WO2019213982A1 - 基于最小作用量原理的驾驶人操控行为量化方法及装置 - Google Patents

基于最小作用量原理的驾驶人操控行为量化方法及装置 Download PDF

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WO2019213982A1
WO2019213982A1 PCT/CN2018/086640 CN2018086640W WO2019213982A1 WO 2019213982 A1 WO2019213982 A1 WO 2019213982A1 CN 2018086640 W CN2018086640 W CN 2018086640W WO 2019213982 A1 WO2019213982 A1 WO 2019213982A1
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vehicle
driver
traffic
risk
resistance
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PCT/CN2018/086640
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English (en)
French (fr)
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王建强
郑讯佳
黄荷叶
李克强
许庆
李升波
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清华大学
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Priority to JP2020541837A priority Critical patent/JP7072133B2/ja
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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

Definitions

  • the invention relates to the technical field of smart car application, in particular to a method and a device for quantifying a driver's manipulation behavior based on the principle of minimum action.
  • Road traffic safety is related to the human-vehicle-environment closed-loop system.
  • the proportion of people is the largest, that is, most of the traffic accidents are caused by human factors.
  • the driver's handling of the vehicle has become a key factor in ensuring traffic safety.
  • the driver acceptance refers to automobile intelligence.
  • the system needs to comply with the driver's driving behavior.
  • the prior art can usually use statistical analysis methods to study the driver's driving behavior, and the research results are beneficial to optimize the vehicle intelligent system to coordinate its safety performance and driver acceptance.
  • Existing statistical analysis methods such as the use of probability statistics, fuzzy mathematics, rough set theory and other research methods or based on real vehicle experimental data statistical analysis of the driver behavior characteristics description method.
  • the existing research methods often require a large number of data samples, which brings great research. Difficulties.
  • the object of the present invention is to provide a method and a device for quantifying a driver's manipulation behavior based on the principle of minimum action, which can describe the driver in accordance with the collection of information in the vehicle and the traffic environment where the driver is located, using the principle of minimum action.
  • Driving control mechanism that avoids harm during driving.
  • the present invention provides a method and apparatus for quantifying a driver's manipulation behavior based on a principle of minimum action
  • the method and apparatus for quantifying a driver's manipulation behavior based on a principle of minimum action includes the following steps:
  • a driver control mechanism quantitative description module is preset in the electronic control unit of the vehicle, and the driver control mechanism quantitative description module includes a workload amount S Risk that simultaneously reflects the driving risk and the traffic efficiency of the driver's driving manipulation behavior.
  • the mathematical expression of S Risk is (1):
  • S Risk represents the amount of action of the self-vehicle in the preset traffic process
  • t 1 is the starting time of the preset traffic process
  • t 2 is the ending time of the preset traffic process
  • L represents the self-vehicle In the amount of Lagrangian in the preset traffic, the expression of L is:
  • T is the kinetic energy of the self-vehicle
  • V is the potential energy received by the vehicle, which is determined by a predetermined constant force field and resistance field existing along the direction of the traffic flow
  • S3 according to the time-synchronized self-vehicle and traffic environment information, quantitatively describe the S Risk in the module by the driver control mechanism, obtain the current driving instruction speed, and minimize the value of the action amount S Risk .
  • the method for obtaining the action amount S Risk includes:
  • the "traffic environment” in S13 is a single vehicle traveling on a straight road, and is preset: there is a constant force field along the traffic direction and there is resistance due to rolling resistance, climbing resistance, acceleration resistance, and air resistance. Field, then the Lagrangian quantity L is expressed as:
  • the "traffic environment" in S13 is a single vehicle traveling on a straight road having a lane line a or a road boundary, and is preset: there is a constant force field along the direction of the vehicle flow and there is a rolling resistance, a climbing resistance, The resistance field caused by the acceleration resistance and the air resistance, the L-language amount L is expressed as:
  • is expressed as the centroid (x i , y i
  • the "traffic environment" in S13 is a single vehicle traveling in a follow-up mode on a straight road, assuming that there is a gravity-like constant force field G along the traffic direction and there is a rolling resistance, a climbing resistance, The acceleration field and the resistance field caused by the air resistance, the L-language amount L is expressed as:
  • the invention also provides a method and a device for quantifying a driver's manipulation behavior based on the principle of minimum action, the driver's manipulation behavior quantification device comprising:
  • An information collecting device is disposed on the vehicle for acquiring self-vehicle and traffic environment information synchronized with time;
  • S Risk represents the amount of action of the self-vehicle in the preset traffic process
  • t 1 is the starting time of the preset traffic process
  • t 2 is the ending time of the preset traffic process
  • L represents the self-vehicle In the amount of Lagrangian in the preset traffic, the expression of L is:
  • T is the kinetic energy of the self-vehicle
  • V is the potential energy received by the vehicle, which is determined by a predetermined constant force field and resistance field existing along the direction of the traffic flow
  • the electronic control unit is configured to obtain the current driving instruction speed through S Risk according to the self-vehicle and traffic environment information synchronized with time, and minimize the value of the action amount S Risk .
  • the present invention also provides a smart car characterized by comprising a driver manipulation behavior quantifying device as described above.
  • the invention has the beneficial effects: the quantitative description method and the device for driving driving mechanism of the driver based on the principle of minimum action amount, and the multi-sensor sensing system composed of 64-line laser radar, millimeter wave radar and visual sensor to build a smart vehicle
  • the platform can identify the position information and state information of surrounding moving objects and stationary objects, collect a large amount of data, establish a database, identify the driving control mechanism from the driver by analyzing the characteristics of the dynamic traffic system during the driving process of the vehicle, and drive
  • the quantitative description of the human driving control mechanism can further quantitatively analyze the driving behavior of the driver.
  • Figure 1 is a side elevational view of the vehicle platform of the present invention
  • Figure 2 is a plan view of the vehicle platform shown in Figure 1;
  • FIG. 3 is a schematic diagram of a transportation system of a single free-riding vehicle provided by the present invention.
  • FIG. 4 is a schematic diagram showing a constraint potential energy model of a lane line to vehicle according to the present invention.
  • FIG. 5 is a schematic diagram of a following car scene provided by the present invention.
  • the method for quantifying the driver's manipulation behavior based on the minimum action principle mainly considers the self-vehicle affected by all factors of the external environment, and the method for quantifying the driver's manipulation behavior based on the principle of minimum action includes the following steps:
  • a driver control mechanism quantitative description module is preset in the electronic control unit of the vehicle, and the driver control mechanism quantitative description module includes a workload amount S Risk that simultaneously reflects the driving risk and the traffic efficiency of the driver's driving manipulation behavior.
  • the mathematical expression of S Risk is (1):
  • S Risk represents the amount of action of the self-vehicle in the preset traffic process, and can be expressed as the integral of the Lagrangian amount of time in the preset traffic, and t 1 is the preset traffic.
  • t 2 is the ending moment of the preset traffic process, and L is the Lagrangian amount of the self-vehicle in the preset traffic process, and the expression of L is:
  • T represents the kinetic energy of the self-vehicle and V represents the potential energy received by the vehicle, which is determined by a predetermined constant force field and resistance field existing along the direction of the traffic flow.
  • the information collecting device can detect and recognize the traffic environment information of the self-vehicle, the traffic environment information includes surrounding obstacles (vehicles, riders, pedestrians, fences, cones) and traffic information (traffic lights, Speed limit sign, lane line).
  • the self-vehicle information includes self-vehicle CAN data, specifically: engine speed, steering wheel angle, vehicle speed, gear position, acceleration and deceleration, and GPS information.
  • S3 according to the time-synchronized self-vehicle and traffic environment information, quantitatively describe the S Risk in the module by the driver control mechanism, obtain the current driving instruction speed, and minimize the value of the action amount S Risk .
  • the quantitative description module of the driver control mechanism calculates the preset traffic process.
  • the magnitude of the action amount S Risk generation by minimizing the value of the action amount S Risk , obtains the current speed optimal value of the smart car traveling, which is the current driving guidance speed. If the smart car is speed controlled by the current driving speed, it can achieve its own safety and efficiency.
  • the method for obtaining the amount of action S Risk includes:
  • S11 a radar and a visual sensor for obtaining target position information and motion information synchronized with time are mounted on the test vehicle. As shown in FIG. 1 and FIG. 2, S11 specifically includes:
  • a 64-line laser radar 1 and a 64-line laser radar 1 are installed at the top of the test vehicle for obtaining the raw and horizontal coordinate positions of the target, and the sensor raw data of the type.
  • the first millimeter wave radar 2a, the second millimeter wave radar 2b, the third millimeter wave radar 2c, the fourth millimeter wave radar 2d, and the first visual sensor are respectively installed in the front, rear, left, and right directions of the test vehicle.
  • 3a, the second visual sensor 3b, the third visual sensor 3c, and the fourth visual sensor 3d acquire the velocity, acceleration, and lateral position information of the target by each of the visual sensor and the millimeter wave radar.
  • the data collected by each sensor in S11 is the original data of the sensor, and the original data needs to be parsed into the target data in the subsequent steps, and then applied.
  • Raw data pictures and videos captured by the camera; point clouds scanned by the laser radar; millimeter wave signals received by the millimeter wave radar.
  • Target data After the original data of the above three sensors are combined, the speed and position data of the target such as a pedestrian, a rider, and a vehicle are obtained.
  • the method of "data fusion” is as follows:
  • Lidar uses feature extraction and point cloud clustering to detect targets and obtain accurate target position information.
  • Vision sensors perform machine learning-based target detection on road targets, providing target category information for lidar target detection; millimeter wave radar recognition dynamics Target and provide accurate target speed and position information.
  • target category information for lidar target detection
  • millimeter wave radar recognition dynamics Target and provide accurate target speed and position information.
  • the same target information detected by each sensor is matched; finally, accurate target position information, motion information, ie coordinates and speed, and acceleration are obtained.
  • the embodiment adopts a multi-sensor sensing system composed of a 64-line laser radar, a millimeter wave radar, and a visual sensor to construct a vehicle platform, which can identify position information and state information of surrounding moving objects and stationary objects.
  • the selection principle of "driver" in S12 includes:
  • the number of “drivers” is as large as possible, so that by collecting as many sets of test data as possible, and considering the driving habits of more drivers, the risk identification curves obtained in the subsequent steps S3 and S4 are more extensive and representative. It is conducive to improving the driver's acceptance of the risk identification of driving.
  • the “self-vehicle and environment-related test data” in S12 includes the test data of the self-vehicle and the number of tests of the environment, wherein
  • the test data of the self-vehicle includes time-synchronized target position information and motion information and self-vehicle CAN data collected by the radar and the visual sensor.
  • the vehicle CAN data includes: engine speed, steering wheel angle, vehicle speed, gear position, acceleration and deceleration, and GPS information.
  • the data collected by each of the radar and the visual sensor is data-fused to obtain accurate target position information, motion information, ie coordinates and speed, and acceleration.
  • the information obtained by the sensor is mainly the speed of other road users in the environment other than the car, the speed of the obstacle, and the relative position of the vehicle.
  • the “different environments” in S12 include:
  • the type of environment is: campus, park, city, high speed;
  • the second type is: uphill, downhill, bridge, under the bridge, tunnel, straight, curved;
  • Traffic participants, the first type is: motor vehicles, non-motor vehicles, fixed objects; in the second type, motor vehicles include: cars, buses, minivans, trucks, medium passenger cars, motorcycles, other vehicles; non-machine Motor vehicles include: pedestrians, cyclists, two-wheelers, and other non-motor vehicles; fixed objects include: cones, fences, etc.;
  • Traffic signs the first type is: traffic signs, traffic lights, lane lines; in the second type, traffic signs include: speed limit, height limit, weight limit, indication class, warning class, prohibition class, other signs; red Street lights include: round, arrow, pedestrian pattern, two-wheeler pattern;
  • Road signs the first type includes lane line and road marking; in the second type, the lane line includes: single solid line, double solid line and dotted line; pavement marking includes: straight arrow, right turn arrow, left turn arrow and other road markings ;
  • test data of the environment corresponds to various information listed in the above "different environments”.
  • the time-synchronized "self-vehicle and environment-related test data" in S12 is stored in a database manner.
  • the “traffic environment” in S13 is a single vehicle traveling on a straight road, as shown in Figure 3.
  • Pre-set there is a constant force field similar to gravity along the direction of traffic flow and there is resistance due to rolling resistance, climbing resistance,
  • the resistance field caused by the acceleration resistance and the air resistance, the L-language amount L is expressed as:
  • T includes the longitudinal kinetic energy of the vehicle V includes a resistance field R i and a constant force field G i ;
  • m i is the mass of the vehicle; x i is the longitudinal displacement of the vehicle; Representing the first derivative of x i , which is the longitudinal velocity of the vehicle; The second derivative of x i is the longitudinal acceleration of the vehicle; g is the acceleration of gravity; f is the rolling resistance coefficient; i ⁇ is the slope; C Di is the drag coefficient of the vehicle; A i is the windward area of the vehicle; ⁇ i is the vehicle The rotation mass conversion factor, ⁇ i is generally 1.05 according to the relevant content of the automobile theory.
  • the “traffic environment” in S13 is a single vehicle traveling on a straight road with lane line a or road boundary, as shown in Figure 4, preset: there is a constant force field along the direction of traffic flow and there is rolling resistance Resistance field caused by climbing resistance, acceleration resistance and air resistance.
  • T includes the longitudinal kinetic energy of the vehicle Lateral kinetic energy
  • V includes a resistance field R i , a constant force field G i , and a vector field strength E ai at (x i , y i ) of the potential energy field formed at the lane line a or the road boundary at (x a , y a );
  • denotes the distance vector pointing from the lane line a or the road boundary to the centroid (x i , y i ) of the vehicle, r ai (x i
  • the “traffic environment” in S13 is a single vehicle traveling in the following mode on a straight road. As shown in Fig. 5, it is assumed that there is a constant force field G similar to gravity along the direction of traffic flow and the presence of rolling resistance. Resistance field caused by climbing resistance, acceleration resistance and air resistance.
  • the field strength generated by moving objects is:
  • the potential energy generated by the object j for the ith car can be obtained as follows.
  • the object j is a road user or an obstacle other than the i-th car; the i-th vehicle
  • the car can be a self-vehicle or another vehicle, which is the target vehicle for the current study:
  • T includes the longitudinal kinetic energy of the vehicle Lateral kinetic energy
  • V includes the resistance field R i , the constant force field G i , the vector field strengths E ai and V at (x i , y i ) of the potential energy field formed at the lane line a or road boundary at (x a , y a )
  • Ji represents the potential energy generated by the object j on the ith car;
  • is [0, D/2]; k is the adjustment coefficient; M i represents the equivalent mass of the vehicle; R i represents the road influence at the vehicle factor; i represents the driver Dr factor; V ji j represents the potential energy of the object generated by the first vehicle i; a represents a lane line a; b represents a total of lanes b line; n represents an n Road users; K is a multiplier.
  • the driver in the process of driving the vehicle, the driver always seeks to avoid disadvantages, that is, to ensure the safety while improving the efficiency as much as possible, that is, the driving expression of the driver is used as a mathematical expression of the amount. Described as the system action amount S Risk takes the extreme value, so that the value of the action amount S Risk is the smallest:
  • the amount of action of the system can be described as:
  • the i-th car is the vehicle platform itself, so here m i is known; x i and y i are self-vehicle CAN data.
  • f is the rolling resistance coefficient
  • i ⁇ is the slope
  • C Di is the drag coefficient of the vehicle
  • a i is the windward area of the vehicle
  • ⁇ i is the vehicle rotating mass conversion factor
  • L T, a , D are obtained by camera recognition, and r ai is obtained by multi-sensor fusion data.
  • M i indicates that the equivalent mass of the vehicle can be obtained according to the driving safety field theory; R i and Dr i can take the empirical value.
  • the invention also provides a driver manipulation behavior quantification device based on the principle of minimum action
  • the driver manipulation behavior quantification device based on the principle of minimum action includes:
  • An information collecting device is disposed on the vehicle for acquiring self-vehicle and traffic environment information synchronized with time;
  • S Risk represents the amount of action of the self-vehicle in the preset traffic process
  • t 1 is the starting time of the preset traffic process
  • t 2 is the ending time of the preset traffic process
  • L represents the self-vehicle In the amount of Lagrangian in the preset traffic, the expression of L is:
  • T is the kinetic energy of the self-vehicle
  • V is the potential energy received by the vehicle, which is determined by a predetermined constant force field and resistance field existing along the direction of the traffic flow
  • the electronic control unit is configured to obtain the current driving instruction speed through S Risk according to the self-vehicle and traffic environment information synchronized with time, and minimize the value of the action amount S Risk .
  • the present invention also provides a smart car comprising a driver manipulation behavior quantifying device based on a minimum action amount principle as described in the above embodiments.

Abstract

本发明公开了一种基于最小作用量原理的驾驶人操控行为量化方法及装置，驾驶人操控行为量化方法包括：S1，在自车的电子控制单元中预先设置驾驶人操控机制定量描述模块，所述驾驶人操控机制定量描述模块包括同时反映驾驶人的驾驶操控行为的交通风险和通行效率的作用量S Risk；S2，通过自车上的信息采集装置，获取与时间同步的自车和交通环境信息；S3，根据所述与时间同步的自车和交通环境信息，通过驾驶人操控机制定量描述模块中的S Risk，获取当前行车指导速度，使所述作用量S Risk的值最小，本发明利用最小作用量原理描述驾驶人驾驶车辆过程中关于风险和效率的权衡，能够定量描述任意驾驶人驾驶过程中趋利避害的驾驶特性。

Description

基于最小作用量原理的驾驶人操控行为量化方法及装置 技术领域
本发明涉及智能车应用技术领域，特别是一种基于最小作用量原理的驾驶人操控行为量化方法及装置。
背景技术
道路交通安全与人-车-环境闭环系统有关，在构成交通事故的人、车、环境这三种要素中，通常人这一因素的占比最大，即绝大多数的交通事故都由人为因素造成，因此，驾驶人对车辆的操控行为成为了确保交通安全的关键因素。目前，对于快速发展的智能交通、智能驾驶技术和智能汽车而言，汽车智能化系统的安全性能和驾驶人接受度是制约汽车智能化的重要因素，其中的驾驶人接受度指的就是汽车智能化系统需要符合驾驶人的驾驶操控行为。
现有技术通常可以利用统计学分析方法来研究驾驶人的驾驶操控行为，该研究结果有利于优化汽车智能化系统协调其安全性能和驾驶人接受度。现有的统计学分析方法比如利用概率统计、模糊数学、粗糙集理论等研究方法或者基于实车实验数据统计分析的驾驶人行为特性描述方法。但是，由于驾驶人的驾驶操控行为本身具有个体差异、年龄段差异、性别差异和地域差异等多种差异影响，因此采用现有的研究方法往往需要大量的数据样本，这给研究带了极大的困难。
除了上述提到的不同驾驶人的驾驶操控行为存在的差异之外，目前汽车智能系统还受道路环境复杂性、驾驶行为差异性、行驶工况多变性等难度所限，在实际应用中仍存在误警率高、可接受性差等问题。
因此，为了提高汽车智能系统对驾驶人个体行为波动和差异的适应性，需要对驾驶人的驾驶操控机制进行深入研究。因此，有必要专门针对驾驶人舱驾驶操控机制的定量描述方法进行新的设计。
发明内容
本发明的目的在于提供一种基于最小作用量原理的驾驶人操控行为量化 方法及装置，该方法能够根据驾驶人所在的车辆和交通环境中信息的采集，利用最小作用量原理，描述驾驶人在驾驶过程中趋利避害的驾驶操控机制。
为实现上述目的，本发明提供一种基于最小作用量原理的驾驶人操控行为量化方法及装置，所述基于最小作用量原理的驾驶人操控行为量化方法及装置包括如下步骤：
S1，在自车的电子控制单元中预先设置驾驶人操控机制定量描述模块，所述驾驶人操控机制定量描述模块包括同时反映驾驶人的驾驶操控行为的交通风险和通行效率的作用量S Risk，S Risk的数学表达式为(1)式：
Figure PCTCN2018086640-appb-000001
其中，S Risk代表自车在所述预设交通过程中的作用量，t 1为所述预设交通过程的起始时刻，t 2为所述预设交通过程的终止时刻，L代表自车在预设交通过程中的拉格朗日量，L的表达式为：
L＝T-V
其中，T表示自车的动能，V表示自车受到的势能，该势能由预先设定的沿着车流方向存在的恒定力场和阻力场确定；
S2，通过自车上的信息采集装置，获取与时间同步的自车和交通环境信息；以及
S3，根据所述与时间同步的自车和交通环境信息，通过驾驶人操控机制定量描述模块中的S Risk，获取当前行车指导速度，使所述作用量S Risk的值最小。
进一步地，所述作用量S Risk的获得方法包括：
S11，通过在试验车上安装交通环境信息采集装置，建立车辆平台；
S12，通过不同驾驶人驾驶所述车辆平台在不同环境中进行自由行驶试验，采集与时间同步的自车和环境相关的试验数据；
S13，根据所述试验数据，获得任意交通环境下的所述作用量S Risk的数学表达式。
进一步地，S13中的“交通环境”为单个车辆行驶在平直道路上，预先设定：沿着车流方向存在恒定力场以及存在由于滚动阻力、爬坡阻力、加速阻力和空气阻力造成的阻力场，则所述拉格朗日量L表示为：
Figure PCTCN2018086640-appb-000002
Figure PCTCN2018086640-appb-000003
G i＝m ig
式中，m i为车辆的质量；x i为车辆的纵向位移；
Figure PCTCN2018086640-appb-000004
为车辆的纵向速度；
Figure PCTCN2018086640-appb-000005
为车辆的纵向加速度；g为重力加速度；f为滚动阻力系数；i α为坡度；C Di为车辆的风阻系数；A i为车辆的迎风面积；λ i为车辆旋转质量换算系数。
进一步地，S13中的“交通环境”为单个车辆行驶在有车道线a或道路边界的平直道路上，预先设定：沿着车流方向存在恒定力场以及存在由于滚动阻力、爬坡阻力、加速阻力和空气阻力造成的阻力场，则所述拉格朗日量L表示为：
Figure PCTCN2018086640-appb-000006
Figure PCTCN2018086640-appb-000007
G i＝m ig
F ai＝E ai·M i·R i·(1+Dr i)
Figure PCTCN2018086640-appb-000008
式中，m i为车辆的质量；x i为车辆的纵向位移；
Figure PCTCN2018086640-appb-000009
为车辆的纵向速度；
Figure PCTCN2018086640-appb-000010
为车辆的纵向加速度；y i为车辆的横向位移；
Figure PCTCN2018086640-appb-000011
表示y i的一阶导数，为车辆的横向速度；g为重力加速度；f为滚动阻力系数；i α为坡度；C Di为车辆的风阻系数；A i为车辆的迎风面积；λ i为车辆旋转质量换算系数；E ai为位于(x a,y a)处的车道线a或道路边界形成的势能场在(x i,y i)处的矢量场强；L T,a表示车道线a或道路边界的类型；R a表示车道线a或道路边界处的道路影响因子；D表示车道宽度；|r ai|表示为从车道线a或道路边界指向车辆的质心(x i,y i)的距离矢量；k是调节系数；M i表示车辆的等效质量；R i表示自车处的道路影响因子；Dr i表示驾驶人影响因子。
进一步地，S13中的“交通环境”为跟车模式下的单个车辆行驶在平直道路上，假设沿着车流方向存在一种类似重力的恒定力场G以及存在由于滚动阻力、爬坡阻力、加速阻力、空气阻力造成的阻力场，则所述拉格朗日量L表示为：
Figure PCTCN2018086640-appb-000012
Figure PCTCN2018086640-appb-000013
G i＝m ig
F ai＝E ai·M i·R i·(1+Dr i)
Figure PCTCN2018086640-appb-000014
式中，m i为车辆的质量；x i为车辆的纵向位移；
Figure PCTCN2018086640-appb-000015
为车辆的纵向速度；
Figure PCTCN2018086640-appb-000016
为车辆的纵向加速度；y i为车辆的横向位移；
Figure PCTCN2018086640-appb-000017
为车辆的横向速度；g为重力加速度；f为滚动阻力系数；i α为坡度；C Di为车辆的风阻系数；A i为车辆的迎风面积；λ i为车辆旋转质量换算系数；E ai为位于(x a,y a)处的车道线a或道路边界形成的势能场在(x i,y i)处的矢量场强；L T,a表示车道线a或道路边界的类型；R a表示车道线a或道路边界处的道路影响因子；D表示车道宽度；|r ai|表示为从车道线a或道路边界指向车辆的质心(x i,y i)的距离矢量；k是调节系数；M i表示车辆的等效质量；R i表示自车处的道路影响因子；Dr i表示驾驶人影响因子；V ji表示物体j对第i辆车产生的势能；a表示车道线a；b表示一共有b条车道线；n表示有n个道路使用者；k就是一个调节系数。
本发明还提供一种基于最小作用量原理的驾驶人操控行为量化方法及装置，所述驾驶人操控行为量化装置包括：
信息采集装置，所述信息采集装置设在自车上，用于获取与时间同步的自车和交通环境信息；和
电子控制单元，所述电子控制单元中预先设置驾驶人操控机制定量描述模块，所述驾驶人操控机制定量描述模块包括同时反映驾驶人的驾驶操控行为的交通风险和通行效率的作用量S Risk，S Risk的数学表达式为(1)式：
Figure PCTCN2018086640-appb-000018
其中，S Risk代表自车在所述预设交通过程中的作用量，t 1为所述预设交通过程的起始时刻，t 2为所述预设交通过程的终止时刻，L代表自车在预设交通过程中的拉格朗日量，L的表达式为：
L＝T-V
其中，T表示自车的动能，V表示自车受到的势能，该势能由预先设定的沿着车流方向存在的恒定力场和阻力场确定；
所述电子控制单元用于根据所述与时间同步的自车和交通环境信息，通过S Risk获取当前行车指导速度，使作用量S Risk的值最小。
本发明还提供一种智能车，其特征在于：包括如上所述的驾驶人操控行为量化装置。
本发明的有益效果：本发明的基于最小作用量原理的驾驶人的驾驶操控机制定量描述方法及其装置，采用64线激光雷达、毫米波雷达、视觉传感器组成的多传感器感知系统，搭建智能车辆平台，可识别周围运动物体、静止物体的位置信息和状态信息，通过采集大量的数据，建立数据库，通过分析车辆行驶过程中的动态交通系统特征，辨识从驾驶人的驾驶操控机制，并对驾驶人的驾驶操控机制进行定量描述，可以进一步的对驾驶人的驾驶行为进行定量分析。
附图说明
图1是本发明中的车辆平台的侧视图；
图2是图1所示车辆平台的俯视图；
图3为本发明提供的单个自由行驶车辆的交通系统的简要示意图；
图4为本发明提供的车道线对车辆的约束势能模型的说明示意图；
图5为本发明提供的跟车场景示意图。
具体实施方式
在附图中，使用相同或类似的标号表示相同或类似的元件或具有相同或类似功能的元件。下面结合附图对本发明的实施例进行详细说明。
本实施例所提供的基于最小作用量原理的驾驶人操控行为量化方法主要 考虑的是外界环境所有因素影响的自车,基于最小作用量原理的驾驶人操控行为量化方法包括如下步骤：
S1，在自车的电子控制单元中预先设置驾驶人操控机制定量描述模块，所述驾驶人操控机制定量描述模块包括同时反映驾驶人的驾驶操控行为的交通风险和通行效率的作用量S Risk，S Risk的数学表达式为(1)式：
Figure PCTCN2018086640-appb-000019
其中，S Risk代表自车在所述预设交通过程中的作用量，即可表示为自车在预设交通过程中的拉格朗日量对时间的积分，t 1为所述预设交通过程的起始时刻，t 2为所述预设交通过程的终止时刻，L代表自车在预设交通过程中的拉格朗日量，L的表达式为：
L＝T-V
其中，T表示自车的动能，V表示自车受到的势能，该势能由预先设定的沿着车流方向存在的恒定力场和阻力场确定。
S2，通过自车上的信息采集装置，获取与时间同步的自车和交通环境信息。通过S2，信息采集装置能够检测和识别到自车所处的交通环境信息，该交通环境信息包括周围的障碍物(车辆、骑车人、行人、栅栏、锥形筒)和交通信息(红绿灯、限速标志、车道线)。自车信息包括自车CAN数据，具体为：发动机转速、方向盘转角、车速、档位、加减速度和GPS信息。
S3，根据所述与时间同步的自车和交通环境信息，通过驾驶人操控机制定量描述模块中的S Risk，获取当前行车指导速度，使所述作用量S Risk的值最小。
通过本实施例提供的方法，智能车在行驶的过程中，当信息采集装置检测到自车周边环境中的障碍物或交通信息后，所述驾驶人操控机制定量描述模块会计算预设交通过程中的作用量S Risk代的大小，通过使所述作用量S Risk的值最小，得到一个智能车行驶的当前速度最优值，该最优值作为当前行车指导速度。智能车若依得到当前行车指导速度进行速度控制，能够达到自身安全和通行效率高效的最优。
在一个实施例中，所述作用量S Risk的获得方法包括：
S11，通过在试验车上安装交通环境信息采集装置，建立车辆平台；
S12，通过不同驾驶人驾驶所述车辆平台在不同环境中进行自由行驶试验，采集与时间同步的自车和环境相关的试验数据；
S13，根据所述试验数据，获得任意交通环境下的所述作用量S Risk的数学表达式。
在S11中，在试验车上安装用于获得与时间同步的目标物位置信息和运动信息的雷达和视觉传感器。如图1和图2所示，S11具体包括：
S111，在试验车的顶端安装64线激光雷达1，64线激光雷达1用于获得的目标物纵横向坐标位置、种类的传感器原始数据。
S112，在试验车的前、后、左、右四个方向分别安装第一毫米波雷达2a、第二毫米波雷达2b、第三毫米波雷达2c、第四毫米波雷达2d以及第一视觉传感器3a、第二视觉传感器3b、第三视觉传感器3c、第四视觉传感器3d，通过各视觉传感器和毫米波雷达获取目标物的速度、加速度、纵横向位置信息。
S113，对S111和S112中的64线激光雷达1以及各毫米波雷达和视觉传感器在所述试验车上的位置进行标定。标定方法可以使用现有的标定方法实现。
需要说明的是，S11中各传感器采集到的数据均是传感器原始数据，在后续步骤中均需要将原始数据解析成目标数据后，再加以应用。原始数据：摄像头拍到的图片、视频；激光雷达扫描到的点云；毫米波雷达接收到的毫米波信号。目标数据：把上述三种传感器的原始数据融合之后，获得行人、骑车人、车辆等目标的速度、位置数据。“数据融合”的方法如下：
激光雷达采用特征提取和点云聚类方法检测目标，并得到准确的目标位置信息；视觉传感器对道路目标进行基于机器学习的目标检测，为激光雷达目标检测提供目标类别信息；毫米波雷达识别动态目标并提供准确的目标速度与位置信息。通过数据关联方法，匹配各传感器检测的相同目标信息；最终获得准确的目标物位置信息、运动信息即坐标和速度、加速度。
因此，本实施例采用64线激光雷达、毫米波雷达、视觉传感器组成的多传感器感知系统，搭建车辆平台，可识别周围运动物体、静止物体的位置信息和状态信息。
在一些实施例中，S12中的“驾驶人”的选取原则包括：
选取一定数量具有长期驾驶经验的，且未发生过重大交通事故的驾驶人。
“驾驶人”的数量尽可能地多，这样可以通过采集尽可能多组的试验数据，考虑更多驾驶人的驾驶习惯，从而后续步骤S3和S4得到的风险辨识曲线更加具有广泛性和代表性，有利于提高驾驶人对行车风险辨识的接受度。
S12中的“自车和环境相关的试验数据”包括自车的试验数据和环境的试 验数，其中，
自车的试验数据包括由雷达和视觉传感器采集到的与时间同步的目标物位置信息和运动信息以及自车CAN数据。自车CAN数据包括：发动机转速、方向盘转角、车速、档位、加减速度和GPS信息。各所述雷达和视觉传感器采集的数据进行数据融合，获得准确的目标物位置信息、运动信息即坐标和速度、加速度。传感器获得的信息主要是除了自车之外环境中的其他道路使用者、障碍物的速度、与自车的相对位置。
S12中的“不同环境”包括：
环境类型，一级类型为：校园、园区、城市、高速；二级类型为：上坡、下坡、桥上、桥下、隧道、直道、弯道；
交通参与者，一级类型为：机动车、非机动车、固定物体；二级类型中，机动车包括：轿车、公交车、小型货车、卡车、中型客车、摩托车、其它机动车；非机动车包括：行人、骑车人、两轮车、其它非机动车；固定物体包括：锥形桶、栅栏等；
交通标志，一级类型为：交通标志牌、红绿灯、车道线；二级类型中，交通标志牌包括：限速、限高、限重、指示类、警告类、禁止类、其它标志牌；红路灯包括：圆形、箭头、行人图案、两轮车图案；
道路标志，一级类型包括车道线和路面标示；二级类型中，车道线包括：单实线、双实线和虚线；路面标示包括：直行箭头、右转箭头、左转箭头和其它路面标示；
天气条件：晴、阴、雨、雪。
即，环境的试验数据对应上述“不同环境”中列出来的各种信息。
S12中的与时间同步的“自车和环境相关的试验数据”通过数据库的方式进行存储。
下面针对不同的“交通环境”，对所述作用量S Risk的数学表达式进行说明。
一、S13中的“交通环境”为单个车辆行驶在平直道路上，如图3所示，预先设定：沿着车流方向存在类似重力的恒定力场以及存在由于滚动阻力、爬坡阻力、加速阻力和空气阻力造成的阻力场，则所述拉格朗日量L表示为：
Figure PCTCN2018086640-appb-000020
Figure PCTCN2018086640-appb-000021
G i＝m ig
式中，T包括自车的纵向动能
Figure PCTCN2018086640-appb-000022
V包括阻力场R i和恒定力场G i；
m i为车辆的质量；x i为车辆的纵向位移；
Figure PCTCN2018086640-appb-000023
表示x i的一阶导数，为车辆的纵向速度；
Figure PCTCN2018086640-appb-000024
表示x i的二阶导数，为车辆的纵向加速度；g为重力加速度；f为滚动阻力系数；i α为坡度；C Di为车辆的风阻系数；A i为车辆的迎风面积；λ i为车辆旋转质量换算系数，λ i根据汽车理论的相关内容，一般可取1.05。
二、S13中的“交通环境”为单个车辆行驶在有车道线a或道路边界的平直道路上，如图4所示,预先设定：沿着车流方向存在恒定力场以及存在由于滚动阻力、爬坡阻力、加速阻力和空气阻力造成的阻力场。
当考虑车道线a或道路边界的影响时，按照行车安全场理论，对于位于(x a,y a)处的车道线a或道路边界形成的势能场在(x i,y i)处的矢量场强E ai可以写为：
Figure PCTCN2018086640-appb-000025
因此,越靠近车道线a或道路边界，车辆所受的约束势能增大。因此，由车道线a或道路边界产生的行车安全场场力可以描述为：
F ai＝E ai·M i·R i·(1+DR i)
因此考虑车道线的单个自由行驶车辆交通系统的拉格朗日量L表示为：
Figure PCTCN2018086640-appb-000026
Figure PCTCN2018086640-appb-000027
G i＝m ig
式中，T包括自车的纵向动能
Figure PCTCN2018086640-appb-000028
和横向动能
Figure PCTCN2018086640-appb-000029
V包括阻力场R i、恒定力场G i和位于(x a,y a)处的车道线a或道路边界形成的势能场在(x i,y i)处的矢量场强E ai；
m i为车辆的质量；x i为车辆的纵向位移；
Figure PCTCN2018086640-appb-000030
为车辆的纵向速度；
Figure PCTCN2018086640-appb-000031
为车辆 的纵向加速度；y i为车辆的横向位移；
Figure PCTCN2018086640-appb-000032
表示y i的一阶导数，为车辆的横向速度；g为重力加速度；f为滚动阻力系数；i α为坡度；C Di为车辆的风阻系数；A i为车辆的迎风面积；λ i为车辆旋转质量换算系数；L T,a表示车道线a或道路边界的类型,其大小由交通法规确定(例如，白实线比白虚线对应的值更大)；R a表示车道线a或道路边界处的道路影响因子；D表示车道宽度；|r ai|表示为从车道线a或道路边界指向车辆的质心(x i,y i)的距离矢量，r ai＝(x i-x a,y i-y a)是从车道线a或道路边界指向图4中白色矩形框表示的车辆的质心(x i,y i)的距离矢量，|r ai|的范围为[0,D/2]；k是调节系数；M i表示车辆的等效质量；R i表示自车处的道路影响因子；Dr i表示驾驶人影响因子。
三、S13中的“交通环境”为跟车模式下的单个车辆行驶在平直道路上，如图5所示，假设沿着车流方向存在一种类似重力的恒定力场G以及存在由于滚动阻力、爬坡阻力、加速阻力、空气阻力造成的阻力场。
根据行车安全场理论，运动物体所产生的场强大小为：
Figure PCTCN2018086640-appb-000033
其中，梯度grad E ij为：
Figure PCTCN2018086640-appb-000034
因此在跟车过程中，如图5所示，可以得到物体j对第i辆车产生的势能表达式如下，物体j是除了第i辆车以外的其他道路使用者或者障碍物；第i辆车可以是自车也可以是其它的车辆，即为当前研究的对象车辆：
Figure PCTCN2018086640-appb-000035
因此，所述拉格朗日量L表示为：
Figure PCTCN2018086640-appb-000036
Figure PCTCN2018086640-appb-000037
F ai＝E ai·M i·R i·(1+Dr i)
G i＝m ig
Figure PCTCN2018086640-appb-000038
式中，T包括自车的纵向动能
Figure PCTCN2018086640-appb-000039
和横向动能
Figure PCTCN2018086640-appb-000040
V包括阻力场R i、恒定力场G i、位于(x a,y a)处的车道线a或道路边界形成的势能场在(x i,y i)处的矢量场强E ai和V ji表示物体j对第i辆车产生的势能；
m i为车辆的质量；x i为车辆的纵向位移；
Figure PCTCN2018086640-appb-000041
为车辆的纵向速度；
Figure PCTCN2018086640-appb-000042
为车辆的纵向加速度；y i为车辆的横向位移；
Figure PCTCN2018086640-appb-000043
为车辆的横向速度；g为重力加速度；f为滚动阻力系数；i α为坡度；C Di为车辆的风阻系数；A i为车辆的迎风面积；λ i为车辆旋转质量换算系数；E ai为位于(x a,y a)处的车道线a形成的势能场在(x i,y i)处的矢量场强；L T,a表示车道线a或道路边界的类型,其大小由交通法规确定(例如，白实线比白虚线对应的值更大)；R a表示车道线a或道路边界处的道路影响因子；D表示车道宽度；|r ai|表示为从车道线a或道路边界指向车辆的质心(x i,y i)的距离矢量，r ai＝(x i-x a,y i-y a)是从车道线a或道路边界指向图4中白色矩形框表示的车辆的质心(x i,y i)的距离矢量，|r ai|的范围为[0,D/2]；k是调节系数；M i表示车辆的等效质量；R i表示自车处的道路影响因子；Dr i表示驾驶人影响因子；V ji表示物体j对第i辆车产生的势能；a表示车道线a；b表示一共有b条车道线；n表示有n个道路使用者；k就是一个调节系数。
在一个实施例中,驾驶人驾驶车辆的过程中，始终追求的是趋利避害，即为在保证安全的同时尽可能提高效率，即驾驶人的驾驶操控行为用作用量的数学表达式可描述为系统作用量S Risk取极值,使作用量S Risk的值最小：
Figure PCTCN2018086640-appb-000044
也就是说，任意驾驶人在驾驶车辆时，其驾驶操控行为的可以用他所追求的速度来体现，该速度可以由求解上述δS Risk获得。
比如：在图3的单车自由行驶场景中，系统的作用量可以描述为：
Figure PCTCN2018086640-appb-000045
为求S Risk的极小值，根据前文所述，泛函S Risk取极值时，一定会满足其变分为0，因此有：
Figure PCTCN2018086640-appb-000046
可得
Figure PCTCN2018086640-appb-000047
也就是说，利用上述方法，对于图3中示出的S13中的“交通环境”为单个车辆行驶在平直道路上时，当前行车指导速度为：
Figure PCTCN2018086640-appb-000048
需要说明的是，上述各个参数中，第i辆车即车辆平台本身，因此这里m i为已知；
Figure PCTCN2018086640-appb-000049
x i、y i均为自车CAN数据。
f为滚动阻力系数、i α为坡度、C Di为车辆的风阻系数、A i为车辆的迎风面积和λ i为车辆旋转质量换算系数可以由技术手册或教科书上的内容获得。
L T,a、D由摄像识别获取，r ai由多传感器融合数据获取得到。
M i表示车辆的等效质量可根据行车安全场理论获得；R i、Dr i可取经验值。
本发明还提供一种基于最小作用量原理的驾驶人操控行为量化装置，所述基于最小作用量原理的驾驶人操控行为量化装置包括：
信息采集装置，所述信息采集装置设在自车上，用于获取与时间同步的自车和交通环境信息；和
电子控制单元，所述电子控制单元中预先设置驾驶人操控机制定量描述模块，所述驾驶人操控机制定量描述模块包括同时反映驾驶人的驾驶操控行为的交通风险和通行效率的作用量S Risk，S Risk的数学表达式为(1)式：
Figure PCTCN2018086640-appb-000050
其中，S Risk代表自车在所述预设交通过程中的作用量，t 1为所述预设交通过程的起始时刻，t 2为所述预设交通过程的终止时刻，L代表自车在预设交通过程中的拉格朗日量，L的表达式为：
L＝T-V
其中，T表示自车的动能，V表示自车受到的势能，该势能由预先设定的沿着车流方向存在的恒定力场和阻力场确定；
所述电子控制单元用于根据所述与时间同步的自车和交通环境信息，通过S Risk获取当前行车指导速度，使作用量S Risk的值最小。
本发明还提供一种智能车，所述智能车包括如上述实施例中所述的基于最小作用量原理的驾驶人操控行为量化装置。
最后需要指出的是：以上实施例仅用以说明本发明的技术方案，而非对其限制。本领域的普通技术人员应当理解：可以对前述各实施例所记载的技术方案进行修改，或者对其中部分技术特征进行等同替换；这些修改或者替换，并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围。

Claims (7)

  1. 一种基于最小作用量原理的驾驶人操控行为量化方法及装置，其特征在于，包括如下步骤：
    S1，在自车的电子控制单元中预先设置驾驶人操控机制定量描述模块，所述驾驶人操控机制定量描述模块包括同时反映驾驶人的驾驶操控行为的交通风险和通行效率的作用量S Risk，S Risk的数学表达式为(1)式：
    Figure PCTCN2018086640-appb-100001
    其中，S Risk代表自车在所述预设交通过程中的作用量，t 1为所述预设交通过程的起始时刻，t 2为所述预设交通过程的终止时刻，L代表自车在预设交通过程中的拉格朗日量，L的表达式为：
    L＝T-V
    其中，T表示自车的动能，V表示自车受到的势能，该势能由预先设定的沿着车流方向存在的恒定力场和阻力场确定；
    S2，通过自车上的信息采集装置，获取与时间同步的自车和交通环境信息；以及
    S3，根据所述与时间同步的自车和交通环境信息，通过驾驶人操控机制定量描述模块中的S Risk，获取当前行车指导速度，使所述作用量S Risk的值最小。
  2. 如权利要求1所述的基于最小作用量原理的驾驶人操控行为量化方法及装置，其特征在于，所述作用量S Risk的获得方法包括：
    S11，通过在试验车上安装交通环境信息采集装置，建立车辆平台；
    S12，通过不同驾驶人驾驶所述车辆平台在不同环境中进行自由行驶试验，采集与时间同步的自车和环境相关的试验数据；
    S13，根据所述试验数据，获得任意交通环境下的所述作用量S Risk的数学表达式。
  3. 如权利要求2所述的基于最小作用量原理的驾驶人操控行为量化方法及装置，其特征在于，S13中的“交通环境”为单个车辆行驶在平直道路上， 预先设定：沿着车流方向存在恒定力场以及存在由于滚动阻力、坡道阻力、加速阻力和空气阻力造成的阻力场，则所述拉格朗日量L表示为：
    Figure PCTCN2018086640-appb-100002
    Figure PCTCN2018086640-appb-100003
    G i＝m ig
    式中，m i为车辆i的质量；x i为车辆i的纵向位移；
    Figure PCTCN2018086640-appb-100004
    为车辆i的纵向速度；
    Figure PCTCN2018086640-appb-100005
    为车辆i的纵向加速度；g为重力加速度；f为滚动阻力系数；i α为坡度；C Di为车辆的风阻系数；A i为车辆的迎风面积；λ i为车辆旋转质量换算系数。
  4. 如权利要求2所述的基于最小作用量原理的驾驶人操控行为量化方法及装置，其特征在于，S13中的“交通环境”为单个车辆行驶在有车道线a或道路边界的平直道路预先设定：沿着车流方向存在恒定力场以及存在由于滚动阻力、坡道阻力、加速阻力和空气阻力造成的阻力场，则所述拉格朗日量L表示为：
    Figure PCTCN2018086640-appb-100006
    Figure PCTCN2018086640-appb-100007
    G i＝m ig
    F ai＝E ai·M i·R i·(1+Dr i)
    Figure PCTCN2018086640-appb-100008
    式中，m i为车辆i的质量；x i为车辆i的纵向位移；
    Figure PCTCN2018086640-appb-100009
    为车辆i的纵向速度；
    Figure PCTCN2018086640-appb-100010
    为车辆i的纵向加速度；y i为车辆i的横向位移；
    Figure PCTCN2018086640-appb-100011
    表示y i的一阶导数，为车辆i的横向速度；g为重力加速度；f为滚动阻力系数；i α为坡度；C Di为车辆i的风阻系数；A i为车辆i的迎风面积；λ i为车辆i旋转质量换算系数；E ai为位于(x a,y a)处的车道线a或道路边界形成的势能场在(x i,y i)处的矢量场强；L T,a表示车道 线a或道路边界的类型；R a表示车道线a或道路边界处的道路影响因子；D表示车道宽度；/r ai|表示为从车道线a或道路边界指向车辆的质心(x i,y i)的距离矢量；k是调节系数；M i表示车辆i的等效质量；R i表示自车处的道路影响因子；Dr i表示驾驶人影响因子。
  5. 如权利要求2所述的基于最小作用量原理的驾驶人操控行为量化方法及装置，其特征在于，S13中的“交通环境”为跟车模式下的单个车辆行驶在平直道路上，假设沿着车流方向存在一种类似重力的恒定力场G以及存在由于滚动阻力、爬坡阻力、加速阻力、空气阻力造成的阻力场，则所述拉格朗日量L表示为：
    Figure PCTCN2018086640-appb-100012
    Figure PCTCN2018086640-appb-100013
    G i＝m ig
    F ai＝E ai·M i·R i·(1+Dr i)
    Figure PCTCN2018086640-appb-100014
    式中，m i为车辆i的质量；x i为车辆i的纵向位移；
    Figure PCTCN2018086640-appb-100015
    为车辆i的纵向速度；
    Figure PCTCN2018086640-appb-100016
    为车辆i的纵向加速度；y i为车辆i的横向位移；
    Figure PCTCN2018086640-appb-100017
    为车辆i的横向速度；g为重力加速度；f为滚动阻力系数；i α为坡度；C Di为车辆i的风阻系数；A i为车辆i的迎风面积；λ i为车辆i旋转质量换算系数；E ai为位于(x a,y a)处的车道线a或道路边界形成的势能场在(x i,y i)处的矢量场强；L T,a表示车道线a或道路边界的类型；R a表示车道线a或道路边界处的道路影响因子；D表示车道宽度；|r ai|表示为从车道线a或道路边界指向车辆i的质心(x i,y i)的距离矢量；k是调节系数；M i表示车辆i的等效质量；R i表示自车处的道路影响因子；Dr i表示驾驶人影响因子；V ji表示物体j对第i辆车产生的势能；a表示车道线a；b表示一共有b条车道线；n表示有n个道路使用者；k就是一个调节系数。
  6. 一种基于最小作用量原理的驾驶人操控行为量化装置，其特征在于， 包括：
    信息采集装置，所述信息采集装置设在自车上，用于获取与时间同步的自车和交通环境信息；和
    电子控制单元，所述电子控制单元中预先设置驾驶人操控机制定量描述模块，所述驾驶人操控机制定量描述模块包括同时反映驾驶人的驾驶操控行为的交通风险和通行效率的作用量S Risk，S Risk的数学表达式为(1)式：
    Figure PCTCN2018086640-appb-100018
    其中，S Risk代表自车在所述预设交通过程中的作用量，t 1为所述预设交通过程的起始时刻，t 2为所述预设交通过程的终止时刻，L代表自车在预设交通过程中的拉格朗日量，L的表达式为：
    L＝T-V
    其中，T表示自车的动能，V表示自车受到的势能，该势能由预先设定的沿着车流方向存在的恒定力场和阻力场确定；
    所述电子控制单元用于根据所述与时间同步的自车和交通环境信息，通过S Risk获取当前行车指导速度，使作用量S Risk的值最小。
  7. 一种智能车，其特征在于：包括如权利要求6所述的基于最小作用量原理的驾驶人操控行为量化装置。
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117125083A (zh) * 2023-10-27 2023-11-28 青岛理工大学 考虑驾驶风格倾性的车辆跟驰行为风险量化方法

Families Citing this family (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN111717221B (zh) * 2020-05-29 2022-11-11 重庆大学 自动驾驶接管风险评估和人机友好预警方法及预警系统
CN112562405A (zh) * 2020-11-27 2021-03-26 山东高速建设管理集团有限公司 一种雷达视频智能融合与预警方法及系统
WO2022141912A1 (zh) * 2021-01-01 2022-07-07 杜豫川 一种面向车路协同的感知信息融合表征及目标检测方法
GB2619196A (en) * 2021-03-01 2023-11-29 Du Yuchuan Multi-target vehicle detection and re-identification method based on radar and video fusion

Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060100958A1 (en) * 2004-11-09 2006-05-11 Feng Cheng Method and apparatus for operational risk assessment and mitigation
WO2011057217A2 (en) * 2009-11-06 2011-05-12 University Of Utah Research Foundation Method for gathering, processing, and analyzing data to determine crash risk associated with driving behavior
CN105939917A (zh) * 2014-01-28 2016-09-14 沃尔沃卡车集团 汽车驾驶员反馈系统和相应的方法
CN106462027A (zh) * 2014-06-23 2017-02-22 本田技研工业株式会社 对驾驶员状态进行响应的系统和方法

Family Cites Families (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011084977A2 (en) 2010-01-08 2011-07-14 Hall William D Improved system and method for measuring energy efficiency in vehicles
CN101807341B (zh) * 2010-04-09 2011-12-07 北京工业大学 基于安全性的限速值确定方法
SE1251304A1 (sv) 2012-11-19 2014-05-20 Scania Cv Ab Bränsleförbrukningsanalys i ett fordon
CN104239741B (zh) * 2014-09-28 2016-05-18 清华大学 基于行车风险场的汽车驾驶安全辅助方法
CN104590275B (zh) * 2014-11-27 2019-01-08 厦门雅迅网络股份有限公司 一种驾驶行为分析方法
EP3254917A1 (en) * 2016-06-07 2017-12-13 Giuseppe Barelli Method and device to control vehicle behaviour
US20180096629A1 (en) * 2016-10-04 2018-04-05 GM Global Technology Operations LLC Virtual driving school
CN107161153B (zh) * 2017-06-16 2019-11-29 重庆途安信息科技有限公司 一种驾驶行为评分方法及装置

Patent Citations (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US20060100958A1 (en) * 2004-11-09 2006-05-11 Feng Cheng Method and apparatus for operational risk assessment and mitigation
WO2011057217A2 (en) * 2009-11-06 2011-05-12 University Of Utah Research Foundation Method for gathering, processing, and analyzing data to determine crash risk associated with driving behavior
CN105939917A (zh) * 2014-01-28 2016-09-14 沃尔沃卡车集团 汽车驾驶员反馈系统和相应的方法
CN106462027A (zh) * 2014-06-23 2017-02-22 本田技研工业株式会社 对驾驶员状态进行响应的系统和方法

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117125083A (zh) * 2023-10-27 2023-11-28 青岛理工大学 考虑驾驶风格倾性的车辆跟驰行为风险量化方法
CN117125083B (zh) * 2023-10-27 2024-01-05 青岛理工大学 考虑驾驶风格倾性的车辆跟驰行为风险量化方法

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