CN112750334B - Centralized control method for venue automatic driving vehicles based on Petri network - Google Patents

Centralized control method for venue automatic driving vehicles based on Petri network Download PDF

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CN112750334B
CN112750334B CN202011543270.9A CN202011543270A CN112750334B CN 112750334 B CN112750334 B CN 112750334B CN 202011543270 A CN202011543270 A CN 202011543270A CN 112750334 B CN112750334 B CN 112750334B
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陈纯玉
吴忻生
陈安
王博
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South China University of Technology SCUT
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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
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/029—Location-based management or tracking services
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/30—Services specially adapted for particular environments, situations or purposes
    • H04W4/40—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P]
    • H04W4/44—Services specially adapted for particular environments, situations or purposes for vehicles, e.g. vehicle-to-pedestrians [V2P] for communication between vehicles and infrastructures, e.g. vehicle-to-cloud [V2C] or vehicle-to-home [V2H]

Abstract

The invention discloses a centralized control method for automatically driving vehicles in a venue based on a Petri network, which comprises the following steps: s1, determining the actual size of the venue, the number of seats, the construction condition of supporting facilities and the traffic condition of surrounding roads; s2, calculating the parking capacity of a PRT station by using road traffic capacity (HCM) according to the actual pedestrian flow of the venue, simulating the arrival time of the pedestrian flow by using Poisson distribution, determining the required maximum throughput, and designing a station pattern; s3, establishing a signalless intersection control model based on the timed Petri network, and dividing the modeling aiming at the station into two parts: modeling parking processes of all lanes and modeling distribution control of public resources; and S4, performing vehicle centralized control by using the central control station control mode. The invention belongs to the field of automatic driving control, and achieves the maximum utilization of resources on the basis of ensuring the service quality by reasonably designing the number of PRT sites through an optimization algorithm.

Description

Centralized control method for venue automatic driving vehicles based on Petri network
Technical Field
The invention relates to the field of automatic driving control, in particular to a centralized control method for automatically driving vehicles in a venue based on a Petri network.
Background
With the development of the unmanned vehicle technology, the unmanned vehicle is increasingly applied to various occasions due to the characteristics of safety, stability, convenience and the like. The large-scale national and international events have the characteristics of large instantaneous people flow, frequent gathering and scattering, relatively centralized people, things and properties and the like. However, if the streams of people cannot be scientifically and effectively guided and managed, the successful holding of the events is inevitably hindered, and even the vicious events damaging the life safety and property safety of the audiences and the participants can be generated.
A Personal Rapid Transit (PRT) is a Personal Rapid public transport system, uses small vehicles (each vehicle takes about 1-8 people) with a self-navigation function, and has the characteristics of comfort and convenience of cars, low energy consumption of buses and high transport efficiency. Each passenger may board a particular ride and travel to any destination. The concept of PRT traffic systems originated in 1900, but modern PRT systems were not studied until 1953. In 1964, Fichter et al published articles to introduce and discuss the application prospect of PRT technology, and have great propulsion effect on the development of PRT system. In the last 70 to 80 centuries, PRT systems have been studied in many countries, but for economic, technical and other reasons there has been no breakthrough.
The first PRT system in the world was put into operation in the international airport cisro, england early in the 21 st century, and this PRT system was based primarily on ultra (urban Light transport) technology and is also under constant extension. With the successful operation of the first PRT system, other countries are increasingly constructed, and in 4 months 2014, a set of SkyCube personal agile system with 40 vehicles, two stations and 4.46 km tracks in the city of the same day in korea is put into use.
The setting of the PRT station has great influence on the overall performance of the PRT system, if the scale is too small, the passengers get off slowly, traffic jam is easily caused, audiences and competitors are not facilitated to enter the field in time, and even competition delay can be caused; if the scale is too large, a large amount of construction and maintenance cost is needed, and resource waste is easily caused.
Aiming at the problem that a PRT station is not mature in design and control at the present stage, the invention provides a PRT station and a method for controlling an unmanned vehicle, and the problem is effectively solved.
Disclosure of Invention
The invention aims to provide a centralized control method for automatically driving vehicles in a venue based on a Petri network. The reasonable design and vehicle centralized control of the PRT station can be realized.
The object of the invention is achieved by at least one of the following solutions.
A centralized control method for automatically driving vehicles in a venue based on a Petri net comprises the following steps:
s1, determining the actual size of the venue, the number of seats, the construction condition of supporting facilities and the traffic condition of surrounding roads;
s2, calculating the parking capacity of a PRT station by using road traffic capacity (HCM) according to the actual pedestrian flow of the venue, simulating the arrival time of the pedestrian flow by using Poisson distribution, determining the required maximum throughput, and designing a station pattern;
s3, establishing a signalless intersection control model based on the timed Petri network, and dividing the modeling aiming at the station into two parts: modeling parking processes of all lanes and modeling distribution control of public resources;
and S4, performing vehicle centralized control by using the central control station control mode.
Preferably, in step S2, the throughput capacity formula of a single parking space is as follows according to the number of parking spaces at the PRT station, the parking time of the unmanned vehicle, and the volatility factor:
Figure BDA0002849784180000021
wherein, C1The number of the single parking spaces for parking per hour is in the unit of bus/h; g/c is the split green ratio of the crossroad near the station; t is tqkThe time is the emptying time, namely the time difference from the leaving of the previous unmanned automobile to the entering of the next automobile into the passenger area, and the unit is second; t is ttkThe average parking time of the unmanned automobile in the passenger area is second; t is tlsThe time of the unmanned vehicle entering and exiting the PRT station is second; t is tylThe operating margin is a period of time, in seconds, that is increased to avoid frequent generation of queues.
Preferably, the operation margin is calculated as follows:
tyl＝Zαcvttk (2)
where α is the probability that the next zone is occupied and must be queued (Failure rate), cvIs the coefficient of variation of parking time (Dwell time variance) due to the visitorThe number and the style of the vehicle are different, the parking time of all the unmanned vehicles is not the same, and the coefficient of variation c of the parking timevIs the ratio of the standard deviation of the individual vehicle stopping times to its mean, ZαIs a standard normal distribution value corresponding to the probability alpha of queuing:
Figure BDA0002849784180000022
tyl＝cvtdZα (4)
where s represents the standard deviation of the vehicle stop time distribution.
Preferably, the poisson distribution formula is as follows:
Figure BDA0002849784180000031
wherein, λ is the sample mean value of the interval, k represents the human flow rate, and the probability of the human flow rate per minute is obtained through the formula.
Preferably, the design station model in step S2 is mainly straight line type and bay type.
Preferably, the step S3 of establishing the signalless intersection control model based on the timed Petri net uses PIPE 4.3 software to perform modeling, and the modeling for the PRT station is divided into two parts: modeling parking processes of all lanes and modeling distribution control of public resources;
according to the number of lanes, dividing the physical space of the intersection into right-of-way resources, according to the intersection conflict points, vehicles in different directions may occupy the same right-of-way resources when passing through the intersection, and in order to ensure the safety of the vehicles, the capacity of the right-of-way resources needs to be limited, that is, the maximum number of vehicles capable of using the right-of-way resources at the same time, and the capacity of each right-of-way resource is usually 1; using road right resources represented by the library, wherein the number of tokens in the library represents the capacity of each public resource, and sending an implementation instruction and a delay instruction by using real-time transition and delay transition; when a vehicle passes through an intersection, other vehicles cannot pass through the intersection at the same time due to the limitation of the number of tokens.
Preferably, the signalless intersection control model in step S3 adopts a centralized control mode, specifically: each vehicle coming off is represented as an independent controlled object, namely each vehicle represents a token, the vehicle enters a PRT station from a limited bank, the central control station allocates lanes for getting off, and the central control station allocates a lane for using, namely triggering transition; the capacity of the garage is 1, namely, only one vehicle can pass through the intersection at the same time, when the waiting position and the passenger area do not occupy the vehicle, namely, no token exists in each garage, the transition is activated in sequence, the vehicle enters the garage without the token, the token exists in the garage at the moment, the vehicle passes through the intersection at the moment, other vehicles cannot trigger the transition to enter the intersection until the vehicle at the intersection passes through the intersection, the token in the garage at the intersection is 0, and other waiting vehicles cannot enter the intersection at the moment.
Preferably, in step S4, the central control station control mode performs vehicle centralized control in a vehicle-road cooperative environment, when a vehicle enters an entrance area of a PRT station, according to uwb (ultra wide band) sensing and positioning and satellite positioning, acquires motion state information of all vehicles within a station Range, directly performs vehicle-road communication of information interaction with an On Board Unit (OBU) in the vehicle through dsrc (dedicated Short Range communications), sends the vehicle-road communication to an intelligent roadside device, waits for road right allocation, and the intelligent roadside device feeds back decision control information of the vehicle to the intelligent vehicle-mounted device according to a Petri network automatic driving control instruction, so as to guide an intelligent driving vehicle to safely and effectively pass through the station, and minimize station delay time.
Preferably, the intelligent roadside device performs Petri model calculation in the centralized controller and sends control information to the vehicle-mounted controller.
Preferably, the intelligent roadside device is an integrated controller STM 32.
Compared with the prior art, the invention has the following beneficial effects:
(1) aiming at the construction problem of the PRT station in a large venue, a scheme capable of effectively determining the scale of the station is provided, and the full utilization of resources and the guarantee of traffic order can be realized at the same time.
(2) The intelligent driving system can effectively and centrally control the intelligent driving vehicles, ensure that passengers can get on and off in order when the vehicles are more, and avoid the behaviors of crowding, collision and the like, and has practical significance in traffic application scenes.
Drawings
Fig. 1 is a schematic overall flow chart of a centralized control method for automatically driving vehicles in a venue based on a Petri net according to this embodiment;
FIG. 2 is a diagram of the structure of an Bay PRT station in this embodiment;
FIG. 3 is a Poisson distribution diagram of the present embodiment;
fig. 4 is a Petri net model of a station in the embodiment.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The embodiment of the invention provides a Petri network-based centralized control method for automatically driving vehicles in a venue, an overall flow chart is shown in figure 1, and the method comprises the following specific steps:
step 1, collecting the actual size of a venue, the number of seats, the construction condition of supporting facilities, the traffic condition of surrounding roads, the actual size of the venue and other factors can influence the design scale and the whole structure of a PRT station, if the capacity of the Guangzhou gym is about 10000 people, the surrounding roads have good conditions and wide roads, and a plurality of vehicles can be simultaneously contained and run in parallel.
Step 2, calculating the station parking capacity by using the HCM, firstly considering the number of parking spaces of the PRT station, the parking time of the unmanned vehicle, the volatility and other factors, and calculating the throughput capacity of a single parking space according to the following formula:
Figure BDA0002849784180000041
wherein, C1The number of the single parking spaces for parking per hour is in the unit of bus/h; g/c is the split green ratio of the crossroad near the station; t is tqkThe time is the emptying time, namely the time difference from the leaving of the previous unmanned automobile to the entering of the next automobile into the passenger area, and the unit is second; t is ttkThe average parking time of the unmanned automobile in the passenger area is second; t is tlsThe time of the unmanned vehicle entering and exiting the PRT station is second; t is tylFor the operation margin, which is a period of time increased to avoid frequent generation of queues, the unit is seconds, and the calculation formula is as follows:
tyl＝Zαcvttk (2)
where α is the probability that the next zone is occupied and must be queued (Failure rate). c. CvFor the coefficient of variation of parking time (Dwell time variability), the parking time of all unmanned vehicles is not the same due to the different numbers of passengers and the different types of vehicles, but the coefficient of variation of parking time cvIs the ratio of the standard deviation of the individual vehicle stopping times to its mean value, according to experience, cvTypically between 0.4 and 0.8, with a recommended value of 0.6. ZαIs a standard normal distribution value corresponding to the probability alpha of queuing:
Figure BDA0002849784180000051
tyl＝cvtdZα (4)
where s represents the standard deviation of the vehicle stop time distribution.
The probability alpha of queuing and the corresponding standard normal distribution value ZαIn places with dense people streams such as the sub-fortuneIn the district, the recommended queuing probability is 20% -30% when estimating the throughput capacity of the PRT station, as shown in the following table:
TABLE 1 probability of queuing alpha and corresponding standard normal distribution value Zα
Probability of queuing alpha Zα Probability of queuing alpha Zα
1.0％ 2.330 15.0％ 1.040
2.5％ 1.960 20.0％ 0.840
5.0％ 1.645 25.0％ 0.675
7.5％ 1.440 30.0％ 0.525
10.0％ 1.280 50.0％ 0.000
The unmanned vehicle of the PRT system adopts a centralized control mode, and in the vehicle-road cooperative system, when a vehicle enters a control area of a PRT station, a signal lamp is not needed, so that the green-to-traffic ratio g/c of a crossroad near the station can be approximate to 1.
Clearing time t of general vehicleqk3 seconds, average docking time ttkThe number of passengers is positively correlated with the actual number of passengers getting on and off, when the number of passengers is 6-10, the time of getting off by a single person is about 1.2 seconds, the bearing capacity of each vehicle is set to be 8, and then t tk10 seconds. When the queuing probability is 25%, the operation margin t yl4 seconds.
Generally speaking, the flow of people to the venue is subject to a poisson distribution, the carrying capacity of each vehicle is 8 people, about 1250 vehicles arrive continuously in 3 hours in total, and the flow of the vehicles is also subject to the poisson distribution. Every 1 minute was used as an interval, and 180 intervals were used. The poisson distribution equation is as follows:
Figure BDA0002849784180000061
in the formula, λ is the sample mean value of the interval, k represents the human flow rate, and the probability of the human flow rate per minute is obtained through the formula.
The relationship between the number of vehicles per minute and the cumulative probability is shown in fig. 3, and it can be seen from fig. 3 that when the throughput of the station reaches 10 vehicles per 1 minute, the retention and accumulation of the vehicles are not generated in 90% of the time.
The designed station style is mainly selected from a linear type and a bay type, wherein the linear type station is arranged at the edge of a lane, has no occupation of the lane, no obvious entrance and exit, small area and cheap and simple construction, but occupies a motor lane, and easily causes traffic jam and delay when the traffic flow is large; the harbor type station is arranged at the outer side of a road vehicle lane, a mode of locally widening the road surface is adopted, the occupied area is large, the construction is expensive, but the harbor type station has the advantages that the traffic capacity service level of the road is not influenced, and the influence on the traffic flow when the vehicle stops is reduced. The specific selection condition needs to be determined by combining the actual venue and the road traffic condition.
Since the PRT system mainly uses the unmanned vehicle in this example, in order to facilitate the entry and exit and stop of vehicles and reduce the risk of traffic congestion, a bay-type PRT station is used, the station mode is as shown in fig. 2, the station is arranged outside a road lane of a road vehicle, and a mode of locally widening the road surface is adopted.
Step 3, building a Petri network model, building a signalless intersection control model based on the timed Petri network, modeling by using PIPE 4.3 software, and dividing the modeling aiming at the PRT station into two parts: modeling the parking process of each lane and modeling the allocation control of the common resource.
According to the number of lanes, the physical space of the intersection is divided into road right resources, and according to the intersection conflict point theory, vehicles in different directions may occupy the same road right resources when passing through the intersection. In order to ensure the safety of vehicles, the capacity of the road right resource, i.e. the maximum number of vehicles capable of using the road right resource at the same time, needs to be limited, and the capacity of each road right resource is usually 1. And using the road right resources represented by the library, wherein the number of tokens in the library represents the capacity of each public resource, and sending an implementation instruction and a delay instruction by using real-time transition and delay transition. When a vehicle passes through an intersection, other vehicles cannot pass through the intersection at the same time due to the limitation of the number of tokens. According to the driving method, the vehicles can safely and autonomously use the station.
Modeling As shown in FIG. 4, a finite repository P is generally represented by a circle, a finite transition T is represented by a box, a black rectangle represents a real-time transition, and a white rectangle represents a delayed transition. The directed arcs connect the transitions with the library to form a static structural relationship of the system, and then the black dots are used for representing the tokens to simulate the dynamic behavior of the system. Each of the vehicles coming down can representFor an independent controlled object, i.e. each token represents a vehicle, driven from P0When a vehicle enters the PRT station and is assigned a lane by the central control station for getting off, the token transferring process simulates the process of using the PRT station by the vehicle, such as when a vehicle enters the P0The central control station allocating its use of the first lane, i.e. triggering the transition T2When the waiting space and the passenger area are not occupied by vehicles, T2、T3、T4Are activated in sequence. When the central control station STM32, namely the centralized controller STM32 (a single chip microcomputer), sends out T2When activating the command, the depot P9Token transfer to P3And the vehicle passes through the first waiting parking space. Following transition T3、T4Are activated in turn when the token is in place in the depository P7When the vehicle is getting off, due to depot P21The number of tokens in the system is 0, other vehicles cannot enter a passenger area according to a transition triggering condition, and the transition T is delayed at the moment7Triggered, starts timing, and transfers the token to P after timing8And ending the behavior of getting off the guest. When a vehicle passes through an intersection, other vehicles cannot pass through the intersection at the same time due to the limitation of the number of tokens. According to the driving method, the vehicles can safely and autonomously use the PRT station by using the centralized control mode.
Step 4, sending a control instruction, carrying out vehicle centralized control in a central control station control mode under a vehicle-road cooperative environment, collecting motion state information of all vehicles in a station Range when the vehicles enter an entrance area of a PRT station according to a UWB (ultra Wide band) sensing technology and a satellite positioning technology, directly sending a vehicle-road communication technology for information interaction with an On Board Unit (OBU) in the vehicle to intelligent road side equipment through a DSRC (dedicated Short Range communications) technology, waiting for road right distribution, and feeding decision control information of the vehicles back to the intelligent vehicle-mounted equipment according to a Petri network automatic driving control instruction by the intelligent road side equipment to guide the intelligent driving vehicles to safely and effectively pass through the station so as to minimize station delay time. The intelligent road side equipment is a central control station STM 32.
The embodiment provides a centralized control method for automatically driving vehicles in a venue based on a Petri network, which comprises the steps of firstly collecting the actual size of the venue, the number of seats, the construction condition of supporting facilities and the traffic condition of surrounding roads, then calculating the stopping capacity of a station and designing the scale and the style of the station, then establishing a Petri network model, and finally sending a control instruction through a central control station STM 32. The technical scheme of the invention can effectively and intensively control the intelligent driving vehicles, ensure that passengers can get on and off in order even when the vehicles are more, and avoid the behaviors of crowding, collision and the like, thereby having practical significance in traffic application scenes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.

Claims (9)

1. A centralized control method for automatically driving vehicles in a venue based on a Petri net is characterized by comprising the following steps:
s1, determining the actual size of the venue, the number of seats, the construction condition of supporting facilities and the traffic condition of surrounding roads;
s2, calculating the parking capacity of the PRT station according to the actual pedestrian flow of the venue by using the road traffic capacity, simulating the arrival time of the pedestrian flow by using Poisson distribution, determining the required maximum throughput, and designing a station pattern;
s3, establishing a signalless intersection control model based on the timed Petri network, and dividing the modeling aiming at the station into two parts: the method specifically comprises the following steps of modeling the parking process of each lane and modeling the distribution control of public resources: according to the number of lanes, dividing the physical space of the intersection into right-of-way resources, according to the intersection conflict points, vehicles in different directions may occupy the same right-of-way resources when passing through the intersection, and in order to ensure the safety of the vehicles, the capacity of the right-of-way resources needs to be limited, that is, the maximum number of vehicles capable of using the right-of-way resources at the same time, and the capacity of each right-of-way resource is usually 1; using road right resources represented by the library, wherein the number of tokens in the library represents the capacity of each public resource, and sending an implementation instruction and a delay instruction by using real-time transition and delay transition; when the vehicle passes through the intersection, other vehicles cannot pass through the intersection at the same time through the limitation of the number of the tokens;
the signalless intersection control model adopts a centralized control mode, and specifically comprises the following steps: each vehicle coming off is represented as an independent controlled object, namely each vehicle represents a token, the vehicle enters a PRT station from a limited bank, the central control station allocates lanes for getting off, and the central control station allocates a lane for using, namely triggering transition; the capacity of the garage is 1, namely only one vehicle can pass through the intersection at the same time, when the waiting position and the passenger area do not occupy the vehicle at the moment, namely no token exists in each garage, the transition is activated in sequence, the vehicle enters the garage without the token, one token exists in the garage at the moment, the token represents that the vehicle passes through the intersection at the moment, other vehicles cannot trigger the transition to enter the intersection until the vehicle at the intersection passes through the intersection, the token in the garage representing the intersection is 0, and other waiting vehicles cannot enter the intersection at the moment;
and S4, performing vehicle centralized control by using the central control station control mode.
2. The Petri net-based centralized control method for the automatic driving vehicles in the stadium as claimed in claim 1, wherein the step S2 is specifically to determine the required maximum throughput according to the number of parking spaces of the PRT station, the parking time of the unmanned vehicles, volatility factors and the throughput capacity of a single parking space, and the throughput capacity formula of the single parking space is as follows:
Figure FDA0003287094860000011
wherein, C1The number of the single parking spaces for parking per hour is in the unit of bus/h; g/c is the split green ratio of the crossroad near the station; t is tqkThe time is the emptying time, namely the time difference from the leaving of the previous unmanned automobile to the entering of the next automobile into the passenger area, and the unit is second; t is ttkFor the average stopping time of the unmanned automobile in the passenger area,the unit is second; t is tlsThe time of the unmanned vehicle entering and exiting the PRT station is second; t is tylThe operating margin is a period of time, in seconds, that is increased to avoid frequent generation of queues.
3. The Petri Net based venue automatic driving vehicle centralized control method according to claim 2, wherein the operation margin is calculated as follows:
tyl＝Zαcvttk (2)
where α is the probability that the next zone is occupied and must be queued (Failure rate), cvFor the coefficient of variation of parking time (Dwell time variability), the parking time of all unmanned vehicles is not the same due to the different numbers of passengers and the different types of vehicles, but the coefficient of variation of parking time cvIs the ratio of the standard deviation of the individual vehicle stopping times to its mean, ZαIs a standard normal distribution value corresponding to the probability alpha of queuing:
Figure FDA0003287094860000021
tyl＝cvtdZα (4)
where s represents the standard deviation of the vehicle stop time distribution.
4. The Petri net-based centralized control method for the automatic driving vehicles in the stadium, as recited in claim 3, wherein the Poisson distribution formula is as follows:
Figure FDA0003287094860000022
wherein, λ is the sample mean value of the interval, k represents the human flow rate, and the probability of the human flow rate per minute is obtained through the formula.
5. The Petri net-based centralized control method for the automatic guided vehicles in the stadium as claimed in claim 4, wherein the design station model in the step S2 is mainly linear and bay type.
6. The centralized control method for automatically driving vehicles in venues based on Petri Net as claimed in claim 5, wherein the building signalless intersection control model based on the timed Petri Net in step S3 is modeled by using PIPE 4.3 software, and the modeling for PRT station is divided into two parts: modeling the parking process of each lane and modeling the allocation control of the common resource.
7. The centralized control method for automatically driving vehicles in stadium based On Petri Net as claimed in claim 6, wherein in step S4, the central control station control mode performs centralized control of vehicles in a cooperative environment of vehicle roads, when a vehicle enters an entrance area of a PRT station, according to UWB (ultra Wide band) sensing positioning and satellite positioning, collects motion state information of all vehicles in the station area, directly performs information interaction with an On Board Unit (OBU) in the vehicle through DSRC (differentiated Short Range communications), sends the vehicle road communication to an intelligent roadside device, waits for road right distribution, and the intelligent roadside device feeds back decision control information of the vehicle to the intelligent vehicle-mounted device according to an automatic driving control command of Petri Net, so as to guide the intelligent driving vehicle to safely and effectively pass through the station, thereby minimizing station delay time.
8. The Petri net-based centralized control method for the automatic driving vehicles in the stadium is characterized in that the intelligent road side equipment performs Petri model calculation in the centralized controller and sends control information to the vehicle-mounted controller.
9. The Petri net-based centralized control method for the automatic driving vehicles in the stadium is characterized in that the intelligent road side equipment is a centralized controller STM 32.
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