CN115578865B - Automatic driving vehicle converging gap selection optimization method based on artificial intelligence - Google Patents
Automatic driving vehicle converging gap selection optimization method based on artificial intelligence Download PDFInfo
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- CN115578865B CN115578865B CN202211189386.6A CN202211189386A CN115578865B CN 115578865 B CN115578865 B CN 115578865B CN 202211189386 A CN202211189386 A CN 202211189386A CN 115578865 B CN115578865 B CN 115578865B
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/01—Detecting movement of traffic to be counted or controlled
- G08G1/052—Detecting movement of traffic to be counted or controlled with provision for determining speed or overspeed
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096708—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control
- G08G1/096725—Systems involving transmission of highway information, e.g. weather, speed limits where the received information might be used to generate an automatic action on the vehicle control where the received information generates an automatic action on the vehicle control
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/09—Arrangements for giving variable traffic instructions
- G08G1/0962—Arrangements for giving variable traffic instructions having an indicator mounted inside the vehicle, e.g. giving voice messages
- G08G1/0967—Systems involving transmission of highway information, e.g. weather, speed limits
- G08G1/096766—Systems involving transmission of highway information, e.g. weather, speed limits where the system is characterised by the origin of the information transmission
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- G—PHYSICS
- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/16—Anti-collision systems
- G08G1/167—Driving aids for lane monitoring, lane changing, e.g. blind spot detection
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- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/10—Internal combustion engine [ICE] based vehicles
- Y02T10/40—Engine management systems
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Abstract
The invention discloses an artificial intelligence-based automatic driving vehicle afflux gap selection optimization method, which specifically comprises the following steps: acquiring microscopic data of an automatic driving vehicle and a target lane vehicle; determining an automated driving vehicle afflux gap; calculating the entry safety cost of the automatic driving vehicle; calculating the entry efficiency cost of the automatic driving vehicle; the minimum total cost of entry for the autonomous vehicle is calculated to optimize the gap selection. The method comprehensively considers the safety and efficiency aspects of the automatic driving vehicle import process, so that the automatic driving vehicle import gap selection method is more scientific and efficient, the potential safety hazard caused by the import process is reduced, and the vehicle operation efficiency is improved.
Description
Technical Field
The invention relates to the technical field of intelligent traffic control, in particular to an automatic driving vehicle converging gap selection optimization method based on artificial intelligence.
Background
With the development of autopilot technology, existing autopilot vehicles can be kept reasonably spaced from the vehicle in front based on advanced sensor and information and communication technologies, such as adaptive cruise control systems and cooperative adaptive cruise control systems. The technology is applied to the lane change converging process, is an expansion application of the automatic driving technology, and has important significance for improving traffic safety, improving traffic efficiency and reducing negative effects brought by lane change converging.
The entry gap selection is the basis of the entry of the lane change, if scientific planning is lacking before the automatic driving vehicle enters the gap between vehicles, collision risk is increased easily, traffic safety accidents are caused, and therefore, the selection result of the entry gap directly influences the road safety of the entry process and is one of important influence factors of the running efficiency after the entry.
Through retrieval, chinese patent with application number 2020110305726 and application date 2020, 9 and 27 discloses a method and a device for determining an intelligent vehicle forced lane change sink. A method for controlling the vehicles on the down ramp from the intelligent network connection special road disclosed in China patent publication No. 2021110086935 and No. 2021, 8 and 31 mainly improves the road changing efficiency of the down ramp vehicles by collecting road traffic flow information and combining the attribute characteristics and traffic flow characteristics of multiple types of lanes.
In general, existing studies lack consideration of automated vehicle lane change entry gap selection, ignoring the impact of gap selection on entry process safety, and post-entry operating efficiency.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides an automatic driving vehicle afflux gap selection optimization method based on artificial intelligence, which takes the speed, the acceleration and the longitudinal gaps of an automatic driving vehicle and a target lane vehicle as basic information, screens the afflux gap of the automatic driving vehicle, calculates the afflux safety cost and the efficiency cost of the automatic driving vehicle, optimizes the gap selection by calculating the minimum afflux total cost of the automatic driving vehicle, provides decision basis for the channel exchange afflux of the automatic driving vehicle, and improves the safety of the channel exchange afflux process and the passing efficiency after the afflux.
In order to solve the technical problems, the invention adopts the following technical scheme:
an automatic driving vehicle afflux gap selection optimization method based on artificial intelligence comprises the following steps:
step 1, acquiring microscopic data of an automatic driving vehicle and a target lane vehicle; microscopic data includes vehicle speed, acceleration and adjacent vehicle longitudinal clearance;
step 2, determining an automatic driving vehicle afflux gap through microscopic data calculation, and comparing the afflux gap with an afflux gap standard value zeta to judge whether the afflux gap critical condition is met; if the afflux gap is smaller than the afflux gap standard value zeta, the gap cannot be afflux; if the afflux gap is larger than or equal to the afflux gap standard value zeta, the gap is an afflux gap;
step 3, calculating the automatic driving vehicle remittance safety cost through the acquired microscopic data;
step 4, calculating the automatic driving vehicle import efficiency cost through the acquired microscopic data;
step 5, calculating the total cost of the automatic driving vehicle according to the safety cost and the efficiency cost of the automatic driving vehicle; all the total costs are screened and compared to obtain the minimum total cost of the gap that can be imported and the vehicle is imported into the gap.
Further preferably, in step 2, the method for determining the threshold condition of the afferent clearance of the autonomous vehicle is as follows:
screening a gap on a target lane, wherein the longitudinal distance between the gap and an automatic driving vehicle is not more than 300m, so that the gap meets the following requirementsIs a affluxable gap;
wherein s is n The length of the nth gap is m;the speeds of the front car and the rear car in the nth gap are respectively expressed in m/s; />The expected acceleration of the front car and the rear car respectively representing the nth gap is expressed as m/s 2 The method comprises the steps of carrying out a first treatment on the surface of the Zeta is the standard value of the afflux gap, and is 2.5.
It is further preferred that the composition comprises,based on a random forest algorithm, taking the vehicle acceleration value of the first 10 seconds and sampling every 0.1 second, taking 100 obtained sample values as input, and outputting average acceleration, namely expected acceleration, in the future 5 seconds.
Further preferably, in step 3, the method for calculating the entry safety cost of the automated driving vehicle is as follows:
in the method, in the process of the invention,representing the security cost of importing an ith importable gap; s is(s) i The unit is m for the length of the ith afflux gap; />The speeds of the front vehicle and the rear vehicle which can be converged into the gap in the ith unit of m/s are respectively shown; v s Representing the speed of the autonomous vehicle in m/s; />The expected acceleration of the i-th front vehicle and the expected acceleration of the rear vehicle which can be converged into the gap are respectively expressed as m/s 2 ；
δ 1 ,δ 2 ,δ 3 Weighting factors, delta, respectively representing gap safety cost, rear vehicle safety cost and front vehicle safety cost 1 ,δ 2 ,δ 3 Taking 0.5, 0.25 and 0.25 respectively; alpha, beta 1 ,β 2 Is the gap safety cost, the rear vehicle safety cost and the front vehicle safety costThe obtained constant coefficients, alpha, beta 1 ,β 2 Taking 1, 1 and 1 respectively.
Further preferably, in step 4, the method for calculating the entry efficiency cost of the automated driving vehicle is as follows:
in the method, in the process of the invention,representing the cost of efficiency of the sink into the ith importable gap; />x i Representing the longitudinal relative distance of the autonomous vehicle from the midpoint of the i-th importable gap, x when the gap is forward of the direction of travel of the autonomous vehicle i Take positive value, otherwise take negative value; lambda (lambda) 1 ,λ 2 The weight factors of the relative speed efficiency cost and the gap relative position efficiency cost of the front vehicle are respectively represented, and xi is a constant coefficient obtained through experiments in the gap relative position efficiency cost; lambda (lambda) 1 ,λ 1 And xi is 0.6, 0.4 and 10 respectively.
Further preferably, in step 5, the method for calculating the lowest total cost of the automated guided vehicle is as follows:
L＝min(L 1 ,L 2 ,...,L i ) The method comprises the steps of carrying out a first treatment on the surface of the In the method, in the process of the invention,L i represents the total cost, ω, of converging into the ith afflux gap a ,ω x Weight factors representing the entry security cost and entry efficiency cost, respectively; omega a ,ω x Taking 0.7 and 0.3 respectively.
The invention has the following beneficial effects: the invention discloses an automatic driving vehicle afflux gap selection optimization method based on artificial intelligence, which is used for determining an afflux gap of an automatic driving vehicle based on the acquired microscopic data of the automatic driving vehicle and a target lane vehicle, calculating the safety cost and the efficiency cost of the afflux of the automatic driving vehicle into each afflux gap, calculating the lowest afflux total cost of the automatic driving vehicle, and optimizing the afflux gap selection of the automatic driving vehicle. The method provided by the invention comprehensively considers the operation characteristics of the vehicles entering the vehicle and the vehicles on the target lane within a certain range, increases the reliability of the automatic driving vehicle entering gap selection, and improves the road safety and the passing efficiency.
Drawings
FIG. 1 is a flow chart of an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention more clear, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. It will be apparent that the described embodiments are some, but not all, embodiments of the invention. All other embodiments, which can be made by a person skilled in the art without creative efforts, based on the described embodiments of the present invention fall within the protection scope of the present invention.
Unless defined otherwise, technical or scientific terms used herein should be given the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs.
The invention will be described in further detail with reference to the accompanying drawings and specific preferred embodiments.
As shown in FIG. 1, in the target lane where the autonomous vehicle is attempting to merge, there are 3 gaps available within 300m of the longitudinal distance, and the speed of the autonomous vehicle is known to be 15m/s for both the front and rear of the 3 gaps and 12m/s. Based on the sensor and communication technology, the respective gap lengths are shown in table 1, acceleration data within each vehicle 10s is collected, predicted using a random forest method, and the expected accelerations of the vehicle before and after the gap are also shown in table 1.
| Gap numbering | Expected acceleration of front vehicle | Expected acceleration of rear vehicle | Gap length | Longitudinal distance |
| 1 | 2m/s 2 | -2m/s 2 | 30m | 50m |
| 2 | -2m/s 2 | -4m/s 2 | 40m | -10m |
| 3 | -4m/s 2 | 0m/s 2 | 45m | -40m |
TABLE 1 microscopic data for autonomous vehicles and target lane vehicles
The invention provides an artificial intelligence-based automatic driving vehicle afflux gap selection optimization method.
And step 1, acquiring microscopic data of the automatic driving vehicle and the target lane vehicle, wherein the microscopic data comprise vehicle speed, acceleration and longitudinal gaps of adjacent vehicles as shown in table 1.
Step 2, determining an automatic driving vehicle entering gap through microscopic data calculation, wherein the numerical values of the three gaps are respectively as follows:
comparing the afflux gap with an afflux gap standard value zeta to judge whether the afflux gap critical condition is met; if the afflux gap is smaller than the afflux gap standard value zeta, the gap cannot be afflux; if the entry gap is equal to or greater than the entry gap criterion ζ, the gap is an entry gap.
As can be seen from the above calculation results, the gap 1 is not allowed to be entered, and the gaps 2 and 3 are allowed to be entered.
Step 3, calculating the entry safety cost of the automatic driving vehicle through the acquired microscopic data, wherein the entry safety cost of the entry gaps 2 and 3 are respectively as follows:
step 4, calculating the cost of the afflux efficiency of the automatic driving vehicle through the acquired microscopic data, wherein the cost of the afflux efficiency of the afflux gaps 2 and 3 is respectively as follows:
step 5, calculating the total cost of the automatic driving vehicle according to the safety cost and the efficiency cost of the automatic driving vehicle; the total costs of the importation into the gaps 2, 3 are respectively:
all the total costs are screened and compared to obtain the minimum total cost of the gap that can be imported and the vehicle is imported into the gap. L=min (L 2 ,L 3 )＝L 2 Since the cost of the importable gap 2 is lower than the cost of the importable gap 3, the importable gap 2 should be selected to sink.
The preferred embodiments of the present invention have been described in detail above, but the present invention is not limited to the specific details of the above embodiments, and various equivalent changes can be made to the technical solution of the present invention within the scope of the technical concept of the present invention, and all the equivalent changes belong to the protection scope of the present invention.
Claims (1)
1. An automatic driving vehicle converging gap selection optimization method based on artificial intelligence is characterized by comprising the following steps of: the method comprises the following steps:
step 1, acquiring microscopic data of an automatic driving vehicle and a target lane vehicle; microscopic data includes vehicle speed, acceleration and adjacent vehicle longitudinal clearance;
step 2, determining an automatic driving vehicle afflux gap through microscopic data calculation, and comparing the afflux gap with an afflux gap standard value zeta to judge whether the afflux gap critical condition is met; if the afflux gap is smaller than the afflux gap standard value zeta, the gap cannot be afflux; if the afflux gap is larger than or equal to the afflux gap standard value zeta, the gap is an afflux gap;
the method for determining the critical condition of the afferent clearance of the automatic driving vehicle comprises the following steps:
screening a gap on a target lane, wherein the longitudinal distance between the gap and an automatic driving vehicle is not more than 300m, so that the gap meets the following requirementsIs a affluxable gap;
wherein s is n The length of the nth gap is m;the speeds of the front car and the rear car in the nth gap are respectively expressed in m/s; />The expected acceleration of the front car and the rear car respectively representing the nth gap is expressed as m/s 2 ；/>Based on a random forest algorithm, taking the vehicle acceleration value of the first 10 seconds and sampling every 0.1 second, taking 100 obtained sample values as input, and outputting average acceleration, namely expected acceleration, in the future 5 seconds; zeta is the standard value of the afflux gap, and is 2.5;
step 3, calculating the automatic driving vehicle import safety cost through the acquired microscopic data, wherein the automatic driving vehicle import safety cost calculating method comprises the following steps:
in the method, in the process of the invention,safety representing pooling of ith poolable gapCost; s is(s) i The unit is m for the length of the ith afflux gap; />The speeds of the front vehicle and the rear vehicle which can be converged into the gap in the ith unit of m/s are respectively shown; v s Representing the speed of the autonomous vehicle in m/s; />The expected acceleration of the i-th front vehicle and the expected acceleration of the rear vehicle which can be converged into the gap are respectively expressed as m/s 2 ；
δ 1 ,δ 2 ,δ 3 Weighting factors, delta, respectively representing gap safety cost, rear vehicle safety cost and front vehicle safety cost 1 ,δ 2 ,δ 3 Taking 0.5, 0.25 and 0.25 respectively; alpha, beta 1 ,β 2 Is the constant coefficient, alpha, beta obtained by experiments in the clearance safety cost, the rear vehicle safety cost and the front vehicle safety cost 1 ,β 2 Taking 1, 1 and 1 respectively;
and 4, calculating the automatic driving vehicle import efficiency cost through the acquired microscopic data, wherein the automatic driving vehicle import efficiency cost calculating method comprises the following steps:
in the method, in the process of the invention,representing the cost of efficiency of the sink into the ith importable gap; />x i Representing the longitudinal relative distance between the autonomous vehicle and the midpoint of the ith afferent gapWhen the gap is located in front of the running direction of the automatic driving vehicle x i Take positive value, otherwise take negative value; lambda (lambda) 1 ,λ 2 The weight factors of the relative speed efficiency cost and the gap relative position efficiency cost of the front vehicle are respectively represented, and xi is a constant coefficient obtained through experiments in the gap relative position efficiency cost; lambda (lambda) 1 ,λ 1 Respectively taking 0.6, 0.4 and 10 of xi;
step 5, calculating the total cost of the automatic driving vehicle according to the safety cost and the efficiency cost of the automatic driving vehicle, wherein the calculation method of the minimum total cost of the automatic driving vehicle is as follows:
L＝min(L 1 ,L 2 ,...,L i )；
in the method, in the process of the invention,L i represents the total cost, ω, of converging into the ith afflux gap a ,ω x Weight factors representing the entry security cost and entry efficiency cost, respectively; omega a ,ω x Taking 0.7 and 0.3 respectively;
screening and comparing all the total cost, obtaining an importable gap with the lowest total cost and converging the vehicles into the gap.
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