CN115578865A - Automatic driving vehicle convergence gap selection optimization method based on artificial intelligence - Google Patents

Automatic driving vehicle convergence gap selection optimization method based on artificial intelligence Download PDF

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CN115578865A
CN115578865A CN202211189386.6A CN202211189386A CN115578865A CN 115578865 A CN115578865 A CN 115578865A CN 202211189386 A CN202211189386 A CN 202211189386A CN 115578865 A CN115578865 A CN 115578865A
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gap
cost
automatic driving
clearance
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CN115578865B (en
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王昊
刘云杰
董长印
陈雨佳
吕科赟
尹芳至
熊卓智
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Southeast University
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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
    • 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
    • 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
    • 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
    • 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
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Abstract

The invention discloses an automatic driving vehicle convergence gap selection optimization method based on artificial intelligence, which specifically comprises the following steps: acquiring microscopic data of an automatic driving vehicle and a target lane vehicle; determining that an autonomous vehicle can merge into the gap; calculating the safe cost of the automatic driving vehicle to enter; calculating the automatic driving vehicle influx efficiency cost; the lowest total cost of entry for the autonomous vehicle is calculated to optimize the gap selection. The method provided by the invention 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 brought by the import process is further reduced, and the vehicle operation efficiency is improved.

Description

Automatic driving vehicle convergence gap selection optimization method based on artificial intelligence
Technical Field
The invention relates to the technical field of intelligent traffic control, in particular to an automatic driving vehicle convergence gap selection optimization method based on artificial intelligence.
Background
With the development of autonomous driving technology, existing autonomous vehicles can maintain a reasonable distance from the vehicle in front based on advanced sensors and information and communication technologies, such as adaptive cruise control systems and coordinated adaptive cruise control systems. The technology is applied to the lane changing and merging process, is an expanded application of the automatic driving technology, and has important significance for improving traffic safety, improving traffic efficiency and reducing negative effects brought by lane changing and merging.
The selection of the merging gap is the basis of road changing and merging, and if scientific planning is lacked before the automatic driving vehicle merges into the gap between the vehicles, collision risks are easily increased, and traffic safety accidents are caused, so that the selection result of the merging gap directly influences the road safety in the merging process, and is also one of important influence factors of the operation efficiency after merging.
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 merge point. A motorcade control method for a vehicle on a lower ramp to drive away from an intelligent internet dedicated road disclosed in Chinese patent application No. 2021110086935, application No. 2021, 8 and 31 is mainly used for improving the lane changing efficiency of the vehicle on the lower ramp by collecting road traffic flow information and combining the attribute characteristics and traffic flow characteristics of multiple types of lanes.
Generally speaking, the existing researches lack consideration on the selection of the lane changing and merging gaps of the automatic driving vehicle, and influence of the gap selection on the safety of the merging process and the operation efficiency after merging is neglected.
Disclosure of Invention
The invention aims to solve the technical problems of the prior art and provides an automatic driving vehicle merging gap selection optimization method based on artificial intelligence, which takes the speed, the acceleration and the longitudinal gap of adjacent vehicles of an automatic driving vehicle and a target lane as basic information, screens the automatic driving vehicle merging gap, calculates the automatic driving vehicle merging safety cost and efficiency cost, provides decision basis for the lane changing merging of the automatic driving vehicle by calculating the minimum merging total cost optimization gap selection of the automatic driving vehicle, and improves the safety of the lane changing merging process and the passing efficiency after merging.
In order to solve the technical problems, the invention adopts the technical scheme that:
an automated vehicle entry 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; the microscopic data includes vehicle speed, acceleration and adjacent vehicle longitudinal clearance;
step 2, determining the influx gap of the automatic driving vehicle through microscopic data calculation, and comparing the influx gap with the influx gap standard value zeta to judge whether the influx gap critical condition is met; if the input clearance is smaller than the input clearance standard value zeta, the clearance can not be input; if the influx clearance is more than or equal to the influx clearance standard value zeta, the clearance is a influx clearance;
step 3, calculating the safe cost of the automatic driving vehicle import through the acquired microscopic data;
step 4, calculating the import efficiency cost of the automatic driving vehicle through the acquired microscopic data;
step 5, calculating the total cost of the automatic driving vehicle for the import according to the safe cost and the efficiency cost of the automatic driving vehicle for the import; and screening and comparing all the total cost of the vehicles to obtain the incorporatable clearance with the lowest total cost and integrating the vehicles into the clearance.
Further preferably, in step 2, the method for determining the critical condition of the merge-in gap of the automatic driving vehicle comprises:
the longitudinal distance between the screening target lane and the automatic driving vehicle is not more than 300m, and the requirement of the longitudinal distance between the screening target lane and the automatic driving vehicle is met
Figure BDA0003868729600000021
Can be merged into the gap;
in the formula s n Is the length of the nth gap in m;
Figure BDA0003868729600000022
respectively representing the speed of the front vehicle and the rear vehicle of the nth gap, and the unit is m/s;
Figure BDA0003868729600000023
respectively representing the expected accelerations of the front and rear vehicles of the nth gap in m/s 2 (ii) a Zeta is the value of the pooled gap standard, and 2.5 is taken.
It is further preferred that the first and second liquid crystal compositions,
Figure BDA0003868729600000024
based on a random forest algorithm, sampling the vehicle acceleration value of the previous 10 seconds every 0.1 second to obtain 100 sample values as input, and outputting the average acceleration in the future 5 seconds, namely the expected acceleration.
Further preferably, in step 3, the calculation method of the safe cost of importing the automatic driving vehicle comprises:
Figure BDA0003868729600000025
in the formula (I), the compound is shown in the specification,
Figure BDA0003868729600000026
representing a safe cost of importing the ith importable gap; s is i Is the length of the ith convergence gap in m;
Figure BDA0003868729600000027
respectively representing the speeds of the ith front vehicle and the ith rear vehicle which can converge into the gap, and the unit is m/s; v. of s Represents the speed of the autonomous vehicle in m/s;
Figure BDA0003868729600000028
respectively represents the expected acceleration of the ith vehicle capable of merging into the gap and the rear vehicle, and the unit is m/s 2 ；
Figure BDA0003868729600000029
δ 1 ,δ 2 ,δ 3 Weight factors, δ, representing the clearance safety cost, rear vehicle safety cost and front vehicle safety cost, respectively 1 ,δ 2 ,δ 3 Respectively taking 0.5, 0.25 and 0.25; alpha, beta 1 ,β 2 The constant coefficients alpha, beta are obtained through experiments in the clearance safety cost, the rear vehicle safety cost and the front vehicle safety cost 1 ,β 2 Respectively taking 1, 1 and 1.
Further preferably, in step 4, the method for calculating the import efficiency cost of the autonomous vehicle includes:
Figure BDA0003868729600000031
in the formula (I), the compound is shown in the specification,
Figure BDA0003868729600000032
representing the cost of efficiency of importing the ith importable gap;
Figure BDA0003868729600000033
x i representing the longitudinal relative distance of the autonomous vehicle from the ith convergence gap midpoint, x, when the gap is forward of the direction of travel of the autonomous vehicle i Taking a positive value, and taking a negative value otherwise; lambda [ alpha ] 1 ,λ 2 Weighting factors respectively representing the efficiency cost of the relative speed of the front vehicle and the efficiency cost of the relative position of the clearance, and xi is a constant coefficient obtained through experiments in the efficiency cost of the relative position of the clearance; lambda [ alpha ] 1 ,λ 1 And xi are respectively 0.6, 0.4 and 10.
Further preferably, in step 5, the method for calculating the minimum total cost of import of the autonomous vehicle comprises:
L＝min(L 1 ,L 2 ,...,L i ) (ii) a In the formula (I), the compound is shown in the specification,
Figure BDA0003868729600000034
L i represents the total cost, ω, of sinking into the ith sinking gap a ,ω x Weighting factors representing the cost of the import security and the cost of the import efficiency respectively; omega a ,ω x Respectively taking 0.7 and 0.3.
The invention has the following beneficial effects: the invention discloses an automatic driving vehicle convergence gap selection optimization method based on artificial intelligence. The method provided by the invention comprehensively considers the running characteristics of the vehicles converging into the vehicle and the target lane within a certain range, increases the reliability of converging gap selection of the automatic driving vehicle, and improves the road safety and the traffic efficiency.
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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 clearer, the technical solutions 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.
Unless defined otherwise, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this invention belongs.
The present invention will be described in further detail with reference to the accompanying drawings and specific preferred embodiments.
As shown in fig. 1, 3 gaps are available for selection in a target lane into which an autonomous vehicle attempts to merge, and it is known that the vehicle speed is 15m/s before and after the 3 gaps, and the autonomous vehicle speed is 12m/s, within a range of a longitudinal distance of 300 m. Based on sensor and communication technology, the lengths of the gaps are shown in table 1, acceleration data in 10s of each vehicle is collected, a random forest method is used for prediction, and the expected acceleration of the vehicle before and after the gaps is also shown in table 1.
Gap numbering Expected acceleration of front vehicle Expected acceleration of rear vehicle Length of gap 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 and target lane vehicles
The automatic driving vehicle convergence gap selection optimization method based on artificial intelligence provided by the invention is adopted in the following.
Step 1, obtaining microscopic data of the automatic driving vehicle and the target lane vehicle, wherein the microscopic data comprises vehicle speed, acceleration and longitudinal clearance of adjacent vehicles as shown in table 1.
Step 2, determining the automatic driving vehicle convergence gap through microscopic data calculation, wherein the numerical values of the three gaps are respectively as follows:
Figure BDA0003868729600000041
Figure BDA0003868729600000042
Figure BDA0003868729600000043
comparing the afflux clearance with the afflux clearance standard value zeta to judge whether the afflux clearance critical condition is satisfied; if the input clearance is smaller than the input clearance standard value zeta, the clearance can not be input; if the influx clearance is larger than or equal to the influx clearance standard value zeta, the clearance is the influx clearance.
From the above calculation results, it is understood that the gap 1 cannot be merged, and the gaps 2 and 3 are merged gaps.
And 3, calculating the safe cost of the automatic driving vehicle for importing according to the acquired microscopic data, wherein the safe cost of importing into the gaps 2 and 3 is respectively as follows:
Figure BDA0003868729600000044
Figure BDA0003868729600000051
step 4, calculating the influx efficiency cost of the automatic driving vehicle through the acquired microscopic data, wherein the influx efficiency cost of the automatic driving vehicle which can be imported into the gaps 2 and 3 is respectively as follows:
Figure BDA0003868729600000052
Figure BDA0003868729600000053
step 5, calculating the total cost of the automatic driving vehicle for the import according to the safe cost and the efficiency cost of the automatic driving vehicle for the import; the total incorporations into the gaps 2 and 3 are respectively:
Figure BDA0003868729600000054
Figure BDA0003868729600000055
and screening and comparing all the total cost of the vehicles to obtain the incorporatable clearance with the lowest total cost and integrating the vehicles into the clearance. L = min (L) 2 ,L 3 )＝L 2 Since the cost of the incorporable gap 2 is lower than the cost of the incorporable gap 3, the incorporable gap 2 should be selected to be incorporable.
Although the preferred embodiments of the present invention have been described in detail, the present invention is not limited to the details of the embodiments, and various equivalent changes may be made within the technical spirit of the present invention, and the technical scope of the present invention is also covered by the present invention.

Claims (6)

1. An automatic driving vehicle convergence gap selection optimization method based on artificial intelligence is characterized by comprising the following steps: the method comprises the following steps:
step 1, acquiring microscopic data of an automatic driving vehicle and a target lane vehicle; the microscopic data includes vehicle speed, acceleration and adjacent vehicle longitudinal clearance;
step 2, determining the influx gap of the automatic driving vehicle through microscopic data calculation, and comparing the influx gap with the influx gap standard value zeta to judge whether the influx gap critical condition is met; if the input clearance is smaller than the input clearance standard value zeta, the clearance can not be input; if the influx clearance is more than or equal to the influx clearance standard value zeta, the clearance is a influx clearance;
step 3, calculating the safe cost of the automatic driving vehicle to be imported through the acquired microscopic data;
step 4, calculating the import efficiency cost of the automatic driving vehicle through the acquired microscopic data;
step 5, calculating to obtain the total import cost of the automatic driving vehicle according to the safe import cost and the efficiency import cost of the automatic driving vehicle; and screening and comparing all the total cost of the incoming vehicles to obtain the gap which can be incoming and has the lowest total cost of the incoming vehicles, and merging the vehicles into the gap.
2. The automated vehicle influx gap selection optimization method based on artificial intelligence of claim 1, wherein: in step 2, the method for determining the critical condition of the automatic driving vehicle capable of converging into the clearance comprises the following steps:
the longitudinal distance between the screening target lane and the automatic driving vehicle is not more than 300m, and the requirement of the longitudinal distance between the screening target lane and the automatic driving vehicle is met
Figure FDA0003868729590000011
Can be merged into the gap;
in the formula, s n Is the length of the nth gap in m;
Figure FDA0003868729590000012
respectively representing the speed of the front vehicle and the rear vehicle of the nth gap, and the unit is m/s;
Figure FDA0003868729590000013
respectively representing the expected accelerations of the front and rear vehicles of the nth gap in m/s 2 (ii) a Zeta is the value of the pooled gap standard, and 2.5 is taken.
3. The automated vehicle influx gap selection optimization method based on artificial intelligence of claim 2, wherein:
Figure FDA0003868729590000014
based on a random forest algorithm, sampling the vehicle acceleration value of the previous 10 seconds every 0.1 second to obtain 100 sample values as input, and outputting the average acceleration in the future 5 seconds, namely the expected acceleration.
4. The automated vehicle influx gap selection optimization method based on artificial intelligence of claim 2, wherein: in step 3, the calculation method of the automatic driving vehicle importing safety cost comprises the following steps:
Figure FDA0003868729590000015
in the formula (I), the compound is shown in the specification,
Figure FDA0003868729590000016
representing a safe cost of importing the ith importable gap; s i Is the length of the ith convergence gap in m;
Figure FDA0003868729590000021
respectively representing the speeds of the ith front vehicle and the ith rear vehicle which can converge into the gap, and the unit is m/s; v. of s Represents the speed of the autonomous vehicle in m/s;
Figure FDA0003868729590000022
respectively representing the expected acceleration of the ith vehicle capable of converging into the gap and the rear vehicle with the unit of m/s 2 ；
Figure FDA0003868729590000023
δ 1 ,δ 2 ,δ 3 Weight factors, δ, representing the clearance safety cost, rear vehicle safety cost and front vehicle safety cost, respectively 1 ,δ 2 ,δ 3 Respectively taking 0.5, 0.25 and 0.25; alpha, beta 1 ,β 2 The constant coefficients alpha, beta are obtained through experiments in the clearance safety cost, the rear vehicle safety cost and the front vehicle safety cost 1 ,β 2 Respectively taking 1, 1 and 1.
5. The method of claim 4 for optimizing automatic vehicle influx gap selection based on artificial intelligence, wherein the method comprises the following steps: in step 4, the calculation method of the automatic vehicle convergence efficiency cost comprises the following steps:
Figure FDA0003868729590000024
in the formula (I), the compound is shown in the specification,
Figure FDA0003868729590000025
representing the cost of efficiency of importing the ith importable gap;
Figure FDA0003868729590000026
x i representing the longitudinal relative distance of the autonomous vehicle from the ith convergence gap midpoint, x, when the gap is forward of the direction of travel of the autonomous vehicle i Taking a positive value, and taking a negative value otherwise; lambda [ alpha ] 1 ,λ 2 Weight factors respectively representing the efficiency cost of the relative speed of the front vehicle and the efficiency cost of the relative position of the clearance, and xi is the efficiency cost of the relative position of the clearance obtained through experimentsConstant coefficient of (d); lambda [ alpha ] 1 ,λ 1 Xi, xi are 0.6, 0.4, 10, respectively.
6. The automated vehicle influx gap selection optimization method based on artificial intelligence of claim 5, wherein: in step 5, the method for calculating the minimum total cost of the automatically-driven vehicle is as follows:
L＝min(L 1 ,L 2 ,...,L i )；
in the formula (I), the compound is shown in the specification,
Figure FDA0003868729590000027
L i represents the total cost, ω, of sinking into the ith sinking gap a ,ω x Weighting factors representing the cost of the import security and the cost of the import efficiency respectively; omega a ,ω x Respectively taking 0.7 and 0.3.
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