセスにおけるアクション量

Figure 0007072133000134
を計算し、前記アクション量
Figure 0007072133000135
の値を最小にすることで、一つのインテリジェント車両が走行する現在の速度の最適値を
取得し、該最適値が現在の走行指導速度とされる。インテリジェント車両が既に取得した
現在の走行指導速度で速度制御を行う場合、最適な自己安全性及び通行効率を達成するこ
とができる。 In the method according to the present embodiment, when the information collecting device detects an obstacle or traffic information in the surrounding environment of the self-propelled vehicle in the process in which the intelligent vehicle travels, the driver control mechanism quantitative description module is previously used. Amount of action in the set traffic process
Figure 0007072133000134
Is calculated, and the above action amount
Figure 0007072133000135
By minimizing the value of, the optimum value of the current speed at which one intelligent vehicle travels is acquired, and the optimum value is taken as the current traveling guidance speed. Optimal self-safety and traffic efficiency can be achieved when speed control is performed at the current driving guidance speed already acquired by the intelligent vehicle.

一実施例では、前記アクション量

Figure 0007072133000136
の取得方法は、
テスト車両に交通環境情報収集装置を取り付けることで、車両プラットフォームを構築
するステップＳ１１と、
異なる運転者が前記車両プラットフォームを運転して異なる環境において自由走行テス
トを行うことで、時間と同期した自走式車両及び環境に関連するテストデータを収集する
ステップＳ１２と、
前記テストデータに基づき、任意の交通環境における前記アクション量
Figure 0007072133000137
の数式を取得するステップＳ１３とを含む。 In one embodiment, the action amount
Figure 0007072133000136
How to get
Step S11 to build a vehicle platform by attaching a traffic environment information collection device to the test vehicle,
Step S12, in which different drivers drive the vehicle platform and perform free driving tests in different environments to collect time-synchronized self-propelled vehicles and environment-related test data.
Based on the test data, the amount of action in any traffic environment
Figure 0007072133000137
Includes step S13 to obtain the formula of.

ステップＳ１１において、テスト車両に、時間と同期したターゲットオブジェクトの位
置情報及び動き情報を取得するためのレーダーとビジョンセンサーを取り付ける。図１と
図２に示すように、Ｓ１１は具体的には、以下のステップを含み、
ステップＳ１１１、テスト車両の上部に、６４線レーザーレーダー１を取り付け、６４
線レーザーレーダー１がターゲットオブジェクトの縦方向及び横方向の座標位置、種類の
センサーの生データを取得することに用いられる。 In step S11, the test vehicle is equipped with a radar and a vision sensor for acquiring the position information and motion information of the target object synchronized with the time. Specifically, as shown in FIGS. 1 and 2, S11 includes the following steps.
Step S111, attach the 64-line laser radar 1 to the top of the test vehicle, 64
The line laser radar 1 is used to acquire the raw data of the vertical and horizontal coordinate positions and types of sensors of the target object.

ステップＳ１１２、テスト車両の前、後、左、右の４つの方向に第１のミリ波レーダー
２ａ、第２のミリ波レーダー２ｂ、第３のミリ波レーダー２ｃ、第４のミリ波レーダー２
ｄ及び第１のビジョンセンサー３ａ、第２のビジョンセンサー３ｂ、第３のビジョンセン
サー３ｃ、第４のビジョンセンサー３ｄをそれぞれ取り付け、各ビジョンセンサーとミリ
波レーダーによってターゲットオブジェクトの速度、加速度、縦方向及び横方向の位置情
報を取得する。 Step S112, the first millimeter-wave radar 2a, the second millimeter-wave radar 2b, the third millimeter-wave radar 2c, and the fourth millimeter-wave radar 2 in the four directions of front, rear, left, and right of the test vehicle.
d and the first vision sensor 3a, the second vision sensor 3b, the third vision sensor 3c, and the fourth vision sensor 3d are attached, and the speed, acceleration, and vertical direction of the target object are measured by each vision sensor and millimeter-wave radar. And the position information in the horizontal direction is acquired.

ステップＳ１１３、Ｓ１１ステップ及びＳ１１２ステップにおける６４線レーザーレー
ダー１及び各ミリ波レーダーとビジョンセンサーの前記テスト車両での位置に対してキャ
リブレーションを行う。キャリブレーション方法について従来のキャリブレーション方法
を用いて実現することができる。 Calibration is performed on the positions of the 64-line laser radar 1 and each millimeter-wave radar and vision sensor in the test vehicle in steps S113, S11, and S112. The calibration method can be realized by using a conventional calibration method.

なお、ステップＳ１１で各センサーによって収集されたデータがいずれもセンサーの生
データであるため、後のステップで生データを応用する前に、いずれもターゲットデータ
に解析する必要がある。生データは、カメラで撮影された写真、ビデオ、レーザーレーダ
ーでスキャンされたポイントクラウド、ミリ波レーダーで受信されたミリ波信号である。
ターゲットデータは、上記の３種センサーの生データを融合した後、歩行者、サイクリス
ト、車両などのターゲットオブジェクトの速度、位置データを取得する。「データ融合」
方法は、
レーザーレーダーが特徴抽出とポイントクラウドクラスタリング方法を用いてターゲッ
トを検出し、正確なターゲット位置情報を取得し、ビジョンセンサーが道路ターゲットに
対して機械学習に基づくターゲット検出を行い、レーザーレーダーによるターゲット検出
のためのターゲットカテゴリ情報を提供し、ミリ波レーダーが動的ターゲットを識別して
正確なターゲット速度と位置情報を提供することである。データ関連付け方法により、各
センサーで検出された同じターゲット情報をマッチングし、最終的に正確なターゲットオ
ブジェクトの位置情報と動き情報即ち座標、速度、加速度を取得する。 Since the data collected by each sensor in step S11 is the raw data of the sensor, it is necessary to analyze all of them into the target data before applying the raw data in a later step. Raw data are photographs taken by cameras, videos, point clouds scanned by laser radar, and millimeter wave signals received by millimeter wave radar.
For the target data, after fusing the raw data of the above three types of sensors, the speed and position data of the target objects such as pedestrians, cyclists, and vehicles are acquired. "Data fusion"
The method is
Laser radar detects targets using feature extraction and point cloud clustering methods, obtains accurate target position information, vision sensors perform machine learning-based target detection on road targets, and laser radar detects targets. It provides target category information for the millimeter-wave radar to identify dynamic targets and provide accurate target speed and location information. By the data association method, the same target information detected by each sensor is matched, and finally accurate position information and motion information of the target object, that is, coordinates, velocity, and acceleration are acquired.

したがって、本実施例では６４線レーザーレーダー、ミリ波レーダー及びビジョンセン
サーで構成されたマルチセンサー検知システムを用いて車両プラットフォームを構築し、
周囲の移動物体、静止物体の位置情報と状態情報を識別できる。 Therefore, in this embodiment, a vehicle platform is constructed using a multi-sensor detection system composed of a 64-line laser radar, a millimeter-wave radar, and a vision sensor.
It is possible to identify the position information and state information of surrounding moving objects and stationary objects.

いくつかの実施例では、ステップＳ１２での「運転者」の選択原則は、
運転経験が長く大きな交通事故を起こしなかった一定数の運転者を選択することを含む
。 In some embodiments, the "driver" selection principle in step S12 is
Includes selecting a certain number of drivers who have long driving experience and have not caused major traffic accidents.

「運転者」の数を可能な限り多くにし、このようにして可能な限り多くのテストデータ
を収集することで、より多くの運転者の運転習慣を考慮することができ、これにより、後
のステップＳ３及びステップＳ４で得られたリスク識別曲線は一般性と代表性をさらに有
し、リスク識別に対する運転者の受け入れ程度を向上させることに役立つ。 By increasing the number of "drivers" as much as possible and collecting as much test data as possible in this way, it is possible to consider the driving habits of more drivers, thereby later. The risk identification curves obtained in steps S3 and S4 have more generality and representativeness and help improve the driver's acceptance of risk identification.

ステップＳ１２での「自走式車両及び環境に関連するテストデータ」は、自走式車両の
テストデータと環境のテストデータを含み、
自走式車両のテストデータはレーダーとビジョンセンサーによって収集された、時間と
同期したターゲットオブジェクトの位置情報と動き情報及び自走式車両のＣＡＮデータを
含む。自走式車両のＣＡＮデータは、エンジン回転数、ハンドル角度、車両速度、ギア、
加速及び減速とＧＰＳ情報を含む。各前記レーダーとビジョンセンサーによって収集され
たデータを融合し、正確なターゲットオブジェクトの位置情報と動き情報即ち座標、速度
、加速度を取得する。センサーによって取得された情報は、主に自走式車両以外の環境に
おける他の道路利用者、障害物の速度、自車との相対する位置。 The "test data related to the self-propelled vehicle and the environment" in step S12 includes the test data of the self-propelled vehicle and the test data of the environment.
Self-propelled vehicle test data includes time-synchronized target object position and motion information and self-propelled vehicle CAN data collected by radar and vision sensors. CAN data for self-propelled vehicles includes engine speed, steering wheel angle, vehicle speed, gear,
Includes acceleration and deceleration and GPS information. The data collected by each of the radars and the vision sensor is fused to obtain accurate position information and motion information of the target object, that is, coordinates, velocity, and acceleration. The information acquired by the sensor is mainly the speed of other road users, obstacles, and the position facing the vehicle in an environment other than the self-propelled vehicle.

ステップＳ１２での「異なる環境」は、環境タイプ、交通参加者、交通標識、道路標識
、天気条件を含み、
環境タイプについては、一級タイプがキャンパス、工業団地、都市、高速道路がであり
、二級タイプが上り坂、下り坂、橋の上、橋の下、トンネル、直線道路、曲線道路であり
、
交通参加者については、一級タイプが自動車、非自動車、静止物体であり、二級タイプ
には、動力車両がセダン、バス、ミニバン、トラック、中型乗用車、オートバイ、その他
の自動車を含み、非動力車両が歩行者、サイクリスト、二輪車、他の非動力車両を含み、
静止物体がバリケード、フェンスなどを含み、
交通標識については、一級タイプが交通標識板、信号機、車線であり、二級タイプには
、交通標識板が速度制限、高さ制限、重量制限、指示、警告、禁止、その他の標識板を含
み、信号機が円形、矢印、歩行者パターン、二輪車パターンを含み、
道路標識については、一級タイプが車線と路面標識を含み、二級タイプには車線がシン
グル実線、二重実線と点線を含み、路面標識が直線矢印、右折矢印、左折矢印、他の路面
標識を含み、
天気条件については晴れ、曇り、雨、雪である。 The "different environment" in step S12 includes the environment type, traffic participants, traffic signs, road signs, weather conditions, and the like.
Regarding environment types, the first-class types are campuses, industrial zones, cities, and highways, and the second-class types are uphills, downhills, on bridges, under bridges, tunnels, straight roads, and curved roads.
For traffic participants, the first class type is automobiles, non-automobiles, stationary objects, and the second class type includes non-powered vehicles including sedans, buses, minivans, trucks, medium-sized passenger cars, motorcycles, and other automobiles. Includes pedestrians, cyclists, motorcycles and other non-powered vehicles,
Still objects include barricades, fences, etc.
For traffic signs, the first-class type is traffic signboards, traffic lights, lanes, and the second-class type includes traffic signboards including speed limits, height limits, weight limits, instructions, warnings, prohibitions, and other signboards. , Traffic lights include circular, arrows, pedestrian patterns, two-wheeled vehicle patterns,
For road signs, the first-class type includes lanes and road signs, the second-class type includes single solid lines, double solid lines and dotted lines, and road signs include straight arrows, right-turn arrows, left-turn arrows, and other road signs. Including,
The weather conditions are sunny, cloudy, rainy and snowy.

即ち、環境のテストデータは上記の「異なる環境」に示される様々な情報に対応する。
ステップＳ１２での時間と同期した「自走式車両及び環境に関連するテストデータ」は
データベースの方式によって記憶される。 That is, the test data of the environment corresponds to various information shown in the above "different environment".
The "test data related to the self-propelled vehicle and the environment" synchronized with the time in step S12 is stored by the method of the database.

以下、異なる「交通環境」について、前記アクション量

Figure 0007072133000138
の数式を説明する。
一、ステップＳ１３での「交通環境」は、単一の車両が直線道路を走行することであり
、図３に示すように、車の流れの方向に沿って重力に類似する一定の力場及び転がり抵抗
、坂道抵抗、加速抵抗及び空気抵抗に起因する抵抗場が存在することを予め設定し、前記
ラグランジュ量
Figure 0007072133000139
が次のように表され、
Figure 0007072133000140
Figure 0007072133000141
Figure 0007072133000142

式中、
Figure 0007072133000143
が自走式車両の縦方向の運動エネルギー
Figure 0007072133000144
を含み、
Figure 0007072133000145
が抵抗場
Figure 0007072133000146
と一定の力場
Figure 0007072133000147
を含み、
Figure 0007072133000148
が車両の質量であり、
Figure 0007072133000149
が車両の縦方向の変位であり、
Figure 0007072133000150
が
Figure 0007072133000151
一次導関数を表し、車両の縦方向の速度であり、
Figure 0007072133000152
が
Figure 0007072133000153
の二次導関数を表し、車両の縦方向の加速度であり、
Figure 0007072133000154
が重力加速度であり、
Figure 0007072133000155
が転がり抵抗係数であり、
Figure 0007072133000156
が勾配であり、
Figure 0007072133000157
が車両の風抵抗係数であり、
Figure 0007072133000158
が車両の風上面積であり、
Figure 0007072133000159
が車両回転質量換算係数であり、
Figure 0007072133000160
が自動車理論の関連コンテンツに基づき、一般には、１．０５であってもよい。 Below, the amount of action for different "traffic environment"
Figure 0007072133000138
Explain the formula of.
1. The "traffic environment" in step S13 is that a single vehicle travels on a straight road, and as shown in FIG. 3, a constant force field similar to gravity and a constant force field along the direction of the vehicle flow. It is set in advance that there is a resistance field due to rolling resistance, slope resistance, acceleration resistance and air resistance, and the amount of Lagrange
Figure 0007072133000139
Is expressed as
Figure 0007072133000140
Figure 0007072133000141
Figure 0007072133000142

During the ceremony
Figure 0007072133000143
Is the vertical kinetic energy of a self-propelled vehicle
Figure 0007072133000144
Including
Figure 0007072133000145
Is a resistance field
Figure 0007072133000146
And a constant force field
Figure 0007072133000147
Including
Figure 0007072133000148
Is the mass of the vehicle,
Figure 0007072133000149
Is the vertical displacement of the vehicle,
Figure 0007072133000150
But
Figure 0007072133000151
Represents the first derivative, the vertical speed of the vehicle,
Figure 0007072133000152
But
Figure 0007072133000153
Represents the quadratic derivative of the vehicle, which is the vertical acceleration of the vehicle.
Figure 0007072133000154
Is the gravitational acceleration,
Figure 0007072133000155
Is the rolling resistance coefficient,
Figure 0007072133000156
Is a gradient,
Figure 0007072133000157
Is the drag coefficient of the vehicle,
Figure 0007072133000158
Is the windward area of the vehicle,
Figure 0007072133000159
Is the vehicle rotation mass conversion coefficient,
Figure 0007072133000160
Is generally 1.05 based on the relevant content of automotive theory.

二、Ｓ１３ステップでの「交通環境」は、単一の車両が車線

Figure 0007072133000161
又は道路境界のある直線道路を走行することであり、図４に示すように、車の流れの方向
に沿って一定の力場及び転がり抵抗、坂道抵抗、加速抵抗及び空気抵抗に起因する抵抗場
が存在することを予め設定する。 2. In the "traffic environment" in step S13, a single vehicle is in the lane.
Figure 0007072133000161
Or traveling on a straight road with a road boundary, and as shown in FIG. 4, a constant force field and rolling resistance, slope resistance, acceleration resistance, and resistance field due to air resistance along the direction of the flow of the vehicle. Is preset to exist.

車線

Figure 0007072133000162
又は道路境界の影響を考慮する場合、走行安全フィールド理論に従って、
Figure 0007072133000163
に位置する車線
Figure 0007072133000164
又は道路境界で形成されたポテンシャルエネルギー場の
Figure 0007072133000165
でのベクトル場の強さ
Figure 0007072133000166
が次のように記述されてもよく、
Figure 0007072133000167
したがって、車線
Figure 0007072133000168
又は道路境界が近いほど、車両が受ける制約エネルギーが大きくなる。したがって、車線
Figure 0007072133000169
又は道路境界によって生成された運転安全フィールドの力が次のように記述されてもよく
、
Figure 0007072133000170
したがって、車線を考慮した単一の自走式車両の交通システムのラグランジュ量
Figure 0007072133000171
が次のように表され、
Figure 0007072133000172

Figure 0007072133000173
Figure 0007072133000174
式中、
Figure 0007072133000175
が自走式車両の縦方向の運動エネルギー
Figure 0007072133000176
と横方向の運動エネルギー
Figure 0007072133000177
を含み、
Figure 0007072133000178
が抵抗場
Figure 0007072133000179
、一定の力場
Figure 0007072133000180
と
Figure 0007072133000181
に位置する車線又
Figure 0007072133000182
は道路境界で形成されたポテンシャルエネルギー場の
Figure 0007072133000183
でのベクトル場の強さを含み、
Figure 0007072133000184
が車両の質量であり、
Figure 0007072133000185
が車両の縦方向の変位であり、
Figure 0007072133000186
が車両の縦方向の速度であり、
Figure 0007072133000187
が車両の縦方向の加速度であり、
Figure 0007072133000188
が車両の横方向の変位であり、
Figure 0007072133000189
が
Figure 0007072133000190
の一次導関数を表し、車両の横方向の速度であり、
Figure 0007072133000191
が重力加速度であり、
Figure 0007072133000192
が転がり抵抗係数であり、
Figure 0007072133000193
が勾配であり、
Figure 0007072133000194
が車両の風抵抗係数であり、
Figure 0007072133000195
が車両の風上面積であり、
Figure 0007072133000196
が車両の回転質量換算係数であり、
Figure 0007072133000197
が車線
Figure 0007072133000198
又は道路境界のタイプであり、その大きさが交通規制によって決定され（例えば、白い実
線が白い破線に対応する値よりも大きい）、
Figure 0007072133000199
が車線
Figure 0007072133000200
又は道路境界の道路影響因子を表し、
Figure 0007072133000201
が車線の幅を表し、
Figure 0007072133000202
が車線
Figure 0007072133000203
又は道路境界から車両の重心
Figure 0007072133000204
までの距離ベクトルを表し、
Figure 0007072133000205
車線
Figure 0007072133000206
又は道路境界から図４における白い長方形ブロックで示される車両の重心
Figure 0007072133000207
までの距離ベクトルを表し、
Figure 0007072133000208
の範囲が
Figure 0007072133000209
であり、
Figure 0007072133000210
が調整係数であり、
Figure 0007072133000211
が車両の等価質量を表し、
Figure 0007072133000212
が自走式車両が位置する場所の道路影響因子を表し、
Figure 0007072133000213
が運転者影響因子を表す。 Lane
Figure 0007072133000162
Or when considering the effects of road boundaries, according to the driving safety field theory
Figure 0007072133000163
Lane located in
Figure 0007072133000164
Or the potential energy field formed at the road boundary
Figure 0007072133000165
The strength of the vector field in
Figure 0007072133000166
May be written as
Figure 0007072133000167
Therefore, the lane
Figure 0007072133000168
Or, the closer the road boundary is, the greater the constraint energy that the vehicle receives. Therefore, the lane
Figure 0007072133000169
Alternatively, the force of the driving safety field generated by the road boundary may be described as:
Figure 0007072133000170
Therefore, the amount of lagrange of the transportation system of a single self-propelled vehicle considering the lane
Figure 0007072133000171
Is expressed as
Figure 0007072133000172

Figure 0007072133000173
Figure 0007072133000174
During the ceremony
Figure 0007072133000175
Is the vertical kinetic energy of a self-propelled vehicle
Figure 0007072133000176
And lateral kinetic energy
Figure 0007072133000177
Including
Figure 0007072133000178
Is a resistance field
Figure 0007072133000179
, Constant force field
Figure 0007072133000180
When
Figure 0007072133000181
In the lane located in
Figure 0007072133000182
Is the potential energy field formed at the road boundary
Figure 0007072133000183
Including the strength of the vector field in
Figure 0007072133000184
Is the mass of the vehicle,
Figure 0007072133000185
Is the vertical displacement of the vehicle,
Figure 0007072133000186
Is the vertical speed of the vehicle,
Figure 0007072133000187
Is the vertical acceleration of the vehicle,
Figure 0007072133000188
Is the lateral displacement of the vehicle,
Figure 0007072133000189
But
Figure 0007072133000190
Represents the first derivative, the lateral speed of the vehicle,
Figure 0007072133000191
Is the gravitational acceleration,
Figure 0007072133000192
Is the rolling resistance coefficient,
Figure 0007072133000193
Is a gradient,
Figure 0007072133000194
Is the drag coefficient of the vehicle,
Figure 0007072133000195
Is the windward area of the vehicle,
Figure 0007072133000196
Is the rotational mass conversion coefficient of the vehicle,
Figure 0007072133000197
Is in the lane
Figure 0007072133000198
Or a type of road boundary whose size is determined by traffic regulation (eg, a solid white line is greater than the value corresponding to the dashed white line).
Figure 0007072133000199
Is in the lane
Figure 0007072133000200
Or, it represents the road influence factor of the road boundary,
Figure 0007072133000201
Represents the width of the lane,
Figure 0007072133000202
Is in the lane
Figure 0007072133000203
Or the center of gravity of the vehicle from the road boundary
Figure 0007072133000204
Represents the distance vector to
Figure 0007072133000205
Lane
Figure 0007072133000206
Or the center of gravity of the vehicle shown by the white rectangular block in FIG. 4 from the road boundary.
Figure 0007072133000207
Represents the distance vector to
Figure 0007072133000208
Range is
Figure 0007072133000209
And
Figure 0007072133000210
Is the adjustment factor,
Figure 0007072133000211
Represents the equivalent mass of the vehicle,
Figure 0007072133000212
Represents the road impact factor where the self-propelled vehicle is located.
Figure 0007072133000213
Represents the driver influence factor.

三、Ｓ１３ステップでの「交通環境」は、車追従モードにおける単一の車両が直線道路
を走行することであり、図５に示すように、車の流れの方向に沿って重力に類似する一定
の力場

Figure 0007072133000214
、及び転がり抵抗、坂道抵抗、加速抵抗及び空気抵抗に起因する抵抗場が存在すると仮定
すると、
走行安全フィールド理論に従って、移動物体によって生成されるフィールド強さは次の
とおりであり、
Figure 0007072133000215
式中、勾配
Figure 0007072133000216
が次のとおりであり、
Figure 0007072133000217
Third, the "traffic environment" in step S13 is that a single vehicle in the vehicle following mode travels on a straight road, and as shown in FIG. 5, it is constant along the direction of the vehicle flow and resembles gravity. Force field
Figure 0007072133000214
, And assuming there is a resistance field due to rolling resistance, slope resistance, acceleration resistance and air resistance,
According to the driving safety field theory, the field strength produced by a moving object is:
Figure 0007072133000215
Gradient in the formula
Figure 0007072133000216
Is as follows,
Figure 0007072133000217

したがって、車追従プロセスでは、図５に示すように、

Figure 0007072133000218
の番目車両に対して物体jによって生成されたポテンシャルエネルギーを取得することが
でき、該ポテンシャルエネルギーの式が次のとおりであり、物体ｊが
Figure 0007072133000219
番目の車両以外の道路利用者又は障害物であり、
Figure 0007072133000220
番目の車両が自走式車両であってもよいし、他の車両、即ち現在の研究されているオブジ
ェクト車両であってもよく、
Figure 0007072133000221
したがって、前記ラグランジュ量は次のように表され、
Figure 0007072133000222
Figure 0007072133000223
Figure 0007072133000224
Figure 0007072133000225
Figure 0007072133000226
Therefore, in the vehicle following process, as shown in FIG.
Figure 0007072133000218
The potential energy generated by the object j can be obtained for the third vehicle, the formula of the potential energy is as follows, and the object j is
Figure 0007072133000219
A road user or obstacle other than the second vehicle,
Figure 0007072133000220
The second vehicle may be a self-propelled vehicle or another vehicle, i.e. the object vehicle currently being studied.
Figure 0007072133000221
Therefore, the amount of Lagrange is expressed as follows.
Figure 0007072133000222
Figure 0007072133000223
Figure 0007072133000224
Figure 0007072133000225
Figure 0007072133000226

式中、

Figure 0007072133000227
が自走式車両の縦方向の運動エネルギー
Figure 0007072133000228
と横方向の運動エネルギー
Figure 0007072133000229
を含み、
Figure 0007072133000230
が抵抗場
Figure 0007072133000231
、一定の力場
Figure 0007072133000232
、
Figure 0007072133000233
に位置する車線
Figure 0007072133000234
又は道路境界で形成されたポテンシャルエネルギー場の
Figure 0007072133000235
でのベクトル場の強さ
Figure 0007072133000236
を含み、
Figure 0007072133000237
が
Figure 0007072133000238
番目車両に対して物体ｊによって生成されたポテンシャルエネルギーを表し、
Figure 0007072133000239
が車両の質量であり、
Figure 0007072133000240
が車両の縦方向の変位であり、
Figure 0007072133000241
が車両の縦方向の速度であり、
Figure 0007072133000242
が車両の縦方向の加速度であり、
Figure 0007072133000243
が車両の横方向の変位であり、
Figure 0007072133000244
が車両の横方向の速度であり、
Figure 0007072133000245
が重力加速度であり、
Figure 0007072133000246
が転がり抵抗係数であり、
Figure 0007072133000247
が勾配であり、
Figure 0007072133000248
が車両の風抵抗係数であり、
Figure 0007072133000249
が車両の風上面積であり、
Figure 0007072133000250
が車両の回転質量換算係数であり、
Figure 0007072133000251
が
Figure 0007072133000252
に位置する車線
Figure 0007072133000253
で形成されたポテンシャルエネルギー場の
Figure 0007072133000254
でのベクトル場の強さであり、
Figure 0007072133000255
が車線a又は道路境界のタイプを表し、その大きさが交通規制によって決定され（例えば
、白い実線が白い破線に対応する値よりも大きい）、
Figure 0007072133000256
が車線a又は道路境界の道路影響因子を表し、
Figure 0007072133000257
が車線の幅を表し、
Figure 0007072133000258
が車線
Figure 0007072133000259
又は道路境界から車両の重心
Figure 0007072133000260
までの距離ベクトルを表し、
Figure 0007072133000261
が車線
Figure 0007072133000262
と道路境界から図４における白い長方形ボックスで示される車両の重心
Figure 0007072133000263
までの距離ベクトルであり、
Figure 0007072133000264
の範囲が
Figure 0007072133000265
であり、
Figure 0007072133000266
が調整係数であり、
Figure 0007072133000267
が車両の等価質量を表し、
Figure 0007072133000268
が自走式車両が位置する場所の道路影響因子を表し、
Figure 0007072133000269
が運転者影響因子を表し、
Figure 0007072133000270
が
Figure 0007072133000271
番目の車両に対して物体ｊによって生成されたポテンシャルエネルギーを表し、
Figure 0007072133000272
が車線
Figure 0007072133000273
を表し、
Figure 0007072133000274
合計
Figure 0007072133000275
本の車線があることを表し、
Figure 0007072133000276
が
Figure 0007072133000277
つ個の道路利用者がいることを表し、
Figure 0007072133000278
が１個の調整係数である。 During the ceremony
Figure 0007072133000227
Is the vertical kinetic energy of a self-propelled vehicle
Figure 0007072133000228
And lateral kinetic energy
Figure 0007072133000229
Including
Figure 0007072133000230
Is a resistance field
Figure 0007072133000231
, Constant force field
Figure 0007072133000232
,
Figure 0007072133000233
Lane located in
Figure 0007072133000234
Or the potential energy field formed at the road boundary
Figure 0007072133000235
The strength of the vector field in
Figure 0007072133000236
Including
Figure 0007072133000237
But
Figure 0007072133000238
Represents the potential energy generated by the object j with respect to the second vehicle.
Figure 0007072133000239
Is the mass of the vehicle,
Figure 0007072133000240
Is the vertical displacement of the vehicle,
Figure 0007072133000241
Is the vertical speed of the vehicle,
Figure 0007072133000242
Is the vertical acceleration of the vehicle,
Figure 0007072133000243
Is the lateral displacement of the vehicle,
Figure 0007072133000244
Is the lateral speed of the vehicle,
Figure 0007072133000245
Is the gravitational acceleration,
Figure 0007072133000246
Is the rolling resistance coefficient,
Figure 0007072133000247
Is a gradient,
Figure 0007072133000248
Is the drag coefficient of the vehicle,
Figure 0007072133000249
Is the windward area of the vehicle,
Figure 0007072133000250
Is the rotational mass conversion coefficient of the vehicle,
Figure 0007072133000251
But
Figure 0007072133000252
Lane located in
Figure 0007072133000253
Of the potential energy field formed by
Figure 0007072133000254
Is the strength of the vector field in
Figure 0007072133000255
Represents the type of lane a or road boundary, the size of which is determined by traffic regulation (eg, the white solid line is greater than the value corresponding to the white dashed line).
Figure 0007072133000256
Represents the road influence factor of lane a or road boundary,
Figure 0007072133000257
Represents the width of the lane,
Figure 0007072133000258
Is in the lane
Figure 0007072133000259
Or the center of gravity of the vehicle from the road boundary
Figure 0007072133000260
Represents the distance vector to
Figure 0007072133000261
Is in the lane
Figure 0007072133000262
And the center of gravity of the vehicle shown by the white rectangular box in FIG. 4 from the road boundary.
Figure 0007072133000263
Is a distance vector to
Figure 0007072133000264
Range is
Figure 0007072133000265
And
Figure 0007072133000266
Is the adjustment factor,
Figure 0007072133000267
Represents the equivalent mass of the vehicle,
Figure 0007072133000268
Represents the road impact factor where the self-propelled vehicle is located.
Figure 0007072133000269
Represents the driver impact factor
Figure 0007072133000270
But
Figure 0007072133000271
Represents the potential energy generated by the object j for the second vehicle,
Figure 0007072133000272
Is in the lane
Figure 0007072133000273
Represents
Figure 0007072133000274
total
Figure 0007072133000275
Indicates that there is a book lane
Figure 0007072133000276
But
Figure 0007072133000277
Represents that there are one road user
Figure 0007072133000278
Is one adjustment coefficient.

一実施例では、運転者が車両を運転するプロセスにおいて、常に効率を最大化しながら
リスクを回避することを図り、即ち安全性を確保しながら効率を可能な限り向上させ、即
ち運転者の運転制御動作がアクション量の数式でシステムアクション量

Figure 0007072133000279
として説明されて極値をとることができ、アクション量の値を最小にし、

Figure 0007072133000280

即ち、任意の運転者が車両を運転する場合、その運転制御動作が他の求める速度で示され
てもよく、該速度が上記を解くことで取得されてもよい。 In one embodiment, in the process in which the driver drives the vehicle, the efficiency is always maximized to avoid the risk, that is, the efficiency is improved as much as possible while ensuring the safety, that is, the driver's operation control. The action is the formula of the action amount and the system action amount
Figure 0007072133000279
Can be explained as extremum, minimize the value of the amount of action,

Figure 0007072133000280

That is, when an arbitrary driver drives a vehicle, the driving control operation may be indicated by another desired speed, and the speed may be obtained by solving the above.

例えば、図３の単一の車両が自由に走行するシーンでは、システムのアクション量は次
のように記述されてもよく、

Figure 0007072133000281

Figure 0007072133000282
の極小値を求めるために、以上の説明に従って、汎関数
Figure 0007072133000283
が極値をとる時に、その変分が０であることを必ず満たし、したがって、
Figure 0007072133000284
以下を得ることでき、
Figure 0007072133000285
即ち、上記の方法を用いて、図３に示すＳ１３ステップでの「交通環境」が単一の車両
が直線道路を走行することである場合、現在の走行指導速度が以下のとおりであり、
Figure 0007072133000286
なお、上記各パラメータでは、
Figure 0007072133000287
番目の車両が車両プラットフォーム自体であり、したがって、ここで
Figure 0007072133000288
が既知であり、
Figure 0007072133000289
、
Figure 0007072133000290
がいずれも自走式車両のＣＡＮデータである。 For example, in the scene where the single vehicle of FIG. 3 runs freely, the amount of action of the system may be described as follows.
Figure 0007072133000281

Figure 0007072133000282
To find the local minimum of, a functional according to the above explanation.
Figure 0007072133000283
Always satisfy that the variation is 0 when it takes an extremum, and therefore
Figure 0007072133000284
You can get:
Figure 0007072133000285
That is, when the "traffic environment" in step S13 shown in FIG. 3 is that a single vehicle travels on a straight road using the above method, the current traveling guidance speed is as follows.
Figure 0007072133000286
In each of the above parameters,
Figure 0007072133000287
The second vehicle is the vehicle platform itself, and therefore here
Figure 0007072133000288
Is known,
Figure 0007072133000289
,
Figure 0007072133000290
Are all CAN data of self-propelled vehicles.

Figure 0007072133000291
が転がり抵抗係数であり、
Figure 0007072133000292
が勾配であり、
Figure 0007072133000293
が車両の風抵抗係数であり、
Figure 0007072133000294
が車両の風上面積であり、
Figure 0007072133000295
が車両の回転質量換算係数であり、技術マニュアル又は教科書の内容から取得されてもよ
い。
Figure 0007072133000291
Is the rolling resistance coefficient,
Figure 0007072133000292
Is a gradient,
Figure 0007072133000293
Is the drag coefficient of the vehicle,
Figure 0007072133000294
Is the windward area of the vehicle,
Figure 0007072133000295
Is the rotational mass conversion coefficient of the vehicle, which may be obtained from the contents of a technical manual or a textbook.

Figure 0007072133000296
がカメラによって識別されることで取得され、
Figure 0007072133000297
がマルチセンサーによってデータを融合することで取得される。
Figure 0007072133000298
が車両の等価質量を表し、走行安全フィールド理論に従って取得されてもよく、
Figure 0007072133000299
、
Figure 0007072133000300
が経験値をとることができる。
本発明は、最小アクション量原理に基づく運転者制御動作定量化装置をさらに提供し、
前記最小アクション量原理に基づく運転者制御動作定量化装置は、
車両に設置され、時間と同期した自走式車両情報及び交通環境情報を取得することに用
いられる情報収集装置と、
電子制御ユニットであって、前記電子制御ユニットに運転者制御メカニズム定量的記述
モジュールを予め設置し、前記運転者制御メカニズム定量的記述モジュールが運転者の運
転制御動作の交通リスクと通行効率を同時に反映するアクション量
Figure 0007072133000301
を含み、
Figure 0007072133000302
の数式が式（１）であり、
Figure 0007072133000303
（３）
式中、
Figure 0007072133000304
が前記予め設定された交通プロセスにおける自走式車両のアクション量を表し、
Figure 0007072133000305
が前記予め設定された交通プロセスの開始時点であり、
Figure 0007072133000306
が前記予め設定された交通プロセスの終了時点であり、
Figure 0007072133000307
が予め設定された交通プロセスにおける自走式車両のラグランジュ量を表し、
Figure 0007072133000308
の式が次のとおりであり、
Figure 0007072133000309
式中、
Figure 0007072133000310
が自走式車両の運動エネルギーを表し、
Figure 0007072133000311
が自走式車両が受けるポテンシャルエネルギーを表し、該ポテンシャルエネルギーが予め
設定された、車の流れの方向に沿って存在する一定の力場と抵抗場によって決定される電
子制御ユニットとを備え、
前記電子制御ユニットが時間と同期した前記自走式車両情報及び交通環境情報に基づき
、
Figure 0007072133000312
で現在の走行指導速度を取得し、アクション量
Figure 0007072133000313
の値を最小にする。
Figure 0007072133000296
Is obtained by being identified by the camera,
Figure 0007072133000297
Is acquired by fusing the data with a multi-sensor.
Figure 0007072133000298
Represents the equivalent mass of the vehicle and may be obtained according to the driving safety field theory.
Figure 0007072133000299
,
Figure 0007072133000300
Can take experience points.
The present invention further provides a driver control motion quantifier based on the principle of minimum action quantity.
The driver control motion quantifier based on the minimum action quantity principle is
An information collection device installed in a vehicle and used to acquire self-propelled vehicle information and traffic environment information synchronized with time.
In the electronic control unit, a driver control mechanism quantitative description module is installed in advance in the electronic control unit, and the driver control mechanism quantitative description module simultaneously reflects the traffic risk and traffic efficiency of the driver's operation control operation. Amount of action to do
Figure 0007072133000301
Including
Figure 0007072133000302
The formula of is formula (1),
Figure 0007072133000303
(3)
During the ceremony
Figure 0007072133000304
Represents the amount of action of the self-propelled vehicle in the preset traffic process.
Figure 0007072133000305
Is the start time of the preset transportation process,
Figure 0007072133000306
Is the end of the preset transportation process,
Figure 0007072133000307
Represents the amount of self-propelled vehicle lagrange in a preset traffic process.
Figure 0007072133000308
The formula is as follows:
Figure 0007072133000309
During the ceremony
Figure 0007072133000310
Represents the kinetic energy of a self-propelled vehicle
Figure 0007072133000311
Represents the potential energy received by a self-propelled vehicle, which is equipped with a preset constant force field existing along the direction of the vehicle flow and an electronically controlled unit determined by a resistance field.
Based on the self-propelled vehicle information and traffic environment information that the electronic control unit synchronizes with the time.
Figure 0007072133000312
Get the current driving guidance speed with, and the amount of action
Figure 0007072133000313
Minimize the value of.

本発明は、インテリジェント車両をさらに提供し、前記インテリジェント車両は、上記
実施例に記載の最小アクション量原理に基づく運転者制御動作定量化装置を備える。 The present invention further provides an intelligent vehicle, wherein the intelligent vehicle comprises a driver control motion quantification device based on the minimum action quantity principle described in the above embodiment.

最後に説明すべきものとして、以上の実施例は本発明の技術的解決手段を説明するため
のものだけであるがそれを制限しない。当業者は、上記の各実施例に記載の技術的解決手
段を変更し、又はそのうちの一部の技術的特徴に対して同等入れ替えを行うことができ、
これらの変更又は入れ替えが対応する技術的解決策の本質を本発明の各実施例の技術的解
決手段の精神及び範囲から逸脱させないことを理解すべきである。 Last but not least, the above embodiments are for the purpose of explaining the technical means of the present invention, but are not limited thereto. Those skilled in the art may modify the technical solutions described in each of the above embodiments, or make equivalent replacements for some of the technical features thereof.
It should be understood that these changes or replacements do not deviate from the spirit and scope of the technical solutions of each embodiment of the invention to the essence of the corresponding technical solution.

Claims (6)

最小アクション量原理に基づく運転者制御動作定量化方法であって、
自走式車両の電子制御ユニットに運転者制御メカニズム定量的記述モジュールを予め設
置し、前記運転者制御メカニズム定量的記述モジュールが運転者の運転制御動作の交通リ
スクと通行効率を同時に反映するアクション量
Figure 0007072133000314
を含み、
Figure 0007072133000315
の数式が式（１）であり、
Figure 0007072133000316
式中、
Figure 0007072133000317
が予め設定された交通プロセスにおける自走式車両のアクション量を表し、
Figure 0007072133000318
が前記予め設定された交通プロセスの開始時点であり、
Figure 0007072133000319
が前記予め設定された交通プロセスの終了時点であり、
Figure 0007072133000320
が予め設定された交通プロセスにおける自走式車両のラグランジュ量を表し、
Figure 0007072133000321
の式が次のとおりであり、
Figure 0007072133000322
式中、
Figure 0007072133000323
が自走式車両の運動エネルギーを表し、
Figure 0007072133000324
が自走式車両が受けるポテンシャルエネルギーを表し、該ポテンシャルエネルギーが予め
設定された、車の流れの方向に沿って存在する一定の力場と抵抗場によって決定されるＳ
１ステップと、
自走式車両の情報収集装置により、時間と同期した自走式車両情報及び交通環境情報を
取得するＳ２ステップと、
時間と同期した前記自走式車両情報及び交通環境情報に基づき、運転者制御メカニズム
定量的記述モジュールにおける
Figure 0007072133000325
で現在の走行指導速度を取得し、前記アクション量
Figure 0007072133000326
の値を最小にするＳ３ステップとを含むことを特徴とする最小アクション量原理に基づく
運転者制御動作定量化方法。 It is a driver control motion quantification method based on the principle of minimum action quantity.
A driver control mechanism quantitative description module is installed in advance in the electronic control unit of a self-propelled vehicle, and the driver control mechanism quantitative description module simultaneously reflects the traffic risk and traffic efficiency of the driver's driving control operation.
Figure 0007072133000314
Including
Figure 0007072133000315
The formula of is formula (1),
Figure 0007072133000316
During the ceremony
Figure 0007072133000317
Represents the amount of action of a self-propelled vehicle in a preset traffic process.
Figure 0007072133000318
Is the start time of the preset transportation process,
Figure 0007072133000319
Is the end of the preset transportation process,
Figure 0007072133000320
Represents the amount of self-propelled vehicle lagrange in a preset traffic process.
Figure 0007072133000321
The formula is as follows:
Figure 0007072133000322
During the ceremony
Figure 0007072133000323
Represents the kinetic energy of a self-propelled vehicle
Figure 0007072133000324
Represents the potential energy received by a self-propelled vehicle, and the potential energy is determined by a predetermined force field and resistance field existing along the direction of the vehicle flow.
1 step and
S2 step to acquire self-propelled vehicle information and traffic environment information synchronized with time by the self-propelled vehicle information collection device,
In the driver control mechanism quantitative description module based on the self-propelled vehicle information and traffic environment information synchronized with time.
Figure 0007072133000325
Obtain the current driving guidance speed with, and the above-mentioned action amount
Figure 0007072133000326
A driver control operation quantification method based on the minimum action amount principle, which comprises an S3 step that minimizes the value of. 前記アクション量
Figure 0007072133000327
の取得方法は、
テスト車両に交通環境情報収集装置を取り付けることで、車両プラットフォームを構築
するＳ１１ステップと、
異なる運転者が前記車両プラットフォームを運転して異なる環境において自由走行テス
トを行うことで、時間と同期した自走式車両及び環境に関連するテストデータを収集する
Ｓ１２ステップと、
前記テストデータに基づき、任意の交通環境における前記アクション量
Figure 0007072133000328
の数式を取得するＳ１３ステップとを含むことを特徴とする
請求項１に記載の最小アクション量原理に基づく運転者制御動作定量化方法。 The amount of action
Figure 0007072133000327
How to get
S11 step to build a vehicle platform by attaching a traffic environment information collection device to the test vehicle,
S12 step to collect time-synchronized self-propelled vehicle and environment-related test data by different drivers driving the vehicle platform and performing free driving tests in different environments.
Based on the test data, the amount of action in any traffic environment
Figure 0007072133000328
The driver control operation quantification method based on the minimum action amount principle according to claim 1, which includes the S13 step for acquiring the mathematical expression of. Ｓ１３ステップでの「交通環境」は、単一の車両が直線道路を走行することであり、車
の流れの方向に沿って一定の力場及び転がり抵抗、坂道抵抗、加速抵抗及び空気抵抗に起
因する抵抗場が存在することを予め設定し、前記ラグランジュ量
Figure 0007072133000329
が次のように表され、
Figure 0007072133000330
Figure 0007072133000331
Figure 0007072133000332

式中、
Figure 0007072133000333
が車両
Figure 0007072133000334
の質量であり、
Figure 0007072133000335
が車両
Figure 0007072133000336
の縦方向の変位であり、
Figure 0007072133000337
が車両
Figure 0007072133000338
の縦方向の速度であり、
Figure 0007072133000339
が車両
Figure 0007072133000340
の縦方向の加速度であり、
Figure 0007072133000341
が重力加速度であり、
Figure 0007072133000342
が転がり抵抗係数であり、
Figure 0007072133000343
が勾配であり、
Figure 0007072133000344
が車両の風抵抗係数であり、
Figure 0007072133000345
が車両の風上面積であり、
Figure 0007072133000346
が車両回転質量換算係数であることを特徴とする
請求項２に記載の最小アクション量原理に基づく運転者制御動作定量化方法。 The "traffic environment" in step S13 is that a single vehicle travels on a straight road, resulting in a constant force field and rolling resistance, slope resistance, acceleration resistance and air resistance along the direction of the vehicle flow. It is set in advance that there is a resistance field to be used, and the amount of Lagrange
Figure 0007072133000329
Is expressed as
Figure 0007072133000330
Figure 0007072133000331
Figure 0007072133000332

During the ceremony
Figure 0007072133000333
Is a vehicle
Figure 0007072133000334
Is the mass of
Figure 0007072133000335
Is a vehicle
Figure 0007072133000336
Is the vertical displacement of
Figure 0007072133000337
Is a vehicle
Figure 0007072133000338
Is the vertical velocity of
Figure 0007072133000339
Is a vehicle
Figure 0007072133000340
Is the vertical acceleration of
Figure 0007072133000341
Is the gravitational acceleration,
Figure 0007072133000342
Is the rolling resistance coefficient,
Figure 0007072133000343
Is a gradient,
Figure 0007072133000344
Is the drag coefficient of the vehicle,
Figure 0007072133000345
Is the windward area of the vehicle,
Figure 0007072133000346
Is a driver control operation quantification method based on the minimum action amount principle according to claim 2, wherein is a vehicle rotation mass conversion coefficient. Ｓ１３ステップでの「交通環境」は、単一の車両が車線又は道路境界のある直線道路を
走行することであり、車の流れの方向に沿って一定の力場及び転がり抵抗、坂道抵抗、加
速抵抗及び空気抵抗に起因する抵抗場が存在することを予め設定し、前記ラグランジュ量
Figure 0007072133000347
が次のように表され、
Figure 0007072133000348
Figure 0007072133000349
Figure 0007072133000350
Figure 0007072133000351
Figure 0007072133000352
式中、
Figure 0007072133000353
が車両
Figure 0007072133000354
の質量であり、
Figure 0007072133000355
が車両
Figure 0007072133000356
の縦方向の変位であり、
Figure 0007072133000357
が車両
Figure 0007072133000358
の縦方向の速度であり、
Figure 0007072133000359
が車両
Figure 0007072133000360
の縦方向の加速度であり、
Figure 0007072133000361
が車両
Figure 0007072133000362
の横方向の変位であり、
Figure 0007072133000363
が
Figure 0007072133000364
の一次導関数を表し、車両
Figure 0007072133000365
の横方向の速度であり、
Figure 0007072133000366
が重力加速度であり、
Figure 0007072133000367
が転がり抵抗係数であり、
Figure 0007072133000368
が勾配であり、
Figure 0007072133000369
が車両
Figure 0007072133000370
の風抵抗係数であり、
Figure 0007072133000371
が車両
Figure 0007072133000372
の風上面積であり、
Figure 0007072133000373
が車両
Figure 0007072133000374
の回転質量換算係数であり、
Figure 0007072133000375
が
Figure 0007072133000376
に位置する車線
Figure 0007072133000377
又は道路境界によって形成されたポテンシャルエネルギー場の
Figure 0007072133000378
でのベクトル場の強さであり、
Figure 0007072133000379
が車線
Figure 0007072133000380
又は道路境界のタイプを表し、
Figure 0007072133000381
が車線
Figure 0007072133000382
又は道路境界の道路影響因子を表し、
Figure 0007072133000383
が車線の幅を表し、
Figure 0007072133000384
が車線
Figure 0007072133000385
又は道路境界から車両の重心
Figure 0007072133000386
までの距離ベクトルを表し、
Figure 0007072133000387
が調整係数であり、
Figure 0007072133000388
が車両
Figure 0007072133000389
の等価質量を表し、
Figure 0007072133000390
が自走式車両が位置する場所の道路影響因子を表し、
Figure 0007072133000391
が運転者影響因子を表すことを特徴とする
請求項２に記載の最小アクション量原理に基づく運転者制御動作定量化方法。 The "traffic environment" in step S13 is that a single vehicle travels on a straight road with lanes or road boundaries, and has a constant force field and rolling resistance, slope resistance, and acceleration along the direction of the vehicle flow. It is set in advance that there is a resistance field due to resistance and air resistance, and the amount of lagrange is said.
Figure 0007072133000347
Is expressed as
Figure 0007072133000348
Figure 0007072133000349
Figure 0007072133000350
Figure 0007072133000351
Figure 0007072133000352
During the ceremony
Figure 0007072133000353
Is a vehicle
Figure 0007072133000354
Is the mass of
Figure 0007072133000355
Is a vehicle
Figure 0007072133000356
Is the vertical displacement of
Figure 0007072133000357
Is a vehicle
Figure 0007072133000358
Is the vertical velocity of
Figure 0007072133000359
Is a vehicle
Figure 0007072133000360
Is the vertical acceleration of
Figure 0007072133000361
Is a vehicle
Figure 0007072133000362
Is the lateral displacement of
Figure 0007072133000363
But
Figure 0007072133000364
Represents the first derivative of the vehicle
Figure 0007072133000365
Is the lateral speed of
Figure 0007072133000366
Is the gravitational acceleration,
Figure 0007072133000367
Is the rolling resistance coefficient,
Figure 0007072133000368
Is a gradient,
Figure 0007072133000369
Is a vehicle
Figure 0007072133000370
Is the drag coefficient of
Figure 0007072133000371
Is a vehicle
Figure 0007072133000372
Is the windward area of
Figure 0007072133000373
Is a vehicle
Figure 0007072133000374
It is a rotation mass conversion coefficient of
Figure 0007072133000375
But
Figure 0007072133000376
Lane located in
Figure 0007072133000377
Or the potential energy field formed by the road boundary
Figure 0007072133000378
Is the strength of the vector field in
Figure 0007072133000379
Is in the lane
Figure 0007072133000380
Or represents the type of road boundary,
Figure 0007072133000381
Is in the lane
Figure 0007072133000382
Or, it represents the road influence factor of the road boundary,
Figure 0007072133000383
Represents the width of the lane,
Figure 0007072133000384
Is in the lane
Figure 0007072133000385
Or the center of gravity of the vehicle from the road boundary
Figure 0007072133000386
Represents the distance vector to
Figure 0007072133000387
Is the adjustment factor,
Figure 0007072133000388
Is a vehicle
Figure 0007072133000389
Represents the equivalent mass of
Figure 0007072133000390
Represents the road impact factor where the self-propelled vehicle is located.
Figure 0007072133000391
Is a driver control motion quantification method based on the minimum action quantity principle according to claim 2, wherein the factor represents a driver influence factor. Ｓ１３ステップでの「交通環境」は、車追従モードにおける単一の車両が直線道路を走
行することであり、車の流れの方向に沿って重力に類似する一定の力場
Figure 0007072133000392
、及び転がり抵抗、坂道抵抗、加速抵抗及び空気抵抗に起因する抵抗場が存在すると仮定
すると、前記ラグランジュ量
Figure 0007072133000393
が次のように表され、
Figure 0007072133000394
Figure 0007072133000395
Figure 0007072133000396
Figure 0007072133000397
Figure 0007072133000398
であり、
式中、
Figure 0007072133000399
が車両
Figure 0007072133000400
の質量であり、
Figure 0007072133000401
が車両
Figure 0007072133000402
の縦方向の変位であり、
Figure 0007072133000403
が車両
Figure 0007072133000404
の縦方向の速度であり、
Figure 0007072133000405
が車両
Figure 0007072133000406
の縦方向の加速度であり、
Figure 0007072133000407
が車両
Figure 0007072133000408
の横方向の変位であり、
Figure 0007072133000409
が車両
Figure 0007072133000410
の横方向の速度であり、
Figure 0007072133000411
が重力加速度であり、
Figure 0007072133000412
が転がり抵抗係数であり、
Figure 0007072133000413
が勾配であり、
Figure 0007072133000414
が車両
Figure 0007072133000415
の風抵抗係数であり、
Figure 0007072133000416
が車両
Figure 0007072133000417
の風上面積であり、
Figure 0007072133000418
が車両
Figure 0007072133000419
の回転質量換算係数であり、
Figure 0007072133000420
が
Figure 0007072133000421
に位置する車線
Figure 0007072133000422
又は道路境界によって形成されたポテンシャルエネルギー場の
Figure 0007072133000423
でのベクトル場の強さであり、
Figure 0007072133000424
が車線
Figure 0007072133000425
又は道路境界のタイプを表し、
Figure 0007072133000426
が車線又は道路境界の道路影響因子を表し、
Figure 0007072133000427
が車線の幅を表し、
Figure 0007072133000428
が車線
Figure 0007072133000429
又は道路境界から車両
Figure 0007072133000430
の重心
Figure 0007072133000431
までの距離ベクトルを表し、
Figure 0007072133000432
が調整係数であり、
Figure 0007072133000433
が車両
Figure 0007072133000434
の等価質量を表し、
Figure 0007072133000435
が自走式車両が位置する場所の道路影響因子を表し、
Figure 0007072133000436
が運転者影響因子を表し、
Figure 0007072133000437
が
Figure 0007072133000438
番目の車両に対して物体
Figure 0007072133000439
によって生成されたポテンシャルエネルギーを表し 、
Figure 0007072133000440
が車線を表し、
Figure 0007072133000441
が合計
Figure 0007072133000442
本の車線があることを表し、
Figure 0007072133000443
が
Figure 0007072133000444
個の道路利用者がいることを表し、
Figure 0007072133000445
が１個の調整係数であることを特徴とする
請求項２に記載の最小アクション量原理に基づく運転者制御動作定量化方法。 The "traffic environment" in step S13 is that a single vehicle in vehicle-following mode travels on a straight road, a constant force field that resembles gravity along the direction of vehicle flow.
Figure 0007072133000392
And, assuming that there is a resistance field due to rolling resistance, slope resistance, acceleration resistance and air resistance, the amount of Lagrange
Figure 0007072133000393
Is expressed as
Figure 0007072133000394
Figure 0007072133000395
Figure 0007072133000396
Figure 0007072133000397
Figure 0007072133000398
And
During the ceremony
Figure 0007072133000399
Is a vehicle
Figure 0007072133000400
Is the mass of
Figure 0007072133000401
Is a vehicle
Figure 0007072133000402
Is the vertical displacement of
Figure 0007072133000403
Is a vehicle
Figure 0007072133000404
Is the vertical velocity of
Figure 0007072133000405
Is a vehicle
Figure 0007072133000406
Is the vertical acceleration of
Figure 0007072133000407
Is a vehicle
Figure 0007072133000408
Is the lateral displacement of
Figure 0007072133000409
Is a vehicle
Figure 0007072133000410
Is the lateral speed of
Figure 0007072133000411
Is the gravitational acceleration,
Figure 0007072133000412
Is the rolling resistance coefficient,
Figure 0007072133000413
Is a gradient,
Figure 0007072133000414
Is a vehicle
Figure 0007072133000415
Is the drag coefficient of
Figure 0007072133000416
Is a vehicle
Figure 0007072133000417
Is the windward area of
Figure 0007072133000418
Is a vehicle
Figure 0007072133000419
It is a rotation mass conversion coefficient of
Figure 0007072133000420
But
Figure 0007072133000421
Lane located in
Figure 0007072133000422
Or the potential energy field formed by the road boundary
Figure 0007072133000423
Is the strength of the vector field in
Figure 0007072133000424
Is in the lane
Figure 0007072133000425
Or represents the type of road boundary,
Figure 0007072133000426
Represents a road impact factor for a lane or road boundary,
Figure 0007072133000427
Represents the width of the lane,
Figure 0007072133000428
Is in the lane
Figure 0007072133000429
Or a vehicle from the road boundary
Figure 0007072133000430
Center of gravity
Figure 0007072133000431
Represents the distance vector to
Figure 0007072133000432
Is the adjustment factor,
Figure 0007072133000433
Is a vehicle
Figure 0007072133000434
Represents the equivalent mass of
Figure 0007072133000435
Represents the road impact factor where the self-propelled vehicle is located.
Figure 0007072133000436
Represents the driver impact factor
Figure 0007072133000437
But
Figure 0007072133000438
Object to the second vehicle
Figure 0007072133000439
Represents the potential energy generated by
Figure 0007072133000440
Represents a lane,
Figure 0007072133000441
Is the total
Figure 0007072133000442
Indicates that there is a book lane
Figure 0007072133000443
But
Figure 0007072133000444
Represents that there are individual road users
Figure 0007072133000445
The driver control operation quantification method based on the minimum action amount principle according to claim 2, wherein is a single adjustment coefficient. 最小アクション量原理に基づく運転者制御動作定量化装置であって、
自走式車両に設置され、時間と同期した自走式車両情報及び交通環境情報を取得するこ
とに用いられる情報収集装置と、
電子制御ユニットに運転者制御メカニズム定量的記述モジュールを予め設置し、前記運
転者制御メカニズム定量的記述モジュールが運転者の運転制御動作の交通リスクと通行効
率を同時に反映するアクション量
Figure 0007072133000446
を含み、
Figure 0007072133000447
の数式が式（１）であり、
Figure 0007072133000448
（１）
式中、
Figure 0007072133000449
が予め設定された交通プロセスにおける自走式車両のアクション量を表し、
Figure 0007072133000450
が前記予め設定された交通プロセスの開始時点であり、
Figure 0007072133000451
が前記予め設定された交通プロセスの終了時点であり、
Figure 0007072133000452
が予め設定されを予め設定した交通プロセスにおける自走式車両のラグランジュ量を表し
、
Figure 0007072133000453
の式が次のとおりであり、
Figure 0007072133000454
であり、
式中、
Figure 0007072133000455
が自走式車両の運動エネルギーを表し、
Figure 0007072133000456
が、自走式車両が受けるポテンシャルエネルギーを表し、該ポテンシャルエネルギーを予
め設定した、車の流れの方向に沿って存在する一定の力場と抵抗場によって決定される電
子制御ユニットとを備え、
前記電子制御ユニットは、時間と同期した自走行情報及び交通環境情報に基づき、
Figure 0007072133000457
で現在の走行指導速度を取得し、アクション量
Figure 0007072133000458
の値を最小にすることに用いられることを特徴とする
最小アクション量原理に基づく運転者制御動作定量化方法装置。 A driver-controlled motion quantifier based on the principle of minimum action quantity,
An information collection device installed in a self-propelled vehicle and used to acquire time-synchronized self-propelled vehicle information and traffic environment information.
A driver control mechanism quantitative description module is installed in advance in the electronic control unit, and the driver control mechanism quantitative description module simultaneously reflects the traffic risk and traffic efficiency of the driver's driving control operation.
Figure 0007072133000446
Including
Figure 0007072133000447
The formula of is formula (1),
Figure 0007072133000448
(1)
During the ceremony
Figure 0007072133000449
Represents the amount of action of a self-propelled vehicle in a preset traffic process.
Figure 0007072133000450
Is the start time of the preset transportation process,
Figure 0007072133000451
Is the end of the preset transportation process,
Figure 0007072133000452
Represents the amount of self-propelled vehicle lagrange in a preset traffic process.
Figure 0007072133000453
The formula is as follows:
Figure 0007072133000454
And
During the ceremony
Figure 0007072133000455
Represents the kinetic energy of a self-propelled vehicle
Figure 0007072133000456
However, it is equipped with an electronic control unit that represents the potential energy received by a self-propelled vehicle and is determined by a constant force field and resistance field existing along the direction of the vehicle flow, in which the potential energy is preset.
The electronic control unit is based on self- driving information and traffic environment information synchronized with time.
Figure 0007072133000457
Get the current driving guidance speed with, and the amount of action
Figure 0007072133000458
A driver-controlled motion quantification method device based on the minimum action quantity principle, which is characterized by being used to minimize the value of.
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