CN114735007A - Driving line determining method for auxiliary driving vehicle considering psychological feeling of driver - Google Patents

Driving line determining method for auxiliary driving vehicle considering psychological feeling of driver Download PDF

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CN114735007A
CN114735007A CN202210261837.6A CN202210261837A CN114735007A CN 114735007 A CN114735007 A CN 114735007A CN 202210261837 A CN202210261837 A CN 202210261837A CN 114735007 A CN114735007 A CN 114735007A
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lane
vehicle
compression force
recorded
compression
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CN114735007B (en
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张方伟
李静媛
尹传忠
黎明杰
李俊博
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Shandong Jiaotong University
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Abstract

A driving assistance vehicle travel line determining method takes into account the psychological feeling of the driver. A method of calculating a lane offset of a vehicle causing level L2 based on a driver psychological load. According to the method, the lane offset caused by psychological feelings generated by a driver due to the influence of a series of factors is calculated in the driving process of the L2 level automatic driving (the vehicle realizes partial automatic driving), so that decision support is provided for a lane keeping technology. The calculation steps of the invention mainly comprise: A. determining main factors influencing the psychological load of a driver during the driving process of a target vehicle; B. respectively proposing a mass compression force function, a speed compression force function and a volume compression force function to respectively quantify the size of the increase of the psychological load of a driver caused by the mass, the speed and the volume of the vehicle; C. providing a kinetic energy compression function, a kinetic energy compression force function, a kinetic energy compression degree function, a visual compression force function and a visual fitness function; D. integrating an intuitionistic fuzzy set consisting of kinetic energy compression degree and visual fitness of a driver to obtain the speed compression degree and the visual fitness after the left lane and the right lane are integrated; E. and finally, calculating the lane offset by utilizing the dynamic energy compression degree and the visual fitness of the integrated left and right lanes.

Description

Auxiliary driving vehicle driving line determining method considering driver psychological feeling
Technical Field
The invention relates to an advanced driver assistance system in the field of automatic driving of automobiles, and relates to a method for calculating lane offset caused by influence of psychological load of a driver.
Background
The lane keeping auxiliary driving system controls the driving direction of the vehicle, so that the vehicle automatically drives on the current lane, the operation burden of a driver is further reduced, and the driving safety and the driving comfort are improved. The lane keeping auxiliary driving system mainly comprises a display, a camera, a controller and a sensor, when the lane departure system is started, the camera can constantly collect an identification line of a driving lane, position parameters of an automobile in the current lane are obtained through image processing, when the automobile is detected to depart from the lane, the sensor can timely collect vehicle data and the operation state of a driver, and then the controller automatically controls the steering wheel system to recover the driving direction.
The automatic driving at the L2 level belongs to partial automatic driving, the basic operation is completed by a vehicle, a driver is responsible for peripheral monitoring and taking over the vehicle at any time, the driver must concentrate attention in the whole driving process, and the peripheral safe driving natural environment of a real-time monitoring system prepares for docking the vehicle at any time and any place. The L2 level automatic driving vehicle mainly includes functions such as ACC automatic cruising, automatic car following, automatic parking, and the like.
Disclosure of Invention
Lane keeping during driving of a vehicle is affected by subjective mental activities of the driver, which are largely affected by the driving state of the vehicle in the next lane. The invention provides a method for determining a driving line of an auxiliary driving vehicle by considering the psychological feeling of a driver, which provides decision support for a lane keeping technology by researching lane deviation caused by the psychological load of the driver influenced by a nearby vehicle.
In order to achieve the above object, the present invention provides a method for calculating lane offset of an L2-level autonomous vehicle due to psychological load of a driver, comprising the steps of:
step S1: the main factors influencing the psychological load of a driver during the driving process of the target vehicle are determined through analysis.
Determining the range of the lane offset of the target vehicle by acquiring the width of a driving lane, the position of a driving vehicle relative to a lane line, the deviation direction and the speed through a recognition module in the lane keeping system
Figure BDA0003550782030000021
Where W is the lane width, W is the target vehicle width, and ε is the measurement error ε → 0.
The main factors affecting the psychological perception of the driver include mass, speed, volume, number of vehicles. Wherein the target vehicle mass is recorded as mnLeft lane vehicle mass is recorded as mli(i is more than or equal to 0), and the vehicle mass of the right lane is recorded as mrj(j is not less than 0); target vehicle speed is noted as vnLeft lane vehicle speed is noted vliThe vehicle speed in the right lane is recorded as vrj(ii) a Target vehicle volume is noted as qnLeft lane vehicle volume is recorded as qliAnd the volume of the right lane vehicle is recorded as qrjThe number of vehicles in the unit left lane is recorded as NlThe number of vehicles in the unit right lane is recorded as Nr。
Step S2: the psychological load of a driver, which is influenced by vehicles on left and right lanes, is quantized, and a mass compression force function, a speed compression force function and a volume compression force function are respectively provided to quantize the psychological load of the driver, which is given by the mass, the speed and the volume of the vehicle.
Wherein mass compression force function FmIs recorded as:
Figure BDA0003550782030000022
wherein λ1(dev) C is a mass compression force constant, PlFor left lane vehicles to left lane line distance, L, from the target vehiclelThe distance between the target vehicle and the left lane line is defined, and p + l is greater than 0.
Velocity compression force function FvIs recorded as: fv＝[λ2(dev)·vn·vl(s)i(j)·|vl(s)i(j)-vn|]，
Wherein λ is2(dev) C is the velocity compression force constant.
Volume compression force function FqIs recorded as: fq＝λ3(dev)·qn·ql(s)i(j)
Wherein λ3(dev) C is the volume compression force constant.
Step S3: the psychological compression force borne by the driver is divided into kinetic compression force and visual compression force.
In which the kinetic energy of compression EAIs marked as
Figure BDA0003550782030000031
Kinetic compression force function FAIs marked as
Figure BDA0003550782030000032
Wherein KA＞0。
The degree of compression of kinetic energy is recorded as OAIs marked as
Figure BDA0003550782030000033
Wherein FAmax＞0，OA∈[0，1]。
Visual compression force function FBIs marked as
Figure BDA0003550782030000034
Visual fitness OBIs recorded as:
Figure BDA0003550782030000035
wherein KB＞0FAmax＞0，OB∈[0，1]。
Step S4: and (3) carrying out information integration on the vehicles in the left lane and the right lane by using an intuitive fuzzy mixed average operator (IFHA).
Figure BDA0003550782030000036
Satisfy the intuitionistic fuzzy attribute, be a set of intuitionistic fuzzy numbers. Intuitive fuzzy number SjAn intuitive fuzzy set S consisting of (j ═ 1, 2.., n) is recorded as S
Figure BDA0003550782030000037
Wherein omegaj＝(ω1，ω2，...，ωn) Is SjA weight vector ω of (j ═ 1, 2.., n)j∈[0，1]。
Weight vector
Figure BDA0003550782030000038
Weighted intuitive fuzzy number
Figure BDA0003550782030000039
Weighted intuitive fuzzy number
Figure BDA0003550782030000041
A scoring function of wherein
Figure BDA0003550782030000042
Then SjThe score function of (c):
Figure BDA0003550782030000043
wherein, pil(r)jIn order to intuitively blur the hesitation degree of the set,
Figure BDA0003550782030000044
to weighted intuitive fuzzy number SjThe score functions are ordered from large to small to obtain a new group of intuitionistic fuzzy numbers
Figure BDA0003550782030000045
Determining ordered weighted average operator weights
Figure BDA0003550782030000046
Wherein Vi＝(V1,V2...Vn) For ordered weighted intuitive fuzzy arrays
Figure BDA0003550782030000047
The weighting vector of (2);
step five; integrating IFHA operators to obtain integrated speed compression degree Il(r)ADegree of adaptability to vision Il(r)B。
The integration formula of the IFHA operator is expressed as
Figure BDA0003550782030000048
Step S5: and finally, calculating the lane deviation direction and the magnitude of the deviation by utilizing the integrated kinetic energy compression degree and visual fitness of the left lane and the right lane. The visual density of the lane is the number of vehicles in the unit length of the lane, and the visual density of the unit left lane is recorded as rholUnit right lane visual density is denoted as ρr. Wherein unit left lane visual density
Figure BDA0003550782030000049
Unit right lane visual density
Figure BDA00035507820300000410
Then the degree of psychological stress of the left lane vehicle on the driver is recorded as
Figure BDA00035507820300000411
In the same way, the psychological stress degree of the right lane vehicle on the driver is
Figure BDA0003550782030000051
Lane offset
Figure BDA0003550782030000052
Wherein
Figure BDA0003550782030000053
Alpha > 0 shifted rightward by alpha < 0 shifted leftward.
Lane keeping during driving of a vehicle is affected by subjective mental activities of the driver, which are largely affected by the driving state of the vehicle in the next lane. According to the lane keeping method and the lane keeping system, the lane deviation caused by the psychological load of the driver due to the influence of the nearby vehicle is researched, the influence of the psychological load of the driver on the lane deviation is quantized, and decision support is provided for a lane keeping technology.
Drawings
FIG. 1 is a flow chart of the present invention.
Fig. 2 is a plan view of the target vehicle.
Detailed Description
The technical scheme of the invention is explained in detail in the following with reference to the attached figure 1
As shown in fig. 1, the present invention aims to provide a method for calculating lane offset of an automatic driving car of level L2, which is affected by psychological load of a driver. The method mainly comprises three steps of data acquisition, model establishment and information integration. The specific implementation steps are as follows:
step S1: firstly, a research target road and a target vehicle are determined, and data required by research are collected, wherein the required information is shown in fig. 2.
The data mainly collected mainly include lane information and vehicle information. The required information is collected by a recognition module in the lane keeping system. And taking the unit lane length as an experimental road section, and acquiring the lane width W and the target vehicle width W.
The main factors influencing the psychological load of the driver include mass, speed, volume and number of vehicles. Wherein the target vehicle mass is recorded as mnLeft lane vehicle mass is recorded as mli(i is more than or equal to 0), and the vehicle mass of the right lane is recorded as mrj(j is not less than 0); target vehicle speed is noted as vnLeft lane vehicle speed is noted vliThe vehicle speed in the right lane is recorded as vrj(ii) a Target vehicle volume is noted as qnLeft lane vehicle volume is recorded as qliThe volume of the right lane vehicle is recorded as qrj(ii) a The number of vehicles in the unit left lane is recorded as NlThe number of vehicles in the unit right lane is recorded as Nr。
Step S2: secondly, calculating the proposed model, and respectively calculating mass compression force, speed compression force and volume compression force of the left lane and the right lane according to the method disclosed by the patent claim 3;
step S3: according to the patent claim 4, the compression force applied to the driver is divided into the kinetic compression force and the visual compression force, and the compression kinetic energy E is calculated respectivelyAKinetic compression force function FADegree of compression with kinetic energy OAVisual compression force function FBDegree of adaptation to vision OB。
Step S4: according to the patent claim 5, the intuitive fuzzy set consisting of the kinetic energy compression degree and the visual fitness of the driver is integrated to obtain the speed compression degree and the visual fitness after the left lane and the right lane are integrated.
Step S5: according to the patent claim 6, the visual density of the left lane and the right lane is calculated, and the lane offset is obtained by utilizing the integrated kinetic energy compression degree and visual fitness of the left lane and the right lane.

Claims (5)

1. A driving-assist vehicle travel line determining method taking into account driver psychology, characterized by comprising the steps of:
step S1: determining main factors influencing the psychological load of a driver during the driving process of a target vehicle; including mass, speed, volume, number of vehicles; wherein the target vehicle mass is recorded as mnLeft lane vehicle mass is recorded as mli(i is more than or equal to 0), and the vehicle mass of the right lane is recorded as mrj(j is not less than 0); target vehicle speed is noted as vnLeft lane vehicle speed is noted as vliThe vehicle speed in the right lane is recorded as vrj(ii) a Target vehicle volume is noted as qnThe volume of the left lane vehicle is recorded as qliThe volume of the right lane vehicle is recorded as qrj(ii) a The number of vehicles in the unit left lane is recorded as NlThe number of vehicles in the unit right lane is recorded as Nr(ii) a Acquiring the width of a driving lane, the position of a driving vehicle relative to a lane line, a deviation direction and a speed through an identification module in a lane keeping system; range of target vehicle lane offset
Figure FDA0003550782020000011
Wherein W is recorded as the lane width, W is recorded as the target vehicle width, and epsilon is the measurement error epsilon → 0;
step S2: respectively proposing a mass compression force function, a speed compression force function and a volume compression force function to respectively quantify the size of the increase of the psychological load of a driver caused by the mass, the speed and the volume of the vehicle;
step S3: providing a kinetic energy compression function, a kinetic energy compression force function, a kinetic energy compression degree function, a visual compression force function and a visual fitness function;
step S4: integrating an intuitionistic fuzzy set consisting of kinetic energy compression and visual compression of a driver to obtain the speed compression and visual fitness after the left lane and the right lane are integrated;
step S5: and finally, calculating the lane offset by utilizing the integrated kinetic energy compression degree and visual fitness of the left lane and the right lane.
2. The method as claimed in claim 1, wherein the psychological compression force function comprises a mass compression force function FmVelocity compression force function FvVolume compression force function Fq(ii) a Wherein the mass compression force function FmIs marked as
Figure FDA0003550782020000021
Wherein λ1(dev) is a mass compression force constant, PlDistance of left lane line of left lane vehicle from target vehicle, llThe distance between the target vehicle and the left lane line is, and p + l is more than 0;
velocity compression force function FvIs marked as Fv＝[λ2(dev)·vn·vl(s)i(j)·|vl(s)i(j)-vn|]Wherein λ is2(dev) C is a velocity compression force constant;
volume compression force function FqIs recorded as: fq＝λ3(dev)·qn·ql(s)i(j)Wherein λ is3(dev) C is the volume compression force constant.
3. The method according to claim 1, wherein the compression kinetic energy E is a kinetic energy of compressionAKinetic compression force function FADegree of compression with kinetic energy OAVisual compression force function FBDegree of adaptation to vision OBAre respectively marked as
Figure FDA0003550782020000022
Figure FDA0003550782020000023
Figure FDA0003550782020000024
Wherein FAmax＞0，OA∈[0，1](ii) a In addition, note that
Figure FDA0003550782020000025
To obtain
Figure FDA0003550782020000026
Wherein FAmax＞0，OB∈[0，1]，KA＞0,KB＞0。
4. The method for determining the driving assistance vehicle driving line according to claim 1, wherein the intuitive fuzzy number comprising the dynamic energy compression degree and the visual fitness degree of the vehicle on the left and right lanes is integrated by using an intuitive fuzzy mixing average operator (IFHA), and the integration steps are as follows:
the method comprises the following steps:
Figure FDA0003550782020000027
satisfying the intuitive fuzzy attribute, and forming a group of intuitive fuzzy numbers; intuitive fuzzy number SjAn intuitive fuzzy set S consisting of (j ═ 1, 2.., n) is recorded as S
Figure FDA0003550782020000031
Wherein ω isj＝(ω1，ω2，...，ωn) Is SjA weight vector ω of (j ═ 1, 2.., n)j∈[0，1](ii) a Weight vector in the invention
Figure FDA0003550782020000032
Weighted intuitive fuzzy number
Figure FDA0003550782020000033
Step two; weighted intuitive fuzzy number
Figure FDA0003550782020000034
A scoring function of (a), and
Figure FDA0003550782020000035
Sjscore function S ofl(r)jIs marked as
Figure FDA0003550782020000036
Wherein pil(r)jIn order to intuitively blur the hesitation degree of the set,
Figure FDA0003550782020000037
step three: to weighted intuitive fuzzy number SjThe score functions are ordered from large to small to obtain a new group of intuitionistic fuzzy numbers
Figure FDA0003550782020000038
Step four; determining ordered weighted average operator weights
Figure FDA0003550782020000039
Wherein Vi＝(V1,V2...Vn) For ordered weighted intuitive fuzzy arrays
Figure FDA00035507820200000310
The weighting vector of (2);
step five; integrating IFHA operators to obtain integrated speed compression degree Il(r)ADegree of adaptability to vision Il(r)B(ii) a The integration function of the IFHA operator is expressed as
Figure FDA00035507820200000311
5. The method for determining the driving line of the assistant driving vehicle according to the claim 4, wherein the direction and the offset of the lane deviation are finally calculated by using the integrated kinetic energy compression degree and visual fitness of the left lane and the right lane; the visual density of the lane is the number of vehicles in the unit length of the lane, and the visual density of the unit left lane is recorded as rholUnit right lane visual density is recorded as ρr(ii) a Wherein unit left lane visual density
Figure FDA0003550782020000041
Unit right lane visual density
Figure FDA0003550782020000042
The psychological stress degree of the left lane vehicle on the driver is recorded as
Figure FDA0003550782020000043
In the same way, the psychological compression degree of the right lane vehicle on the driver is recorded as
Figure FDA0003550782020000044
Lane offset
Figure FDA0003550782020000045
Wherein
Figure FDA0003550782020000046
When alpha is larger than 0, the right deviation alpha is smaller than 0, and the left deviation is larger.
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Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101511861B1 (en) * 2013-11-04 2015-04-13 현대오트론 주식회사 Driver assistance systems and controlling method for the same
CN108216231A (en) * 2018-01-12 2018-06-29 合肥工业大学 One kind can open up united deviation auxiliary control method based on steering and braking
US20200269839A1 (en) * 2019-02-22 2020-08-27 Suzuki Motor Corporation Driving Control Apparatus for Vehicle
CN112046482A (en) * 2020-08-10 2020-12-08 华人运通(上海)自动驾驶科技有限公司 Lane departure intervention control method and device and computer-readable storage medium
CN112389448A (en) * 2020-11-23 2021-02-23 重庆邮电大学 Abnormal driving behavior identification method based on vehicle state and driver state
CN113945221A (en) * 2021-09-26 2022-01-18 华中科技大学 Automatic driving lane width determination method considering proximity effect

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
KR101511861B1 (en) * 2013-11-04 2015-04-13 현대오트론 주식회사 Driver assistance systems and controlling method for the same
CN108216231A (en) * 2018-01-12 2018-06-29 合肥工业大学 One kind can open up united deviation auxiliary control method based on steering and braking
US20200269839A1 (en) * 2019-02-22 2020-08-27 Suzuki Motor Corporation Driving Control Apparatus for Vehicle
CN112046482A (en) * 2020-08-10 2020-12-08 华人运通(上海)自动驾驶科技有限公司 Lane departure intervention control method and device and computer-readable storage medium
CN112389448A (en) * 2020-11-23 2021-02-23 重庆邮电大学 Abnormal driving behavior identification method based on vehicle state and driver state
CN113945221A (en) * 2021-09-26 2022-01-18 华中科技大学 Automatic driving lane width determination method considering proximity effect

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