EP3826895A1 - Procédé d'identification de conducteur reposant sur une modélisation de suivi de voiture - Google Patents

Procédé d'identification de conducteur reposant sur une modélisation de suivi de voiture

Info

Publication number
EP3826895A1
EP3826895A1 EP18924856.0A EP18924856A EP3826895A1 EP 3826895 A1 EP3826895 A1 EP 3826895A1 EP 18924856 A EP18924856 A EP 18924856A EP 3826895 A1 EP3826895 A1 EP 3826895A1
Authority
EP
European Patent Office
Prior art keywords
driver
car
initialization mode
processor
sequence
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Withdrawn
Application number
EP18924856.0A
Other languages
German (de)
English (en)
Other versions
EP3826895A4 (fr
Inventor
Mathieu MOZE
Francois Aioun
Franck Guillemard
Donghao XU
Chenfeng TU
Huijing ZHAO
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Peking University
Stellantis Auto SAS
Original Assignee
Peking University
PSA Automobiles SA
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Peking University, PSA Automobiles SA filed Critical Peking University
Publication of EP3826895A1 publication Critical patent/EP3826895A1/fr
Publication of EP3826895A4 publication Critical patent/EP3826895A4/fr
Withdrawn legal-status Critical Current

Links

Classifications

    • 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
    • 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
    • B60W2040/0809—Driver authorisation; Driver identity check
    • 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
    • B60W2050/0001—Details of the control system
    • B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
    • 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
    • B60W2050/0001—Details of the control system
    • B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
    • B60W2050/005—Sampling
    • 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
    • B60W2050/0001—Details of the control system
    • B60W2050/0043—Signal treatments, identification of variables or parameters, parameter estimation or state estimation
    • B60W2050/0052—Filtering, filters
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • 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
    • B60W2520/00—Input parameters relating to overall vehicle dynamics
    • B60W2520/10—Longitudinal speed
    • B60W2520/105—Longitudinal acceleration
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • B60W2554/802—Longitudinal distance
    • 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
    • B60W2554/00—Input parameters relating to objects
    • B60W2554/80—Spatial relation or speed relative to objects
    • B60W2554/804—Relative longitudinal speed
    • 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

Definitions

  • the present invention is directed to the domain of automobile vehicles, and more particularly, to a method for driver identification based on car following modeling.
  • the driving style of each driver is different. Heterogeneities exist in the way a driver operates on steering wheel, gas and brake pedals etc. in performing certain behaviors, which turns out different driving styles correlating with road and scene vehicles. It is possible to treat such heterogeneities as a kind of signature thus leading to identification of the driver.
  • car following is an essential component of a driver’s behavior, where heterogeneity has been studied as an important facet that is a consequence of human factors in this driving process.
  • the level of heterogeneity in the car-following behaviors of different drivers is substantial as well as of vehicle/driver combinations, which is called inter-driver heterogeneity.
  • the internal stochasticity of an individual driver, called intra-driver heterogeneity is another rational cause for the randomness of car-following behaviors.
  • Many car following models have been developed, where the process of a driver’s behavior is generally described as a transformation from some perceived information about the driving situation, such as the speed and distance of a leading vehicle relative the ego (i.e. the follower) vehicle, and the ego vehicle’s speed, to control actions for acceleration or deceleration.
  • the present invention realizes that driver identification could lead to multiple improvements regarding attractiveness and safety of cars through adaptive algorithms and HMI to the driver style (attractiveness) and driver monitoring by comparing actual and usual driving style (safety) .
  • driver identification could lead to multiple improvements regarding attractiveness and safety of cars through adaptive algorithms and HMI to the driver style (attractiveness) and driver monitoring by comparing actual and usual driving style (safety) .
  • this invention enables driver identification from stochastic distribution of his driving behavior.
  • a method for driver identification based on car following modeling comprising:
  • driver classes associated to drivers based on driver state parameters and driver trusted signature parameters in an initialization mode considering driving sequence
  • driver identification from measurements by computation of class belonging probability in the normal usage mode based on the driver classes defined in the initialization mode.
  • the present invention may further include any one or more of the following alternative forms.
  • the computation of class belonging probability further comprises a calculation of acceleration estimation associated to a class from the car-following sequence at a given instant.
  • the computation of class belonging probability further comprises a calculation of class belonging probabilities for all instants contained in the car-following sequence by comparing the measured and class estimated accelerations.
  • a signature is determined by compilation of instantaneous signatures for a sequence of measurements at different instants.
  • the driver identification is performed by comparing its Euclidean distances to the driven trusted signatures defined in the initialization mode based on a determined sequence signature.
  • the car-following sequence comprises information of ego vehicle’s acceleration and velocity, ego vehicle’s distance to the leading vehicle and relative velocity of the leading vehicle to the ego vehicle.
  • the set of parameters estimation in the initialization mode is obtained by minimizing the loss function with gradient descent.
  • the initialization mode is performed offline and no computational issue is involved.
  • a device for vehicle arranged and operable to carry out the above method is provided.
  • a processing means programmed and operable to execute instructions for carrying out the above method is provided.
  • the present invention enables driver identification of a vehicle when this vehicle is behind another vehicle and the driver regulates its speed and position according to its normal driving. It uses information from available sensors: ego vehicle acceleration and velocity, ego vehicle distance to the leader (vehicle in front) and relative velocity of the leader to the ego vehicle. That is, it does not need dedicated sensors to perform driver identification such as cameras.
  • the proposed invention delivers identification of a driver from multiple already known drivers (previously identified) and a level of confidence associated to the identification in the initialization mode, and thus a stochastic approach can be used based on probability for the driver to belong to a previously defined class. Each class is supposed to represent a unique driver.
  • Fig. 1 schematically illustrates notations used in an initialization mode according to the present invention
  • Fig. 2 schematically illustrates car following sequence input according to the invention
  • Fig. 3 schematically illustrates calculation of acceleration estimation from input sequence
  • Fig. 4 schematically illustrates calculation of class belonging probabilities for all instants contained in input sequence
  • Fig. 5 schematically illustrates driver identification from class belonging probabilities
  • Fig. 6 schematically illustrates signatures of three drivers from experimental dataset
  • Fig. 7 schematically illustrates states PCA for three drivers from experimental dataset
  • Fig. 8 schematically illustrates twelve sequences signatures and three driver’s signatures, and dissimilarity of the first sequence is computed for each driver signature.
  • the driving style of each driver is different. Heterogeneities exist in the way a driver operates on steering wheel, gas and brake pedals etc. in performing certain behaviors, which turns out different driving styles correlating with road and scene vehicles. It is possible to treat such heterogeneities as a kind of signature thus leading to identification of the driver.
  • the goal of this initialization mode is to find the optimal set of parameters ⁇ q * and C d * that discriminates the most between all the drivers and the less between sequences generated by the same driver.
  • the discriminating function used for this purpose is called loss function and is presented now.
  • X [y] denotes the y th coordinate of any vector
  • ⁇ T max denotes the upper limit of a driver’s response time (10s in practice)
  • ⁇ is a small positive real number to keep any ⁇ q from getting too close to 0 which would lead to numerical problems (0.0001 in practice) .
  • minimization is performed through a classical gradient descent method.
  • This mode supposes existence of Q predefined classes or states, each one corresponding to a particular state. These states are abstract concepts and cannot be related to any objective specificity of a driver.
  • the input time series is a car-following sequence (S) , which is composed of sequences of leading vehicle’s relative motion states to the ego vehicle and containing (h) and respectively relative distance and velocity of ego vehicle to leader, ego vehicle’s velocity (v) and ego vehicle’s acceleration (a) .
  • the sequence (S) is supposed to correspond to a car following phase, i.e. where the ego vehicle is behind a leader.
  • the proposed invention uses only available information for autonomous and assisted driving from usual dedicated sensors such as radars and GPS and more classical ones such as speed meter. This car following sequence input in a normal usage mode is depicted on Fig. 2.
  • the driver identification can be performed by the following steps.
  • Step 1 Computation of class belonging probability
  • Each class q is represented by parameters: ⁇ q , ⁇ q , ⁇ 1, q , ⁇ 2, q , ⁇ 3, q , ⁇ 4, q and ⁇ T q . Computation of these parameters is performed during initialization mode.
  • a parameter associated to class q is calculated using the relation:
  • x k [x 1, k , ..., x q, k , ..., x Q, k ] .
  • a signature X S is determined by compilation of instantaneous signatures such that
  • vectors x k and X S represent probabilities and that the sum of their components is equal to 1.
  • this invention can be used for analyzing and evidencing intra-driver heterogeneity during long-term driving.
  • Step 2 Driver identification from state belonging probabilities
  • D (a, b) denotes Euclidean distance between a and b.
  • the sequence is longer than 25 seconds.
  • PCA Principal Components Analysis
  • Fig. 7 presents such a representation into a 3 dimensional space for the drivers.
  • sample driver 2 is misclassified as driver 1 on a particular (and unique) sequence.
  • Fig. 8 presents the states distribution obtained on 4 different sequences of 3 different drivers.
  • the first line is the distribution density of each driver (signature) calculated from these associated 3 sequences.
  • the first graph (line 1, column 1) is used to compute its dissimilarity from each driver profile. As expected, the first distribution density has the lowest dissimilarity, which is correct and validates the method.

Landscapes

  • Engineering & Computer Science (AREA)
  • Physics & Mathematics (AREA)
  • Automation & Control Theory (AREA)
  • Mathematical Physics (AREA)
  • Transportation (AREA)
  • Mechanical Engineering (AREA)
  • Control Of Driving Devices And Active Controlling Of Vehicle (AREA)
  • Traffic Control Systems (AREA)

Abstract

L'invention concerne un procédé d'identification de conducteur reposant sur une modélisation de suivi de voiture. Le procédé consiste : à définir, au niveau d'un processeur, des classes de conducteurs associées à des conducteurs sur la base de paramètres d'état de conducteur et de paramètres de signature de confiance de conducteur dans un mode d'initialisation tenant compte d'une séquence de conduite ; à obtenir, au niveau du processeur, un ensemble d'estimation de paramètres de l'état de conducteur et de la signature de confiance de conducteur qui écarte beaucoup de conducteurs et peu de séquences générées par le même conducteur dans le mode d'initialisation ; à fournir, au niveau du processeur, une séquence de suivi de voiture composée de séquences d'états de mouvement relatif du véhicule de tête au véhicule ego dans un mode d'utilisation normale ; et à sélectionner, au niveau du processeur, d'une identification de conducteur à partir de mesures par le calcul d'une probabilité d'appartenance de classe dans le mode d'utilisation normal sur la base des classes de conducteurs définies dans le mode d'initialisation.
EP18924856.0A 2018-06-26 2018-06-26 Procédé d'identification de conducteur reposant sur une modélisation de suivi de voiture Withdrawn EP3826895A4 (fr)

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
PCT/CN2018/092903 WO2020000191A1 (fr) 2018-06-26 2018-06-26 Procédé d'identification de conducteur reposant sur une modélisation de suivi de voiture

Publications (2)

Publication Number Publication Date
EP3826895A1 true EP3826895A1 (fr) 2021-06-02
EP3826895A4 EP3826895A4 (fr) 2022-03-02

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Country Status (2)

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EP (1) EP3826895A4 (fr)
WO (1) WO2020000191A1 (fr)

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CN111968372B (zh) * 2020-08-25 2022-07-22 重庆大学 一种考虑主观因素的多车型混合交通跟驰行为仿真方法
CN112201069B (zh) * 2020-09-25 2021-10-29 厦门大学 基于深度强化学习的驾驶员纵向跟车行为模型构建方法
CN113111502B (zh) * 2021-04-01 2022-07-05 同济大学 基于跟驰模型与驾驶员特征的驾驶员感知距离建模方法
CN114248780B (zh) * 2021-12-27 2024-07-12 江苏大学 考虑驾驶员风格的idm-lstm组合型跟车模型建立方法
CN116432108B (zh) * 2023-06-13 2023-08-08 北京航空航天大学 一种跟驰场景下驾驶行为评价及驾驶风格在线识别方法

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JP5621921B2 (ja) * 2011-05-18 2014-11-12 日産自動車株式会社 運転不安定度判定装置
DE112012006465B4 (de) * 2012-06-05 2021-08-05 Toyota Jidosha Kabushiki Kaisha Fahreigenschaftenabschätzvorrichtung und Fahrassistenzsystem
EP2891589B1 (fr) 2014-01-06 2024-09-25 Harman International Industries, Incorporated Identification automatique d'un conducteur
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EP3826895A4 (fr) 2022-03-02
WO2020000191A1 (fr) 2020-01-02

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