CN108733063A - A kind of autonomous collaboration traveling decision-making technique of automatic driving vehicle - Google Patents
A kind of autonomous collaboration traveling decision-making technique of automatic driving vehicle Download PDFInfo
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- G—PHYSICS
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
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- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0223—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory involving speed control of the vehicle
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
- G05D1/0212—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory
- G05D1/0214—Control of position or course in two dimensions specially adapted to land vehicles with means for defining a desired trajectory in accordance with safety or protection criteria, e.g. avoiding hazardous areas
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- G05D—SYSTEMS FOR CONTROLLING OR REGULATING NON-ELECTRIC VARIABLES
- G05D1/00—Control of position, course, altitude or attitude of land, water, air or space vehicles, e.g. using automatic pilots
- G05D1/02—Control of position or course in two dimensions
- G05D1/021—Control of position or course in two dimensions specially adapted to land vehicles
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Abstract
The present invention proposes the autonomous collaboration traveling decision-making technique of automatic driving vehicle, specially：Vehicle acquires running condition information by vehicle-mounted detecting system；Vehicle application ambiguity function realizes the Fuzzy processing of running condition information parameter, estimates the transport condition of vehicle and its neighbours' vehicle；By the running condition information parameter after blurring, as indistinct logic computer input parameter, indistinct logic computer is according to the fuzzy rule base reasoning car travel mode of setting；The optimal driving mode result of decision of output separate vehicle is handled by ambiguity solution, realizes the autonomous collaboration traveling between cluster vehicle.Advantageous effect：Infomation detection, calculating and the communication capacity having by automatic driving vehicle, pass through blurring to vehicle itself transport condition parameter set and fuzzy logic inference, realize the autonomous collaboration traveling between cluster vehicle, best driving mode is selected to provide reference for separate vehicle, improve vehicle driving safety, reduce the vehicle journeys time, reduces traffic energy consumption.
Description
Technical Field
The invention relates to a vehicle networking technology, in particular to an autonomous cooperative driving technology among automatic driving vehicles.
Background
The Internet of Vehicles (IoV) is a convergence of wireless communication technology and transportation network, is a typical application of the Internet of things in the transportation field, and is a demand for the evolution towards automatic driving. The automatic driving technology is an application of traffic intellectualization and networking with high mobility, complex road condition information and high safety requirement, is a development trend of road traffic, and is a necessary process for realizing unmanned driving. The autonomous cooperative driving of the vehicle is essentially a dynamic adjustment process of a driving track according to a road passing state, a driving state of the vehicle and a driving state of a neighboring vehicle. In the internet of vehicles, vehicles travel cooperatively in a cluster mode as a whole, wherein vehicles in a slow lane generally travel in a predetermined track mode, vehicles in a traffic lane generally travel in a following mode, vehicles in a overtaking lane generally travel in random modes such as speeding and lane changing, and the following mode is realized in a overtaking return lane. The automatic driving vehicle serving as the Internet of vehicles node is provided with a complete vehicle-mounted sensing detection, calculation processing and communication propagation system, can acquire, transmit and process vehicle driving state information data in real time, realizes autonomous cooperative driving among clustered vehicles, and improves vehicle driving safety and road traffic efficiency.
At present, the automatic driving vehicle independently collects and processes the traffic environment state information in real time by means of an additional sensing detection and calculation processing system, the driving safety of the vehicle is guaranteed, but the mutual cooperation function is lacked among the vehicles, the road passing time of the independent vehicle is increased, and the overall passing efficiency of a traffic transport network is reduced. Therefore, the optimal mode of the vehicle running state is calculated in real time by utilizing the strong vehicle-mounted detection system, the information processing capacity and the communication capacity of the automatic driving vehicle, the autonomous cooperative running among the vehicles in the cluster is realized, the large-scale reliable commercial use of the automatic driving vehicle is promoted, and the running data is provided for the future unmanned driving.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provides an autonomous cooperative driving decision method among automatic driving vehicles, which utilizes the real-time traffic state information acquisition capacity, strong information calculation processing capacity and high-reliability low-delay communication capacity of the automatic driving vehicles, calculates the membership degree of vehicle driving state information parameters by using a fuzzy function, and infers the optimal driving mode of the vehicles by fuzzy logic to realize the autonomous cooperative driving and road traffic optimization among clustered vehicles in an internet of vehicles, and is specifically realized by the following technical scheme:
the autonomous cooperative driving decision method for the automatic driving vehicle is characterized in that a positioning system, a driving state information detection system and a vehicle-mounted wireless communication system supporting workshop communication are arranged on the vehicle, and driving state information of neighboring vehicles is periodically exchanged through workshop communication technology, and the method specifically comprises the following steps:
the vehicle acquires driving state information through a vehicle-mounted detection system;
the vehicle uses a fuzzy function to realize fuzzification processing of the driving state information parameters, and estimates the driving states of the vehicle and the neighboring vehicles;
the running state information parameters after fuzzification are used as input parameters of a fuzzy inference machine, and the fuzzy inference machine infers a vehicle running mode according to a set fuzzy rule base;
and outputting an optimal running mode decision result of the independent vehicles through the fuzzy solution processing, and realizing the autonomous cooperative running among the clustered vehicles.
The autonomous cooperative driving decision method of the automatic driving vehicle is further designed in such a way that a vehicle driving state information parameter set is set to be theta ═ thetaV,θN,θLIn which θVAs the vehicle running speed, thetaNNumber of neighbor nodes of vehicle, thetaLIs the current driving lane of the vehicle; according to thetaV、θNAnd thetaLCorresponding fuzzy membership degrees mu (V), mu (N) and mu (L) are calculated respectively.
The autonomous cooperative driving decision method of the automatic driving vehicle is further designed in such a way that a vehicle driving speed parameter theta is setV＝{VL,VM,VH}，VL,VM,VHThree running speed states of low speed, medium speed and high speed are correspondingly represented. Setting an upper limit of a vehicle running speed to VUThe lower limit of the running speed of the vehicle is VLBAverage running speed of VAThen the vehicle running speed parameter thetaVThe fuzzy membership degree mu (V) of three running speed states of high speed, medium speed and low speed is { mu (V) }H,μM,μLCalculating according to formula (1), formula (2) and formula (3);
wherein,
the autonomous cooperative driving decision method of the automatic driving vehicle is further designed in that a parameter theta of the number of the nodes adjacent to the vehicle is setN＝{NS,NM,ND}，NS,NM,NDRespectively representing the distribution states of three neighbor nodes of sparse, normal and dense, and setting the average number of neighbor nodes to be N during normal trafficNUpper limit of average neighbor node number NU＝2NNLower limit of average neighbor node numberThen the three kinds of vehicle neighbor nodes of sparse, normal and dense are distributed with the state parameter thetaNIs fuzzy degree of membership mu (N) { mu }S,μN,μDIs calculated according to equation (4), equation (5), and equation (6):
the autonomous cooperative driving decision method of the automatic driving vehicle is further designed in such a way that the standardized single lane width is set to be 3.75 meters, the distribution states of the three-lane road are a passing lane, a driving lane and a slow lane from left to right in sequence, and the vehicle driving lane parameter theta isL＝{LO,LC,LS}，LO,LC,LSRespectively and correspondingly representing three occupied lane states of a passing lane, a traffic lane and a slow lane, and then three vehicle driving lane state parameters theta of the passing lane, the traffic lane and the slow laneLIs fuzzy degree of membership mu (L) { mu }O,μC,μSIs calculated according to equation (7), equation (8), and equation (9):
the autonomous cooperative driving decision method of the automatic driving vehicle is further designed in such a way that the driving state set of the vehicle is set as M ═ MR,MF,MP}，MR、MFAnd MPThe random mode running state, the follow-up mode running state, and the predetermined track mode running state are respectively expressed correspondingly.
The autonomous cooperative driving decision method of the automatic driving vehicle is further designed in such a way that the set fuzzy rule base is shown as a table 1,
TABLE 1
The invention has the following advantages:
the autonomous cooperative driving decision method among the automatic driving vehicles realizes autonomous cooperative driving among clustered vehicles through information detection, calculation and communication capabilities of the automatic driving vehicles and fuzzification and fuzzy logic reasoning of the driving state parameter set of the vehicles, provides reference for selecting an optimal driving mode for independent vehicles, improves the driving safety of the vehicles, reduces the vehicle travel time and reduces the traffic energy consumption.
Drawings
Fig. 1 is a cluster vehicle travel model.
FIG. 2 is a membership function for vehicle speed.
FIG. 3 is a distribution membership function of vehicle neighbor nodes.
FIG. 4 is a membership function for a vehicle lane.
Fig. 5 is a vehicle driving pattern fuzzy inference system.
Detailed Description
The technical solution of the present invention is further explained with reference to the specific embodiments and the accompanying drawings.
As shown in fig. 1, the autonomous cooperative driving decision method for an autonomous driving vehicle provided in this embodiment is configured with a positioning system, a driving state information detection system, and a vehicle-mounted wireless communication system supporting vehicle-to-vehicle communication, where neighboring vehicles periodically exchange driving state information with each other through a vehicle-to-vehicle communication technology, and the specific content includes: the vehicle acquires driving state information through a vehicle-mounted detection system; when the vehicle plans to change the traveling mode, the self traveling state information is issued in an active broadcasting mode;
the vehicle uses a fuzzy function to realize fuzzification processing of the driving state information parameters, and estimates the driving states of the vehicle and the neighboring vehicles;
the running state information parameters after fuzzification are used as input parameters of a fuzzy inference machine, and the fuzzy inference machine infers a vehicle running mode according to a set fuzzy rule base;
and outputting an optimal running mode decision result of the independent vehicles through the fuzzy solution processing, and realizing the autonomous cooperative running among the clustered vehicles.
In the present embodiment, the vehicle driving state information parameter set is set to θ ═ θV,θN,θLIn which θVAs the vehicle running speed, thetaNNumber of neighbor nodes of vehicle, thetaLIs the current driving lane of the vehicle; according to thetaV、θNAnd thetaLCorresponding fuzzy membership degrees mu (V), mu (N) and mu (L) are calculated respectively.
Further, a vehicle running speed parameter theta is setV＝{VL,VM,VH}，VL,VM,VHRespectively representing three running speed states of low speed, medium speed and high speed, and setting the upper limit of the running speed of the vehicle to be VUThe lower limit of the running speed of the vehicle is VLBAverage running speed of VAThen the vehicle running speed parameter thetaVThe fuzzy membership degree mu (V) of three running speed states of high speed, medium speed and low speed is { mu (V) }H,μM,μLCalculating according to formula (1), formula (2) and formula (3);
wherein,
in this embodiment, assuming that the normalized road driving safety index is β 0.001H (H is H), on the premise of ensuring the driving safety of the vehicle,average number of neighbor nodes in normal traffic of three lanes(R is the radial length of the communication area of the vehicle-mounted communication system) and the upper limit of the average number of neighbor nodesAverage neighbor node number lower boundThen the three kinds of vehicle neighbor nodes of sparse, normal and dense are distributed with the state parameter thetaNIs fuzzy degree of membership mu (N) { mu }S,μN,μDCalculating as shown in formulas (4) to (6):
in the embodiment, the standardized single lane width is set to be 3.75 meters, the distribution states of the three-lane road are a passing lane, a traffic lane and a slow lane from left to right in sequence, and the vehicle driving lane parameter thetaL＝{LO,LC,LS}，LO,LC,LSRespectively and correspondingly representing three occupied lane states of a passing lane, a traffic lane and a slow lane, and then three vehicle driving lane state parameters theta of the passing lane, the traffic lane and the slow laneLIs fuzzy degree of membership mu (L) { mu }O,μC,μSIs calculated according to equation (7), equation (8), and equation (9):
the present embodiment sets the vehicle driving state set to M ═ MR,MF,MP}，MR、MFAnd MPThe random mode running state, the follow-up mode running state, and the predetermined track mode running state are respectively expressed correspondingly.
The fuzzy rule base of settings mentioned in this embodiment is shown in table 1,
TABLE 1
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 (7)
1. An autonomous cooperative driving decision method for automatically driven vehicles features that a locating system, a driving state information detecting system and a vehicle-carried radio communication system supporting communication between adjacent vehicles are used to periodically exchange the driving state information between adjacent vehicles by communication technique between vehicles, and features that the autonomous cooperative driving decision method for automatically driven vehicles is used to automatically detect the driving state information of adjacent vehicles
The vehicle acquires driving state information through a vehicle-mounted detection system;
the vehicle uses a fuzzy function to realize fuzzification processing of the driving state information parameters, and estimates the driving states of the vehicle and the neighboring vehicles;
the running state information parameters after fuzzification are used as input parameters of a fuzzy inference machine, and the fuzzy inference machine infers a vehicle running mode according to a set fuzzy rule base;
and outputting an optimal running mode decision result of the independent vehicles through the fuzzy solution processing, and realizing the autonomous cooperative running among the clustered vehicles.
2. The autonomous cooperative travel decision method of an autonomous vehicle according to claim 1, characterized in that the vehicle travel state information parameter set is set to θ ═ { θ ═ θV,θN,θLIn which θVAs the vehicle running speed, thetaNNumber of neighbor nodes of vehicle, thetaLIs the current driving lane of the vehicle; according to thetaV、θNAnd thetaLCorresponding fuzzy membership degrees mu (V), mu (N) and mu (L) are calculated respectively.
3. The autonomous cooperative driving decision method of an autonomous-driven vehicle according to claim 2, characterized in that a vehicle driving speed parameter θ is setV＝{VL,VM,VH}，VL,VM,VHRespectively correspondingly showing three running speed states of low speed, medium speed and high speed,
setting an upper limit of a vehicle running speed to VUThe lower limit of the running speed of the vehicle is VLBAverage running speed of VAThen the vehicle running speed parameter thetaVThe fuzzy membership degree mu (V) of three running speed states of high speed, medium speed and low speed is { mu (V) }H,μM,μLCalculating according to formula (1), formula (2) and formula (3);
wherein,
4. the autonomous cooperative driving decision method of an autonomous-driven vehicle according to claim 3, characterized in that a vehicle neighbor node number parameter θ is setN＝{NS,NM,ND}，NS,NM,NDRespectively representing the distribution states of three neighbor nodes of sparse, normal and dense, and setting the average number of neighbor nodes to be N during normal trafficNUpper limit of average neighbor node number NU＝2NNLower limit of average neighbor node numberThen the three kinds of vehicle neighbor nodes of sparse, normal and dense are distributed with the state parameter thetaNIs fuzzy degree of membership mu (N) { mu }S,μN,μDIs calculated according to equation (4), equation (5), and equation (6):
where N represents the number of neighbor nodes.
5. The autonomous cooperative driving decision method of an autonomous vehicle according to claim 4, characterized in that a standardized lane width of 3 is set.75 m, the distribution states of the three-lane road are a passing lane, a traffic lane and a slow lane from left to right in sequence, and the vehicle driving lane parameter thetaL＝{LO,LC,LS}，LO,LC,LSRespectively and correspondingly representing three occupied lane states of a passing lane, a traffic lane and a slow lane, and then three vehicle driving lane state parameters theta of the passing lane, the traffic lane and the slow laneLIs fuzzy degree of membership mu (L) { mu }O,μC,μSIs calculated according to equation (7), equation (8), and equation (9):
where L represents the currently occupied lane.
6. The autonomous cooperative driving decision method of an autonomous vehicle according to claim 5, characterized in that the vehicle driving state set is set to M ═ { M ═ MR,MF,MP}，MR、MFAnd MPThe random mode running state, the follow-up mode running state, and the predetermined track mode running state are respectively expressed correspondingly.
7. The autonomous cooperative driving decision method of an autonomous vehicle according to claim 6, characterized in that the fuzzy rule base is set as table 1.
TABLE 1
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Effective date of registration: 20231229 Address after: 518000 1104, Building A, Zhiyun Industrial Park, No. 13, Huaxing Road, Henglang Community, Longhua District, Shenzhen, Guangdong Province Patentee after: Shenzhen Hongyue Enterprise Management Consulting Co.,Ltd. Address before: 226000 Jiangsu city of Nantong province sik Road No. 9 Patentee before: NANTONG University Patentee before: NANTONG RESEARCH INSTITUTE FOR ADVANCED COMMUNICATION TECHNOLOGIES Co.,Ltd. |