CN112590791A - Intelligent vehicle lane change gap selection method and device based on game theory - Google Patents

Intelligent vehicle lane change gap selection method and device based on game theory Download PDF

Info

Publication number
CN112590791A
CN112590791A CN202011489329.0A CN202011489329A CN112590791A CN 112590791 A CN112590791 A CN 112590791A CN 202011489329 A CN202011489329 A CN 202011489329A CN 112590791 A CN112590791 A CN 112590791A
Authority
CN
China
Prior art keywords
vehicle
lane
target
data
chips
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.)
Granted
Application number
CN202011489329.0A
Other languages
Chinese (zh)
Other versions
CN112590791B (en
Inventor
董长印
王昊
彭显玥
刘晓瀚
王雷震
巴贝尔
李昌泽
卢云雪
阮天承
刘雍翡
陈�全
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.)
Southeast University
Original Assignee
Southeast University
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 Southeast University filed Critical Southeast University
Priority to CN202011489329.0A priority Critical patent/CN112590791B/en
Publication of CN112590791A publication Critical patent/CN112590791A/en
Application granted granted Critical
Publication of CN112590791B publication Critical patent/CN112590791B/en
Active legal-status Critical Current
Anticipated expiration legal-status Critical

Links

Images

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
    • B60W30/00—Purposes of road vehicle drive control systems not related to the control of a particular sub-unit, e.g. of systems using conjoint control of vehicle sub-units
    • B60W30/18—Propelling the vehicle
    • B60W30/18009—Propelling the vehicle related to particular drive situations
    • B60W30/18163—Lane change; Overtaking manoeuvres
    • 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
    • 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
    • B60W40/10—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 vehicle motion
    • 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
    • B60W40/10—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 vehicle motion
    • B60W40/105—Speed

Landscapes

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

Abstract

The invention relates to the field of intelligent traffic control, in particular to a method and a device for selecting lane changing gaps of an intelligent vehicle based on a game theory. The method comprises the steps of obtaining gap selection data of a target vehicle, calculating corresponding data chips according to the gap selection data of the target vehicle, calculating a game coefficient through the data chips, and obtaining the recommended grade and the recommended degree of lane change gap selection of the target vehicle according to the game coefficient. The method and the device of the invention calculate the chips of the vehicle and the target vehicle after the target vehicle and the adjacent gap of the target lane based on the speed data, the acceleration data, the position data and other data of the vehicle after the adjacent gap of the target vehicle and the target lane as basic information, thereby calculating the game coefficient, determining the recommended grade and the recommended degree of the selection of the lane changing gap of the target vehicle, providing scientific and reasonable judgment and decision basis for the driving of intelligent vehicles, and ensuring the road traffic safety.

Description

Intelligent vehicle lane change gap selection method and device based on game theory
Technical Field
The invention relates to the field of intelligent traffic control, in particular to a method and a device for selecting lane changing gaps of an intelligent vehicle based on a game theory.
Background
With the combination of emerging technologies such as electronic computers and the like with the modern automobile industry, intelligent vehicles equipped with intelligent devices, including intelligent networked automobiles and autonomous automobiles, are continuously developing. The intelligent vehicle can sense the external traffic environment and complete driving behaviors such as speed regulation, direction control and the like. It is very necessary to design a reasonable lane change gap selection strategy to help the intelligent vehicle adapt to more complex traffic environments.
The Chinese patent CN201910603412.7 discloses a method for establishing an automatic driving lane change decision model in a hybrid driving environment, which utilizes a game theory-kinematics coupling model method to establish a multi-step dynamic game lane change model of an automatic driving vehicle and a human-driven target lane rear vehicle in the hybrid driving environment.
The method for establishing the automatic driving vehicle lane change conflict coordination model based on the game theory, disclosed by the Chinese patent CN201910603407.6, considers the benefit balance among vehicles and the system integral maximization, and designs a lane change-avoidance decision rule of two vehicles when a lane change vehicle conflicts with a rear vehicle of a target lane in an automatic driving environment.
According to researches, the scheme provided by the prior art cannot judge the situation change on a road, the multi-group game calculation effect of a target vehicle and a plurality of adjacent gap rear vehicles is poor, and lane change gap selection considering factors such as gap sequencing, lane change forced terminal points and the like is more important under the forced lane change conditions such as highway exit circle road sections and the like, but the factors are not considered in the existing researches.
Disclosure of Invention
The invention provides a scientific and reasonable judgment and decision basis for intelligent vehicle driving, guarantees road traffic safety, and provides an intelligent vehicle lane change gap selection method and device based on a game theory.
The invention adopts the following technical scheme:
the invention discloses an intelligent vehicle lane change gap selection method based on a game theory, which comprises the following steps:
step 1, obtaining clearance selection data of a target vehicle; the gap selection data are target vehicle data and target lane adjacent gap rear vehicle data
The acquired data of the rear vehicle in the adjacent gap of the target lane is as follows: speed data, acceleration data, position data, warning action data and steering lamp state data;
the acquired target vehicle data are: speed data, acceleration data, position data, head deflection angle data and steering lamp state data;
step 2, calculating and obtaining speed chips, acceleration chips, car following distance chips, warning chips and steering lamp chips of the vehicles behind the adjacent gaps of the target lane according to the speed data, the acceleration data, the position data, the warning action data and the steering lamp state data of the vehicles behind the adjacent gaps of the target lane; and calculating the chips of the vehicles behind the adjacent gaps of the target lane by using the speed chips, the acceleration chips, the chips of the following distances, the warning chips and the steering lamp chips of the vehicles behind the adjacent gaps of the target lane.
Step 3, calculating and obtaining longitudinal speed chips, longitudinal acceleration chips, transverse speed chips, transverse acceleration chips, head deflection angle chips, distance lane line length chips, distance lane changing end point length chips, gap sorting chips and steering lamp chips of the target vehicle according to the speed data, acceleration data, position data, head deflection angle data and steering lamp state data of the target vehicle; the chips of the target vehicle are obtained by calculating the chips of the longitudinal speed, the longitudinal acceleration, the transverse speed, the transverse acceleration, the nose deflection angle, the distance lane line, the distance lane changing end point, the gap sorting chips and the steering lamp chips of the target vehicle.
And 4, calculating a game coefficient through chips of the vehicles behind the adjacent gaps of the target lanes and chips of the target vehicles, and obtaining the recommended grade and the recommended degree of the target vehicle lane changing gap selection according to the game coefficient.
The invention discloses an intelligent vehicle lane change gap selection method based on game theory,
the position data of the rear vehicle in the gap adjacent to the target lane in the step 1 comprises the distance between the rear vehicle in the gap adjacent to the target lane and the front vehicle in the gap adjacent to the target lane;
the warning action data of the vehicle behind the adjacent gap of the target lane comprises the following data: flashing a flash lamp of a rear vehicle in an adjacent gap of the target lane and whistling the rear vehicle in the adjacent gap of the target lane;
the position data of the target vehicle includes: the length of the center of the front bumper of the target vehicle from the lane line, the length of the target vehicle from the forced lane changing terminal point and the current gap selection times,
the nose deflection angle data of the target vehicle is as follows: and the included angle between the driving direction of the target vehicle and the lane line.
Preferably, in step 1, the position data of the vehicle behind the gap adjacent to the target lane is the distance between the vehicle and the front vehicle, the warning action data of the vehicle behind the gap adjacent to the target lane includes flashing and whistling of a vehicle flash lamp, the position data of the target vehicle includes the length from the center of a front bumper of the target vehicle to a lane line, the length from the target vehicle to a forced lane change end point and the current gap selection times, and the nose slip angle data of the target vehicle is the included angle between the driving direction of the target vehicle and the lane line.
Preferably, the method for calculating chips of the vehicle behind the gap between the adjacent target lanes in the step (2) comprises the following steps:
Figure BDA0002840280350000031
wherein, CfChips of the rear vehicle in the adjacent gap of the target lane; cv，Ca，CL，CLS，CTLRespectively including speed chips, acceleration chips, distance chips, warning chips and steering lamp chips of the rear vehicle in the adjacent gaps of the target lane; alpha is alpha1，α2，α3，α4，α5The weights are corresponding to the chips respectively.
Preferably, the method for calculating the speed chips of the vehicles behind the adjacent gaps of the target lanes in the step (2) comprises the following steps:
Cv＝vf
wherein v isfThe speed of the vehicle behind the adjacent gap of the target lane is km/h.
The method for calculating the acceleration chips of the vehicles behind the adjacent gaps of the target lanes comprises the following steps:
Ca＝af 2Θ(-af)
wherein, afThe acceleration of the vehicle after the adjacent gap of the target lane is in m/s2，
Figure BDA0002840280350000032
Figure BDA0002840280350000033
The method for calculating the distance between the following vehicles of the vehicles behind the adjacent gaps of the target lane comprises the following steps:
Figure BDA0002840280350000034
wherein, L is the distance between the rear vehicle and the front vehicle in the adjacent gap of the target lane, and the unit is m, namely the gap size.
The method for calculating the warning chips of the vehicle behind the adjacent gap of the target lane comprises the following steps:
Figure BDA0002840280350000035
wherein, the warning action comprises flashing or whistling of a flash of the vehicle.
The method for calculating the steering lamp chips of the vehicle after the adjacent gap of the target lane comprises the following steps:
Figure BDA0002840280350000036
preferably, the method for calculating the chips of the target vehicle in the step (3) includes:
Figure BDA0002840280350000041
wherein, FsA chip that is a target vehicle; n is the selection gap sorting chips, which represents the Nth group of games, namely the Nth gap; fZv，FZa，FHv，FHa，Fag，FCTL，FFL，FTLRespectively being a longitudinal speed chip, a longitudinal acceleration chip, a transverse speed chip, a transverse acceleration chip, a nose deflection angle chip, a distance lane line length chip, a distance lane changing end point length chip and a steering lamp chip of a target vehicle; beta is a1，β2，β3，β4，β5，β6，β7，β8The weights are corresponding to the chips respectively.
Preferably, the method for calculating the longitudinal speed chip of the target vehicle in the step (3) comprises the following steps:
FZv＝|vmax-vs·cosθ|
wherein v ismaxThe unit is km/h for limiting the speed of the road; v. ofsThe speed of the target vehicle is km/h; theta is the included angle between the driving direction and the lane line and is the unit. .
The method for calculating the longitudinal acceleration chips of the target vehicle comprises the following steps:
FZa＝(as·cosθ)2
wherein, asIs the acceleration of the target vehicle in m/s2。
The method for calculating the transverse speed chip of the target vehicle comprises the following steps:
FHv＝vs·sinθ
the method for calculating the transverse acceleration chips of the target vehicle comprises the following steps:
FHa＝(as·sinθ)2
the method for calculating the nose deflection angle chips of the target vehicle comprises the following steps:
Figure BDA0002840280350000042
the method for calculating the distance lane line length chip of the target vehicle comprises the following steps:
Figure BDA0002840280350000043
wherein L iswThe lane width is generally 3-3.75 m; l isCTLIs the length of the center of the front bumper of the target vehicle from the lane line used to distinguish the target lane from the current lane, in m.
The method for calculating the distance lane change end point length chip of the target vehicle comprises the following steps:
Figure BDA0002840280350000044
wherein d isMLength of target vehicle from forced end point of lane change when lane change demand is generated, unitIs m; dFLThe length of the target vehicle from the lane change forced terminal in the lane change clearance selection process is m. In an urban road, a lane change forced terminal point is the starting point of a solid line drawn by a forbidden lane change before an intersection; in the exit ramp section of the expressway, the forced lane change end point refers to the starting point of the parallel lane of the exit ramp; the other cases default to 1000 m.
The method for calculating the steering lamp chips of the target vehicle comprises the following steps:
Figure BDA0002840280350000051
preferably, the method for calculating the game coefficients in the step (4) comprises the following steps:
Figure BDA0002840280350000052
wherein P is a game coefficient.
Preferably, in the step (4), the recommended level and the recommended degree of the ramp gap selection are classified into six levels according to the following rules:
game coefficient P Recommending a rating Degree of recommendation
[0,0.5) Class VI Is not recommended very much
[0.5,1) Class V Is not recommended
[1,2) Grade IV General recommendations
[2,5) Class III Recommending
[5,10) Class II Moderate recommendations
[10,+∞) Class I Very recommended
The intelligent vehicle lane change gap selection device based on the game theory and the intelligent vehicle state sensing module comprise a self-sensing unit for a vehicle and a sensing unit for a vehicle behind a gap adjacent to a target lane, and are respectively used for acquiring speed data, acceleration data, position data, head deflection angle data, steering lamp state data of the target vehicle and speed data, acceleration data, position data, warning action data and steering lamp state data of the vehicle behind the gap adjacent to the target lane.
The data storage module comprises a historical data unit and a real-time data unit which are respectively used for storing historical and real-time interval selection data;
and the game coefficient calculation module comprises a chip calculation unit of the rear vehicle in the adjacent gap of the target lane, a chip calculation unit of the target vehicle and a game coefficient calculation unit. Respectively calculating chips of the vehicles behind the adjacent gaps of the target lanes, chips of the target vehicles and game coefficients;
and the intelligent vehicle lane change gap selection recommendation module is used for determining the recommendation level and recommendation degree of the target vehicle lane change gap selection.
Advantageous effects
The invention provides an intelligent vehicle lane change gap selection method and device based on a game theory, which are characterized in that chips of a vehicle behind a target lane adjacent gap and a target vehicle are calculated based on speed data, acceleration data, position data and other data of the vehicle behind the target vehicle and the adjacent gap of the target lane as basic information, so that a game coefficient is calculated, the recommendation level and recommendation degree of the target vehicle lane change gap selection are determined, scientific and reasonable judgment and decision basis is provided for the driving of an intelligent vehicle, and the road traffic safety is guaranteed.
Drawings
FIG. 1 is a flow chart of a method of an embodiment of the present invention.
Fig. 2 is a schematic illustration of traffic conditions in an example of an embodiment of the invention.
Fig. 3 is a schematic structural diagram of an apparatus according to an embodiment of the present invention.
Detailed Description
In order to make the purpose and technical solution of the embodiments of the present invention clearer, the technical solution of the embodiments of the present invention will be clearly and completely described below with reference to the drawings of the embodiments of the present invention. It is to be understood that the embodiments described are only a few embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the invention without any inventive step, are within the scope of protection of the invention.
As shown in fig. 1, an intelligent vehicle lane change gap selection method based on a game theory includes the following steps:
(1) the method comprises the steps of obtaining gap selection data of a target vehicle, wherein the gap selection data comprise speed data, acceleration data, position data, warning action data and steering lamp state data of a vehicle behind a gap adjacent to a target lane, and the speed data, the acceleration data, the position data, vehicle head deflection angle data and the steering lamp state data of the target vehicle.
Specifically, the position data of the rear vehicle in the adjacent gap of the target lane is the distance between the vehicle and the front vehicle, the warning action data of the rear vehicle in the adjacent gap of the target lane comprises the flashing and whistling conditions of a vehicle flash lamp, the position data of the target vehicle comprises the length from the center of a front bumper of the target vehicle to a lane line, the length from the target vehicle to a forced lane change terminal point and the current gap selection times, and the head deflection angle data of the target vehicle is the included angle between the driving direction of the target vehicle and the lane line.
(2) Calculating chips of the rear vehicle in the adjacent gap of the target lane; the chips comprise speed chips, acceleration chips, car following distance chips, warning chips and turn light chips.
Specifically, the method for calculating chips of the vehicle after the adjacent gap of the target lane comprises the following steps:
Figure BDA0002840280350000071
wherein, CfChips of the rear vehicle in the adjacent gap of the target lane; cv，Ca，CL，CLS，CTLRespectively including speed chips, acceleration chips, distance chips, warning chips and steering lamp chips of the rear vehicle in the adjacent gaps of the target lane; alpha is alpha1，α2，α3，α4，α5The weights are corresponding to the chips respectively.
Specifically, the method for calculating the speed chips of the vehicles behind the adjacent gaps of the target lane comprises the following steps:
Cv＝vf
wherein v isfThe speed of the vehicle behind the adjacent gap of the target lane is km/h.
The method for calculating the acceleration chips of the vehicles behind the adjacent gaps of the target lanes comprises the following steps:
Ca＝af 2Θ(-af)
wherein, afThe acceleration of the vehicle after the adjacent gap of the target lane is in m/s2，
Figure BDA0002840280350000072
The method for calculating the distance between the following vehicles of the vehicles behind the adjacent gaps of the target lane comprises the following steps:
Figure BDA0002840280350000073
and L is the distance between the rear vehicle and the front vehicle in the adjacent gap of the target lane, namely the gap size, and the unit is m.
The method for calculating the warning chips of the vehicle behind the adjacent gap of the target lane comprises the following steps:
Figure BDA0002840280350000074
wherein, the warning action comprises flashing or whistling of a flash of the vehicle.
The method for calculating the steering lamp chips of the vehicle after the adjacent gap of the target lane comprises the following steps:
Figure BDA0002840280350000075
(3) calculating chips of the target vehicle; the chips comprise longitudinal speed chips, longitudinal acceleration chips, transverse speed chips, transverse acceleration chips, car head deflection angle chips, distance lane line length chips, distance lane changing end point length chips, gap sorting chips and steering lamp chips.
Specifically, the method for calculating the chips of the target vehicle comprises the following steps:
Figure BDA0002840280350000081
wherein, FsA chip that is a target vehicle; n is the selection gap sorting chips, which represents the Nth group of games, namely the Nth gap; fZv，FZa，FHv，FHa，Fag，FCTL，FFL，FTLRespectively being a longitudinal speed chip, a longitudinal acceleration chip, a transverse speed chip, a transverse acceleration chip, a nose deflection angle chip, a distance lane line length chip, a distance lane changing end point length chip and a steering lamp chip of a target vehicle; beta is a1，β2，β3，β4，β5，β6，β7，β8The weights are corresponding to the chips respectively.
Specifically, the method for calculating the longitudinal speed chip of the target vehicle comprises the following steps:
FZv＝|vmax-vs·cosθ|
wherein v ismaxThe unit is km/h for limiting the speed of the road; v. ofsThe speed of the target vehicle is km/h; theta is the included angle between the driving direction and the lane line and has the unit of degree.
The method for calculating the longitudinal acceleration chips of the target vehicle comprises the following steps:
FZa＝(as·cosθ)2
wherein, asIs the acceleration of the target vehicle in m/s2。
The method for calculating the transverse speed chip of the target vehicle comprises the following steps:
FHv＝vs·sinθ
the method for calculating the transverse acceleration chips of the target vehicle comprises the following steps:
FHa＝(as·sinθ)2
the method for calculating the nose deflection angle chips of the target vehicle comprises the following steps:
Figure BDA0002840280350000082
the method for calculating the distance lane line length chip of the target vehicle comprises the following steps:
Figure BDA0002840280350000083
wherein L iswThe lane width is generally 3-3.75 m; l isCTLIs the length of the center of the front bumper of the target vehicle from the lane line used to distinguish the target lane from the current lane, in m.
The method for calculating the distance lane change end point length chip of the target vehicle comprises the following steps:
Figure BDA0002840280350000091
wherein d is0The length of a target vehicle from a lane change forced terminal when a lane change requirement is generated is m; dFLThe length of the target vehicle from the lane change forced terminal in the lane change clearance selection process is m. In an urban road, a lane change forced terminal point is the starting point of a solid line drawn by a forbidden lane change before an intersection; in the exit ramp section of the expressway, the forced lane change end point refers to the starting point of the parallel lane of the exit ramp; the other cases default to 1000 m.
The method for calculating the steering lamp chips of the target vehicle comprises the following steps:
Figure BDA0002840280350000092
(4) and calculating a game coefficient, and determining the recommendation level and recommendation degree of the lane change gap selection of the target vehicle according to the game coefficient.
Specifically, the method for calculating the game coefficient comprises the following steps:
Figure BDA0002840280350000093
wherein P is a game coefficient.
Specifically, the recommended level and the recommended degree of the ramp gap selection are classified into six levels according to the following rules:
game coefficient P Recommending a rating Degree of recommendation
[0，0.5) Class VI Is not recommended very much
[0.5，1) Class V Is not recommended
[1，2) Grade IV General recommendations
[2，5) Class III Recommending
[5，10) Class II Moderate recommendations
[10，+∞) Class I Very recommended
The invention is further elucidated below on the basis of a traffic example.
Traffic example: the target vehicle runs on a one-way three-lane road, and the road speed is limited vmax90km/h, lane width Lw3.75 m. The traffic situation is as shown in fig. 2, the 2 nd gap selection is currently performed, that is, the gap sorting chip N is selected to be 2, and the number of the vehicle behind the gap adjacent to the target lane is three.
The corresponding weights for each chip are shown in the table below.
Figure BDA0002840280350000094
Figure BDA0002840280350000101
The first embodiment is as follows:
the intelligent vehicle lane change gap selection method based on the game theory is adopted as follows:
(1) extracting all vehicle microscopic data in a research range from an existing vehicle information database, wherein the information of a target vehicle is as follows: velocity v of the target vehicles80 km/h; an included angle theta between the driving direction of the target vehicle and the lane line is 30 degrees; acceleration a of the target vehicles＝2m/s2(ii) a Length L of center of front bumper of target vehicle from lane lineCTL1 m; length d of target vehicle from forced terminal point of lane change when lane change demand is generated01000 m; length d of target vehicle from forced lane change terminal in vehicle lane change gap selection processFL300 m; the turn signal lamp of the subject vehicle is turned on. The information of the vehicle (number three) behind the adjacent gap of the target lane is as follows: speed v of vehicle behind adjacent gap of target lanef70 km/h; acceleration a of vehicle after adjacent gap of target lanef＝-4m/s2(ii) a The distance L between the rear vehicle and the front vehicle in the adjacent gap of the target lane is 250 m; warning actions such as flash lamp flashing or whistling do not occur on the rear vehicle in the adjacent gap of the target lane; and the steering lamp of the vehicle is not lighted after the adjacent gap of the target lane.
(2) Counter for calculating adjacent gap of target lane
Cv＝vf＝70
Ca＝af 2Θ(-af)＝-16
Figure BDA0002840280350000102
CLS＝0
CTL＝0
Figure BDA0002840280350000111
(3) Counting chips of a target vehicle
Figure BDA0002840280350000112
Figure BDA0002840280350000113
Figure BDA0002840280350000114
Figure BDA0002840280350000115
Figure BDA0002840280350000116
Figure BDA0002840280350000117
Figure BDA0002840280350000118
FTL＝1
Figure BDA0002840280350000119
(4) And calculating a game coefficient, and determining the recommendation level and recommendation degree of the lane change gap selection of the target vehicle according to the game coefficient.
Coefficient of game play
Figure BDA00028402803500001110
Refer to the following table:
Figure BDA00028402803500001111
Figure BDA0002840280350000121
because the game coefficient is 4.03 and the recommended grade of the lane changing gap is level III, the intelligent vehicle is recommended to select the current gap to change lanes.
As shown in fig. 3, an intelligent vehicle lane change gap selection device disclosed in the embodiment of the present invention based on a game theory includes: the intelligent vehicle lane change clearance selection recommendation system comprises an intelligent vehicle state sensing module, a data storage module, a game coefficient calculation module and an intelligent vehicle lane change clearance selection recommendation module; the intelligent vehicle state sensing module is used for acquiring gap selection data of all vehicles in a research range, wherein the gap selection data comprises speed data, acceleration data, position data, head deflection angle data, steering lamp state data of a target vehicle and speed data, acceleration data, position data, warning action data and steering lamp state data of a vehicle behind a gap adjacent to a target lane; the data storage module is used for storing historical and real-time gap selection data; the game coefficient calculation module is used for calculating chips of the vehicles behind the adjacent gaps of the target lanes, chips of the target vehicles and game coefficients; and the intelligent vehicle lane change gap selection recommendation module is used for determining the recommendation level and recommendation degree of the target vehicle lane change gap selection.
Wherein, intelligent car state perception module includes: the self-sensing unit for the vehicle and the sensing unit for the rear vehicle in the adjacent gap of the target lane are used for acquiring speed data, acceleration data, position data, head deflection angle data, steering lamp state data of the target vehicle and speed data, acceleration data, position data, warning action data and steering lamp state data of the rear vehicle in the adjacent gap of the target lane; the data storage module includes: the historical data unit and the real-time data unit are used for storing historical and real-time gap selection data; the game coefficient calculation module comprises: the chip calculating unit of the vehicle behind the adjacent gap of the target lane, the chip calculating unit of the target vehicle and the game coefficient calculating unit are used for calculating chips of the vehicle behind the adjacent gap of the target lane, chips of the target vehicle and game coefficients; and the intelligent vehicle lane change gap selection recommendation module is used for determining the recommendation level and recommendation degree of the target vehicle lane change gap selection.
The embodiment of the intelligent vehicle lane change gap selection device based on the game theory and the embodiment of the intelligent vehicle lane change gap selection method based on the game theory belong to the same concept, and specific implementation processes are detailed in the embodiment of the method and are not repeated herein.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. An intelligent vehicle lane change gap selection method based on a game theory is characterized by comprising the following steps: the method comprises the following steps:
step 1, obtaining clearance selection data of a target vehicle; the gap selection data are target vehicle data and target lane adjacent gap rear vehicle data
The acquired data of the rear vehicle in the adjacent gap of the target lane is as follows: speed data, acceleration data, position data, warning action data and steering lamp state data;
the acquired target vehicle data are: speed data, acceleration data, position data, head deflection angle data and steering lamp state data;
step 2, calculating and obtaining speed chips, acceleration chips, car following distance chips, warning chips and steering lamp chips of the vehicles behind the adjacent gaps of the target lane according to the speed data, the acceleration data, the position data, the warning action data and the steering lamp state data of the vehicles behind the adjacent gaps of the target lane; calculating the chips of the vehicles behind the adjacent gaps of the target lane by using the speed chips, the acceleration chips, the distance chips between the vehicles, the warning chips and the steering lamp chips of the vehicles behind the adjacent gaps of the target lane;
the expression of step 3 is: calculating longitudinal speed chips, longitudinal acceleration chips, transverse speed chips, transverse acceleration chips, nose deflection angle chips, distance lane line length chips, distance lane changing end point length chips, gap sorting chips and steering lamp chips of the target vehicle according to the speed data, acceleration data, position data, nose deflection angle data and steering lamp state data of the target vehicle; calculating the chips of the target vehicle by using the chips of the longitudinal speed, the longitudinal acceleration, the transverse speed, the transverse acceleration, the head deflection angle, the distance lane line, the distance lane changing end point, the gap sequencing chips and the steering lamp chips of the target vehicle;
and 4, calculating a game coefficient through chips of the vehicles behind the adjacent gaps of the target lanes and chips of the target vehicles, and obtaining the recommended grade and the recommended degree of the target vehicle lane changing gap selection according to the game coefficient.
2. The intelligent vehicle lane change gap selection method based on the game theory as claimed in claim 1, wherein: the position data of the rear vehicle in the adjacent gap of the target lane in the step 1 comprises the distance between the rear vehicle and the front vehicle in the adjacent gap of the target lane;
the warning action data of the vehicle behind the adjacent gap of the target lane comprises the following data: the flash lamp of the vehicle flashes after the adjacent gap of the target lane and the whistle condition of the vehicle after the adjacent gap of the target lane, and the position data of the target vehicle comprises: the length of the center of the front bumper of the target vehicle from the lane line, the length of the target vehicle from the forced lane changing terminal point and the current gap selection times,
the nose deflection angle data of the target vehicle is as follows: and the included angle between the driving direction of the target vehicle and the lane line.
3. The intelligent vehicle lane change gap selection method based on the game theory as claimed in claim 1, wherein:
the method for calculating chips of the vehicle behind the adjacent gap of the target lane in the step 2 comprises the following steps:
Figure FDA0002840280340000021
wherein, CfChips of the rear vehicle in the adjacent gap of the target lane; cv，Ca，CL，CLS，CTLRespectively including speed chips, acceleration chips, distance chips, warning chips and steering lamp chips of the rear vehicle in the adjacent gaps of the target lane; alpha is alpha1，α2，α3，α4，α5The weights are corresponding to the chips respectively.
4. The intelligent vehicle lane-changing gap selection method based on game theory as claimed in claim 3, wherein the speed chips C of the vehicles behind the adjacent gap of the target lane in the step 2vThe calculation method comprises the following steps:
Cv＝vf
wherein v isfThe speed of the vehicle behind the adjacent gap of the target lane is obtained;
acceleration chip C of vehicle behind adjacent gap of target laneaThe calculation method comprises the following steps:
Ca＝af 2Θ(-af)
wherein, afAcceleration of vehicle after adjacent gap of target lane，
Figure FDA0002840280340000022
The method for calculating the distance between the following vehicles of the vehicles behind the adjacent gaps of the target lane comprises the following steps:
Figure FDA0002840280340000023
wherein L is the distance between the rear vehicle and the front vehicle in the adjacent gap of the target lane, namely the gap size;
warning chip C of rear vehicle in adjacent gap of target laneLSThe calculation method comprises the following steps:
Figure FDA0002840280340000024
wherein the warning action comprises flashing of a flash lamp of the vehicle or a whistle;
turn light chip C of rear vehicle in adjacent gap of target laneTLThe calculation method comprises the following steps:
Figure FDA0002840280340000025
5. the intelligent lane change gap selection method based on game theory as claimed in claim 1, wherein the chip F of the target vehicle in the step 3sThe calculation method comprises the following steps:
Figure FDA0002840280340000031
wherein, FsA chip that is a target vehicle; n is the selection gap sorting chips, which represents the Nth group of games, namely the Nth gap; fZv，FZa，FHv，FHa，Fag，FCTL，FFL，FTLRespectively being a longitudinal speed chip, a longitudinal acceleration chip, a transverse speed chip, a transverse acceleration chip, a nose deflection angle chip, a distance lane line length chip, a distance lane changing end point length chip and a steering lamp chip of a target vehicle; beta is a1，β2，β3，β4，β5，β6，β7，β8The weight of the chips of the longitudinal speed, the longitudinal acceleration, the transverse speed, the transverse acceleration, the head deflection angle, the distance lane line, the distance lane changing end point and the steering lamp is respectively.
6. The intelligent lane change gap selection method based on game theory as claimed in claim 5, wherein the chip F of longitudinal speed of the target vehicle in the step 3ZvThe calculation method comprises the following steps:
FZv＝|vmax-vs·cosθ|
wherein v ismaxLimiting the speed of the road; v. ofsIs the speed of the target vehicle; theta is an included angle between the driving direction and the lane line.
Chip F for longitudinal acceleration of subject vehicleZaThe calculation method comprises the following steps:
FZa＝(as·cosθ)2
wherein, asIs the acceleration of the subject vehicle,
chip F for lateral velocity of target vehicleHvThe calculation method comprises the following steps:
FHv＝vs·sinθ
chip F for lateral acceleration of subject vehicleHaThe calculation method comprises the following steps:
FHa＝(as·sinθ)2
the method for calculating the nose deflection angle chips of the target vehicle comprises the following steps:
Figure FDA0002840280340000032
distance lane line length chip F for target vehicleCTLThe calculation method comprises the following steps:
Figure FDA0002840280340000033
wherein L iswIs the lane width; l isCTLThe length of the center of a front bumper of a target vehicle from a lane line, wherein the lane line is used for distinguishing a target lane from a current lane;
distance lane change end point length chip F for target vehicleFLThe calculation method comprises the following steps:
Figure FDA0002840280340000041
wherein d is0The length of the target vehicle from the lane change mandatory terminal when the lane change requirement is generated; dFLSelecting the length of the target vehicle from a lane change forced terminal in the lane change gap selection process for the vehicle;
the method for calculating the steering lamp chips of the target vehicle comprises the following steps:
Figure FDA0002840280340000042
7. the intelligent vehicle lane change gap selection method based on the game theory as claimed in claim 1, wherein the method for calculating the game coefficient in the step 4 comprises the following steps:
Figure FDA0002840280340000043
wherein P is game coefficient, CfChips of vehicles behind adjacent gaps of the target lane, FsBeing the chips of the target vehicle.
8. The intelligent vehicle lane change gap selection method based on the game theory as claimed in claim 1, wherein in the step 4, the gap selection of the target vehicle ramp is obtained according to the game coefficient and is divided into a recommendation level and a recommendation degree; the recommendation degree is classified into I level, II level, III level, IV level, V level and VI level, and the corresponding recommendation grades are classified into extraordinary recommendation, moderate recommendation, general recommendation, non-recommendation and extremely non-recommendation:
when the game coefficient P range is [0, 0.5), the recommendation level is VI level, and the recommendation degree is not recommended; when the game coefficient P range is [0.5, 1), the recommendation level is V level, and the recommendation degree is not recommended; when the game coefficient P range is [1, 2), the recommendation level is IV level, and the recommendation degree is general recommendation; when the game coefficient P range is [2, 5), the recommendation level is grade III, and the recommendation degree is recommendation; when the game coefficient P range is [5, 10), the recommendation level is II level, and the recommendation degree is medium recommendation; when the game coefficient P is in the range of [10, + ∞ ], the recommendation level is level I, and the recommendation level is very recommended.
9. An intelligent vehicle lane change gap selection device based on a game theory is characterized by comprising an intelligent vehicle state sensing module, a data storage module, a game coefficient calculation module and an intelligent vehicle lane change gap selection module; the intelligent vehicle state sensing module is used for acquiring target vehicle data and data of a vehicle behind a target lane adjacent gap and transmitting the data to the data storage module, the data storage module processes the data and transmits the data to the game coefficient calculation module for calculation, and the intelligent vehicle lane change gap selection module performs actions according to calculation results of the game coefficient calculation module.
10. The intelligent vehicle lane-changing gap selection device based on the game theory as claimed in claim 9, wherein the intelligent vehicle state sensing module comprises a self-sensing unit for the vehicle and a sensing unit for the vehicle after the adjacent gap of the target lane; the self-perception unit for the vehicle and the perception unit for the vehicle behind the adjacent gap of the target lane; the system is used for respectively acquiring speed data, acceleration data, position data, head deflection angle data, steering lamp state data of a target vehicle and speed data, acceleration data, position data, warning action data and steering lamp state data of a vehicle behind an adjacent gap of a target lane;
the data storage module comprises a historical data unit and a real-time data unit; respectively for storing historical and real-time gap selection data;
and the game coefficient calculation module comprises a chip calculation unit of the rear vehicle after the adjacent gap of the target lane, a chip calculation unit of the target vehicle and a game coefficient calculation unit.
CN202011489329.0A 2020-12-16 2020-12-16 Intelligent vehicle lane change gap selection method and device based on game theory Active CN112590791B (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202011489329.0A CN112590791B (en) 2020-12-16 2020-12-16 Intelligent vehicle lane change gap selection method and device based on game theory

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202011489329.0A CN112590791B (en) 2020-12-16 2020-12-16 Intelligent vehicle lane change gap selection method and device based on game theory

Publications (2)

Publication Number Publication Date
CN112590791A true CN112590791A (en) 2021-04-02
CN112590791B CN112590791B (en) 2022-03-11

Family

ID=75196785

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202011489329.0A Active CN112590791B (en) 2020-12-16 2020-12-16 Intelligent vehicle lane change gap selection method and device based on game theory

Country Status (1)

Country Link
CN (1) CN112590791B (en)

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115384501A (en) * 2022-08-19 2022-11-25 重庆长安汽车股份有限公司 Automatic lane changing method and device for vehicle, vehicle and storage medium
CN115578865A (en) * 2022-09-28 2023-01-06 东南大学 Automatic driving vehicle convergence gap selection optimization method based on artificial intelligence

Citations (21)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011026092A1 (en) * 2009-08-31 2011-03-03 Telcordia Technologies, Inc. System and method for detecting and evicting malicious vehicles in a vehicle communications network
CN103929777A (en) * 2014-05-08 2014-07-16 西安电子科技大学 Vehicle network data distribution congestion control method based on congestion game
CN106043313A (en) * 2016-07-28 2016-10-26 南昌大学 Automobile driving lane-changing intelligent guiding device
CN106218638A (en) * 2016-08-17 2016-12-14 北方工业大学 Intelligent network-connected automobile cooperative lane change control method
US20170098372A1 (en) * 2014-05-04 2017-04-06 Roger Andre EILERTSEN A road traffic server
WO2018115963A2 (en) * 2016-12-23 2018-06-28 Mobileye Vision Technologies Ltd. Navigational system with imposed liability constraints
WO2018132608A2 (en) * 2017-01-12 2018-07-19 Mobileye Vision Technologies Ltd. Navigation based on occlusion zones
CN108387242A (en) * 2018-02-07 2018-08-10 西南交通大学 Automatic Pilot lane-change prepares and executes integrated method for planning track
CN108595823A (en) * 2018-04-20 2018-09-28 大连理工大学 A kind of computational methods of Autonomous Vehicles lane-change strategy that combining driving style and theory of games
CN109671287A (en) * 2019-01-07 2019-04-23 武汉理工大学 A kind of the intersection vehicles guidance control system and method for Acceleration Control
CN110111605A (en) * 2019-06-12 2019-08-09 吉林大学 Automatic driving vehicle entrance ring road based on dynamic game travels decision-making technique
WO2019180506A2 (en) * 2018-03-20 2019-09-26 Mobileye Vision Technologies Ltd. Systems and methods for navigating a vehicle
CN110298131A (en) * 2019-07-05 2019-10-01 西南交通大学 Automatic Pilot lane-change decision model method for building up under a kind of mixing driving environment
CN110362910A (en) * 2019-07-05 2019-10-22 西南交通大学 Automatic driving vehicle lane-change conflict coordination method for establishing model based on game theory
CN110962853A (en) * 2019-11-25 2020-04-07 浙江工业大学 Vehicle game lane change cooperation method in Internet of vehicles environment
CN111267846A (en) * 2020-02-11 2020-06-12 南京航空航天大学 Game theory-based peripheral vehicle interaction behavior prediction method
US20200269871A1 (en) * 2019-02-27 2020-08-27 Zf Automotive Germany Gmbh Method and system for determining a driving maneuver
CN111624996A (en) * 2020-05-12 2020-09-04 哈尔滨工程大学 Multi-unmanned-boat incomplete information trapping method based on game theory
CN111645692A (en) * 2020-06-02 2020-09-11 中国科学技术大学先进技术研究院 Hybrid strategy game-based driver overtaking intention identification method and system
CN111780981A (en) * 2020-05-21 2020-10-16 东南大学 Intelligent vehicle formation lane change performance evaluation method
CN111942383A (en) * 2020-07-14 2020-11-17 南京天航智能装备研究院有限公司 Electric automobile following energy-saving control system and game theory method thereof

Patent Citations (22)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
WO2011026092A1 (en) * 2009-08-31 2011-03-03 Telcordia Technologies, Inc. System and method for detecting and evicting malicious vehicles in a vehicle communications network
US20170098372A1 (en) * 2014-05-04 2017-04-06 Roger Andre EILERTSEN A road traffic server
CN103929777A (en) * 2014-05-08 2014-07-16 西安电子科技大学 Vehicle network data distribution congestion control method based on congestion game
CN106043313A (en) * 2016-07-28 2016-10-26 南昌大学 Automobile driving lane-changing intelligent guiding device
CN106218638A (en) * 2016-08-17 2016-12-14 北方工业大学 Intelligent network-connected automobile cooperative lane change control method
WO2018115963A2 (en) * 2016-12-23 2018-06-28 Mobileye Vision Technologies Ltd. Navigational system with imposed liability constraints
EP3657130A1 (en) * 2017-01-12 2020-05-27 Mobileye Vision Technologies Ltd. Navigation based on vehicle activity
WO2018132608A2 (en) * 2017-01-12 2018-07-19 Mobileye Vision Technologies Ltd. Navigation based on occlusion zones
CN108387242A (en) * 2018-02-07 2018-08-10 西南交通大学 Automatic Pilot lane-change prepares and executes integrated method for planning track
WO2019180506A2 (en) * 2018-03-20 2019-09-26 Mobileye Vision Technologies Ltd. Systems and methods for navigating a vehicle
CN108595823A (en) * 2018-04-20 2018-09-28 大连理工大学 A kind of computational methods of Autonomous Vehicles lane-change strategy that combining driving style and theory of games
CN109671287A (en) * 2019-01-07 2019-04-23 武汉理工大学 A kind of the intersection vehicles guidance control system and method for Acceleration Control
US20200269871A1 (en) * 2019-02-27 2020-08-27 Zf Automotive Germany Gmbh Method and system for determining a driving maneuver
CN110111605A (en) * 2019-06-12 2019-08-09 吉林大学 Automatic driving vehicle entrance ring road based on dynamic game travels decision-making technique
CN110362910A (en) * 2019-07-05 2019-10-22 西南交通大学 Automatic driving vehicle lane-change conflict coordination method for establishing model based on game theory
CN110298131A (en) * 2019-07-05 2019-10-01 西南交通大学 Automatic Pilot lane-change decision model method for building up under a kind of mixing driving environment
CN110962853A (en) * 2019-11-25 2020-04-07 浙江工业大学 Vehicle game lane change cooperation method in Internet of vehicles environment
CN111267846A (en) * 2020-02-11 2020-06-12 南京航空航天大学 Game theory-based peripheral vehicle interaction behavior prediction method
CN111624996A (en) * 2020-05-12 2020-09-04 哈尔滨工程大学 Multi-unmanned-boat incomplete information trapping method based on game theory
CN111780981A (en) * 2020-05-21 2020-10-16 东南大学 Intelligent vehicle formation lane change performance evaluation method
CN111645692A (en) * 2020-06-02 2020-09-11 中国科学技术大学先进技术研究院 Hybrid strategy game-based driver overtaking intention identification method and system
CN111942383A (en) * 2020-07-14 2020-11-17 南京天航智能装备研究院有限公司 Electric automobile following energy-saving control system and game theory method thereof

Non-Patent Citations (3)

* Cited by examiner, † Cited by third party
Title
NAN DING: "Multivehicle Coordinated Lane Change Strategy in the Roundabout Under Internet of Vehicles Based on Game Theory and Cognitive Computing", 《IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS》 *
曲大义等: "车辆换道交互行为分析和建模", 《公路交通科技》 *
薛春铭等: "基于博弈论的人类驾驶与无人驾驶协作换道模型", 《计算机工程》 *

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115384501A (en) * 2022-08-19 2022-11-25 重庆长安汽车股份有限公司 Automatic lane changing method and device for vehicle, vehicle and storage medium
CN115578865A (en) * 2022-09-28 2023-01-06 东南大学 Automatic driving vehicle convergence gap selection optimization method based on artificial intelligence
CN115578865B (en) * 2022-09-28 2023-08-29 东南大学 Automatic driving vehicle converging gap selection optimization method based on artificial intelligence

Also Published As

Publication number Publication date
CN112590791B (en) 2022-03-11

Similar Documents

Publication Publication Date Title
CN113291308B (en) Vehicle self-learning lane-changing decision-making system and method considering driving behavior characteristics
JP5385056B2 (en) Driving status estimation device, driving support device
CN107742432B (en) Expressway operation speed active early warning system based on vehicle-road cooperation and control method
CN109885040B (en) Vehicle driving control right distribution system in man-machine driving
JP5643386B2 (en) Driving status estimation device, driving support device
CN103221665B (en) Driving support system and driving support managing device
CN104691545B (en) Adaptability vehicle collision avoidance method
CN112590791B (en) Intelligent vehicle lane change gap selection method and device based on game theory
CN101633358A (en) Adaptive vehicle control system with integrated driving style recognition
DE112014003982T5 (en) Control unit and method for controlling a vehicle in a vehicle
CN109849785B (en) Lane change judgment system and method based on Internet of vehicles
US11661061B2 (en) Method and device for assisting a driver in a vehicle
CN112249008B (en) Unmanned automobile early warning method aiming at complex dynamic environment
CN113657716B (en) Comprehensive evaluation method for driving behavior safety of driver based on entropy weight method
JP6326968B2 (en) Driving support system and driving support method
CN108573617A (en) Drive assistance device, vehicle and its method
DE102015223481A1 (en) Method for adjusting a steering assistance of a vehicle
CN116740945B (en) Method and system for multi-vehicle collaborative grouping intersection of expressway confluence region in mixed running environment
CN115273003A (en) Traffic sign recognition and navigation decision method and system combining character positioning
CN105620486B (en) Driving mode judgment means and method applied to vehicle energy management
CN113592221B (en) Road section risk dynamic assessment method based on safety substitution evaluation index
CN113240901B (en) Intelligent vehicle lane changing behavior risk level determination method and device
CN116013078B (en) Dynamic control method for merging main line of ramp vehicles in rapid transit merging area
CN112948525B (en) High-precision dynamic map generation method, equipment and medium for intelligent networked automobile
CN113361613A (en) Method and device for classifying ramp vehicle lane change simulation models based on trajectory data

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination
GR01 Patent grant
GR01 Patent grant